Minneapolis, 1949: a meeting that almost certainly never happened

Samuel Chester Gale set two boxes on the conference table.

Gale was General Mills’ vice president of advertising and one of the people credited with turning Betty Crocker into an American institution. In front of him were two very real products: Betty Crocker Party Cake Mix and Devil’s Food Cake Mix.

“Ladies and gentlemen,” he said, “I give you the future of cake.”

He poured the mix into a bowl.

“No measuring flour. No measuring sugar. No shortening. No wondering whether you used enough baking powder. Add water. Stir. Bake.”

He paused for effect.

“Cake. Almost instantly.”

There were approving nods around the table.

It was a very good answer.

Now imagine the next meeting.

The sales chart was not cooperating.

The sales executive spoke first.

“We need to emphasize speed. Put a clock in the advertisement. Cake in minutes!”

The operations person disagreed.

“Speed is only one part of it. Think through the steps. Gathering ingredients. Measuring. Mixing. Cleaning. We should market the cleanup. One bowl. One spoon. No flour all over the kitchen.”

The packaging executive tapped the box.

“The product is fine. The box is the problem. We need brighter colors. Maybe a birthday party.”

R&D had another theory.

“Flavors. That is the problem. Chocolate and spice are not enough. We need yellow cake. White cake. Maybe something with fruit.”

Finance cleared his throat.

“Or perhaps it costs too much.”

One by one, the team worked through every step surrounding the purchase and preparation of a cake.

Improve the advertising.

Add more flavors.

Reduce the price.

Get better shelf placement.

Make the instructions simpler.

Make cleanup faster.

Every suggestion was perfectly reasonable.

And every suggestion began with the same underlying assumption:

People want to make cake faster.

Finally, one of the home economists from the Betty Crocker test kitchens spoke.

“What if they don’t want faster?”

The room went quiet.

“What if they still want to feel like they are cooking, just being able to more reliably bake a great cake without having a lot of baking skill?”

That question sounds obvious now.

It was not obvious in a room full of people trying to improve an instant cake mix.

They had been asking:

How do we make cake making faster?

She was asking:

Which parts of making a cake feel like work—and which parts make it feel like baking?

That is a very different question.

The meeting and dialogue are fictional of course! Gale was real, and the Hennepin History Museum credits him as a driving force behind Betty Crocker. The products were real, too. General Mills’ history says home bakers testing its 1949 Party Cake and Devil’s Food mixes asked to add some of their own fresh ingredients, including eggs, even though the technology existed to have just-add-water cake cake mixes.

The answer in the room

The instant-cake idea was not foolish.

Baking from scratch involved a lot of steps. Some were tedious. Some introduced risk. Forget an ingredient, measure incorrectly or use the wrong pan and the result might be a small culinary tragedy in the middle of someone’s birthday.

A mix solved real problems.

But the team in our imaginary meeting had started with an answer:

Make cooking faster.

Once that answer was in place, every new idea became an attempt to improve it. Faster cleanup. Faster instructions. More varieties of fast cake.

The egg suggested something else.

A cake was not merely a delicious way to combine flour and sugar. It was often an expression of care. It was something you made for your family, your friends or someone celebrating a birthday.

If the box did everything, the result might be convenient—but it did not necessarily feel homemade.

Cracking an egg was technically an extra step.

Emotionally, it may have been the step that let someone say, “I baked this.”

The famous version of this story is a little too perfect. Requiring fresh eggs did not single-handedly create the cake-mix category. Frosting, decorating, new flavors, packaging, positioning and broader postwar changes all mattered.

But the messiness makes the lesson better, not worse.

The egg was not a magical answer.

It was evidence of a better question.

Finding product-market fit is hard

We all know product-market fit when we see it.

ChatGPT. Claude. Cursor. Ramp.

Once it happens, the product can feel inevitable. It’s customer led, not sales led. Word of mouth takes over. The question changes from “Will anyone buy this?” to “How fast can we keep up?”

The interesting part is what happens before that.

What do you do when customers think your product is interesting—but not essential? When pilots do not expand? When prospects compliment the demo and then return to whatever they were already doing?

One useful place to look is not only at your product.

Look at the questions you are asking.

Are you starting with a question?

Or are you starting with an answer?

Both are legitimate. The trouble begins when you confuse the two.

I started Polyverse with an answer

As many of my readers know, I started and ran a cybersecurity company called Polyverse for almost twelve years. We achieved a small level of success with high end sales to the military, but we never achieved a breakout success, and after twelve years the company ceased operations in 2024.

In hindsight, I started with an answer at Polyverse.

Companies care about cybersecurity.

Seems pretty indisputable, right? Every company said security was important. Cyberattacks were growing. The consequences were enormous.

So the rest appeared almost mechanical.

If companies care about cybersecurity, then better cybersecurity should be valuable. Build technology that makes attacks dramatically harder, and companies should buy it.

We did build exceptionally strong technology. It worked. Some of the most elite cyber units in government understood it, valued it and used it.

But broad commercial product-market fit remained elusive.

My answer contained a hidden assumption.

I had anthropomorphized my customer, the idea of a company.

“Companies care about cybersecurity” makes a company sound like a person: a single mind that recognizes danger, compares solutions and buys the best protection.

But a company is not a person.

It is a collection of people.

The CISO may care deeply about reducing risk. The application owner cares about uptime. The developer cares about whether a new security tool breaks the build. Procurement cares about vendor terms. Finance cares about the budget. An executive may care intensely about security right after an incident and much less six months later.

Everyone can agree that cybersecurity is important while no individual has enough urgency, authority and incentive to adopt your product.

If a company were one person, selling Polyverse might have been much easier.

A company cannot love your product.

A person can.

Who would be very disappointed?

That leads to a more useful first question:

Is there a specific group of people who love your product—and cannot imagine returning to life without it?

Not people who like the idea.

Not people who describe it as innovative.

Not people who agree to another meeting.

People who would fight to keep it.

Sean Ellis turned this into a wonderfully brutal product-market-fit test: ask active users, “How would you feel if you could no longer use this product?” His heuristic is that a strong signal appears when roughly 40 percent answer “very disappointed.” The question was later popularized through Superhuman’s approach to measuring product-market fit.

Forty percent is not a law of nature. Surveying the wrong users will produce a meaningless number. And in an enterprise product, buyers, users, administrators and executives may value entirely different things.

But the question is powerful because it measures loss rather than approval.

People are generous with approval.

Loss is harder to fake.

When a pilot ends, does anyone demand that it be restored? When the product is unavailable, does work stop? Does a champion train coworkers, push the product through security and procurement, and spend political capital to get the contract renewed?

That is what love looks like in B2B.

It does not require everyone at the company to adore you. Procurement does not need to send you a valentine.

But somewhere in the account, real people must care enough to create pull.

“Meh” is a powerful signal

If the response is “meh,” you have learned something.

If the response is “The other tools are good enough,” you have learned something even more important.

Praise during a demo is not pull.

A pilot that survives only because the founder keeps nudging everyone is not pull.

A feature wish list untethered to an urgent workflow is not pull.

Your next move may not be another feature.

It may be a better question.

Return to the imaginary General Mills meeting.

If you ask, “How can we make cake faster?” the answers are predictable:

Remove another ingredient.

Eliminate another utensil.

Shorten the baking time.

Improve cleanup.

Those may all be good ideas. But the answer has already been smuggled into the question.

What would you ask instead?

Perhaps:

Walk me through the last cake you made for someone you cared about. Which parts were annoying? Which parts worried you? Which parts made it feel like yours?

Or:

When you put the cake on the table, what did you want the people around it to believe?

Or, most simply:

Which steps do you wish would disappear—and which steps would you miss?

Those questions leave room for surprise.

They might reveal that measuring flour is a burden, while cracking an egg feels like participation. They might reveal that the baker wants convenience but still needs confidence, authorship and pride.

The same discipline applies to technology products:

1. Who is the person experiencing the problem?

2. What happened the last time they experienced it?

3. What do they use today, and why is it good enough?

4. What would break if your product disappeared tomorrow?

5. Who would fight to get it back?

Those questions do not ask customers to design the product for you.

They help you understand what the product must accomplish.

Answers are hypotheses

Starting with an answer is not a mistake.

Sometimes the answer comes first. You invent a technology. You discover a new capability. You see a future that does not exist yet and decide to build it.

That is conviction.

Just call it what it is and proceed accordingly.

If you begin with an answer, treat it as a hypothesis and figure out how to test it as fast as possible. Decide what customer behavior would prove it right. More importantly, decide what evidence would make you change your hypothesis.

If nobody loves the answer enough to miss it, resist the temptation to keep polishing it indefinitely.

Go back to the questions.

The imagined General Mills team could have spent years making instant cake even more instant. Instead, the market offered a clue: perhaps the goal was not to remove every step.

A cake has two recipes.

One produces the food.

The other produces the feeling that I made this for you.

Product-market fit often begins when you stop asking how to sell the answer you already have—and start asking which part of the customer’s life they cannot imagine losing.

Sometimes the breakthrough is not removing one more step.

It is discovering which step must remain.

But now the pointy hair boss has some real competition with AI. There is a new fad of sending around AI generated strategy mails and treating them with the same pointy hair boss conviction.

It usually begins with a piece of news: a competitor launched a product, made an acquisition, hired an executive, or raised a round. A few minutes later, out comes the analysis.

The headings are perfect. There is a “What Happened,” a “Why This Matters,” five strategic implications, a battle card, and a crisp one-line takeaway. The new development is simultaneously a competitive threat, validation of our strategy, and a reason to move faster.

Every sentence is plausible.

None of it proves anything.

This is a more dangerous form of AI slop than the awkward cold email. It does not look like garbage. It looks like McKinsey spent the weekend on it.

The problem is not that AI wrote the words. I use AI constantly, including to help research this post, not to mention coding! The problem is that a machine can turn a starting assumption into a finished-looking conclusion so quickly that nobody notices the missing step in between:

Did anyone actually think?

Why AI is so good at coding?

To understand the problem, consider why AI has become so astonishingly good at software.

Code has a fitness function.

Does it compile? Do the tests pass? Does it return the right answer? Does it run faster? Even when the first attempt is wrong, a model can generate another candidate, run it, observe the failure, and try again.

That does not make coding easy. Tests can be incomplete, specifications can be wrong, and software can pass every check while still being useless. But much of the coding work produces fast, objective feedback.

This is unusually friendly terrain for reinforcement learning. OpenAI describes modern reasoning models as improving with both more reinforcement learning and more time spent reasoning. Its more recent summary of AI in science is even more direct: training that rewards verifiable outcomes, such as a correct final answer or executable code, has made math and coding more reliable.

In plain English: when the model can check its work, it has a chance to learn what “better” means.

Now try to write the fitness function test for this:

Why did a business strategy fail?

There is no compiler. The evidence is incomplete. Causes interact. The people involved remember events differently. The outcome may take years. Most important, we only observe the path that happened—not the counterfactual.

Betamax may have had better picture quality. VHS had longer recording time and a broader licensing ecosystem. People still debate the role of the adult-video market. Which factor was decisive? There is no alternate 1980s in which Sony made a different licensing decision and we reran history.

The same ambiguity surrounds nearly every famous strategy story. Did the iPod win because of design, iTunes, the customer experience, marketing, or Apple’s ability to lock up the supply of tiny hard drives? Did Zune lose because Microsoft entered too late, because the product was not different enough, or because smartphones and streaming were about to erase the category?

All of those explanations can be true.

“True” is not the same as “causal.” And “causal” is not the same as “decisive.”

One question, three ways

I wanted a way to make this visible without asking the reader to trust another layer of analysis—or another AI’s judgment of the AI.

So I used one case: Microsoft Zune. I happened to be at Microsoft at the time, and I had a front row seat watching the Zune project. It’s a topic I know well!

To test, I opened fresh conversations with three frontier systems at their maximum reasoning settings: Claude Fable 5 Max, Grok 4.5 Heavy, and Chat GPT 5.6 Sol. I told them not to browse and asked for one sentence.

The event stayed the same. I only changed the context embedded in the question.

1. The product frame

Why did Microsoft’s Zune fail despite features the iPod lacked, including an FM radio and more colors? Answer in one sentence using only your existing knowledge; do not browse.

Claude

The Zune failed because it arrived five years late to a market Apple already dominated, and standalone features like FM radio couldn’t overcome the iPod’s entrenched iTunes ecosystem, cultural cachet, and massive accessory and retail network—advantages that mattered far more to buyers than spec-sheet extras, especially once the iPhone soon made dedicated MP3 players obsolete altogether.

Grok

Despite offering an FM radio and more color options that early iPods lacked, Microsoft’s Zune failed due to its late 2006 market entry against the iPod’s already-dominant design, marketing, cultural cool factor, and tightly integrated iTunes ecosystem.

OpenAI

Zune failed because its incremental hardware advantages, including an FM radio and color options, were not compelling enough to overcome Microsoft’s late entry, weaker brand appeal, and Apple’s superior iPod/iTunes experience.

2. The ecosystem frame

Why did Microsoft’s Zune fail after Apple had already built a five-year lead and an integrated iPod/iTunes ecosystem? Answer in one sentence using only your existing knowledge; do not browse.

Claude

The Zune failed because it arrived too late with too little differentiation—consumers were already locked into iTunes libraries and the iPod’s vast accessory and retail ecosystem, its signature Wi-Fi song-sharing feature was hobbled by restrictive DRM (three plays or three days), Microsoft’s marketing and design couldn’t match Apple’s cultural cachet, and just months after launch the iPhone signaled that dedicated MP3 players were a dying category, leaving Zune to fight for second place in a market that was about to disappear.

Grok

Microsoft’s Zune failed because Apple’s five-year head start had already entrenched a dominant, seamlessly integrated iPod/iTunes ecosystem with powerful network effects, high switching costs from users’ music libraries, superior design, and cultural cachet that Zune’s 2006 launch could not overcome.

OpenAI

Zune failed because Apple’s five-year lead had already created an iPod/iTunes ecosystem, installed base, and switching costs that Microsoft could not dislodge without a radically better product.

3. The market-shift frame

Why did Microsoft’s Zune fail as smartphones and streaming were beginning to replace dedicated music players? Answer in one sentence using only your existing knowledge; do not browse.

Claude

The Zune failed because it launched in 2006 as a late me-too competitor to an iPod that already dominated the market and had the entrenched iTunes ecosystem, offering too little differentiation to lure users away, and just as it began improving, the 2007 debut of the iPhone and the broader shift toward smartphones and streaming made the entire dedicated-music-player category it was fighting for obsolete.

Grok

Microsoft’s Zune failed because it entered the dedicated portable-music-player market too late against the already-dominant iPod, with a weaker ecosystem, design, and brand appeal, just as smartphones and streaming services began making standalone players obsolete.

OpenAI

Zune failed because it entered too late to build meaningful scale before smartphones and streaming collapsed the standalone music-player category.

Of course, this was one run per question, not a scientific benchmark. But it’s enough to highlight the issue!

In the first set, better specifications are not enough to overcome Apple’s design, brand, and experience. In the second, a five-year lead becomes lock-in, switching costs, and an ecosystem Microsoft cannot dislodge. In the third, Zune simply arrives too late to outrun the collapse of the category.

The answers are not mutually exclusive. That is precisely the problem. Ambiguous strategy questions usually have several plausible causes, and the model can turn whichever one the question foregrounds into the spine of a coherent answer.

Same event. Same models. Different question. Different strategy.

The question selected the story.

The model is completing the task you gave it

The experiment is not a gotcha. None of the systems malfunctioned. Each produced a sensible answer to the question it was asked.

That is the point.

Language models are trained to be useful and responsive. If you ask for a battle card, they will find the reasons you win. If you ask why a competitor’s move validates your strategy, they will connect the dots. If you ask for the implications of a threat, they will produce threats and implications.

In a verifiable domain, a wrong answer can collide with reality. The code fails. The equation does not balance. The proof checker rejects the step.

In strategy, a coherent story can masquerade as a correct answer because there is no immediate collision. The headings line up. The facts sound familiar. The conclusion fits the question.

I think of this as narrative completion.

The model is not necessarily discovering the cause. It is completing the causal story implied by the task.

And most real corporate prompts are far more loaded than my experiment. They include management’s current strategy, selected customer quotes, internal talking points, claimed moats, and the desired output: “position us against this competitor,” “explain why this validates our direction,” or “turn this into a board update.”

By the time the model begins, the conclusion may already be in the prompt.

The new GIGO

The original meaning of “garbage in, garbage out” was straightforward: bad data or bad instructions produce bad results.

Frontier models complicate that rule because they can rescue a weak prompt surprisingly well. They can challenge false premises, identify missing facts, and produce genuinely novel analysis.

The models are getting better.

That makes the human problem more important, not less.

The new garbage is rarely a nonsensical input. It is an unexamined premise. A selective fact set. A requested conclusion. A missing counterfactual. A question that quietly defines what kind of answer will count as helpful.

And the new output does not look like garbage. It looks like an executive memo.

Perhaps the updated rule is not garbage in, garbage out, but rather:

Conclusion in, justification out.

Use AI to challenge a strategy, not certify it

I do not want less AI in strategy work. I want it used differently.

Before asking a model for analysis, the human should write down five things:

1. The decision. What are we actually deciding?

2. The competing hypotheses. What are the genuinely different explanations—not just a list of factors?

3. The evidence. Which statements are observed facts, which are internal claims, and which are inferences?

4. The disconfirmation test. What would have to be true for our preferred story to be wrong?

5. The unknowns. What information would most change the decision?

Then AI becomes enormously useful. Ask it to attack the favored hypothesis. Ask for the strongest rival explanation. Ask which facts would distinguish the two. Ask it to find hidden assumptions, construct counterfactuals, and design the fastest test.

That is very different from asking it to turn your first thought into a battle card.

AI is a wonderful sparring partner. It is a dangerous source of borrowed conviction.

The danger is not that AI cannot reason—it’s getting better and better at that over time.

The danger is that some things, like business strategy, are not “provable”—there is no (at least not yet!) mathematical formula that completely describes human behavior with perfect precision.

But it’s easy to sound like there is!

Beware the “easy” button of AI. For these kinds of ambiguous questions, it can make your first thought look finished. Resist! Your brain is still a pretty good AI too!

At the time, 8,804 lines per day felt absurd.

I just reran my GitHub script for the first week of July. From July 1 through July 7, I averaged 214,364 lines of code changed per day.

That is more than 1.5 million lines in a single week. It is roughly 24x the number that felt absurd a year ago, and about 350x my 2022 self.

Here is the embarrassing part. I started drafting this post in mid-May, when my peak day hit 53,747 lines, and my weekly average hit 17,249. I did not publish fast enough. Today’s average day is four times May’s peak day. The post kept going obsolete while it sat in my drafts folder.

And I am not even the interesting data point:

• At the Claude Fable 5 launch in June, Anthropic published a stat from Stripe: in a 50-million-line Ruby codebase, the model performed a codebase-wide migration in one day that “would otherwise have taken a whole team over two months by hand.”

• Ryan Waliany’s startup ran 200+ autonomous coding agents over 30 days, landed 8,000+ commits and 3.6 million lines of code, and rebuilt fifteen SaaS applications from scratch along the way.

One person is doing hundreds of thousands of lines a day. A small startup is doing millions a month. A public company is moving a fifty-million-line codebase before lunch.

These numbers are ridiculous. Ridiculous in a good way. But ridiculous enough to demand two questions: what changed, and what breaks next?

What changed: the loop closed

It helps to see the last five years of AI coding tools as a staircase.

Tab completion (2021). The model finished your line. Magical, but you were still the author.

A function at a time (2022). ChatGPT could write a whole routine on request. You pasted it in, fixed it up, and asked for the next one.

Multi-file changes (2023–2024). Tools like Cursor could hold enough context to modify a real codebase — a feature, not a snippet.

Agents (2025). Codex, Claude Code, Cursor. The tools could finally run the code: reproduce a bug, attempt a fix, execute the tests, and open a pull request. But a human still drove. I prompted, reviewed, and prompted again. The AI worked inside my attention span.

Goal loops (2026). This year, Claude Code, OpenAI Codex, and now Grok have shipped a deceptively simple command: /goal.

You type a completion condition. “All tests in test/auth pass and lint is clean.” “Every call site compiles against the new API.” “The issue backlog labeled bug is empty.” The agent works. After each turn, a separate model checks whether the condition is true. If not, the agent keeps going — no human prompting between steps. The loop runs until “done” is actually done, or until the budget you set runs out.

That sounds like a small product feature. It is not. Every previous step on the staircase made the model a better tool within a human’s attention span. This step removed the human attention span as the unit of work.

There is already a name for the craft that replaces prompting: loop engineering. You stop telling the model what to do next and start designing the conditions under which it decides for itself: the goal, the context it can see, the checks it must satisfy, the tripwires that stop it. Prompt engineering was imperative. Loop engineering is declarative.

Andrej Karpathy put it this way in January:

“LLMs are exceptionally good at looping until they meet specific goals, and this is where most of the ‘feel the AGI’ magic is to be found. Don’t tell it what to do, give it success criteria and watch it go.”

And on why the loops win:

“They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day.”

Stamina, it turns out, was a bigger bottleneck than talent.

Stop counting lines

Which brings me to an uncomfortable admission about the chart above: it has stopped measuring anything about me.

Lines of code was always a goofy metric. Some of the best engineering days delete more than they add. But for decades, it at least correlated with something scarce — engineer-hours at a keyboard. That correlation is now gone. When one person’s average day is 214,364 changed lines, you are not measuring the person. You are measuring how many loops they left running.

So if you are still evaluating developers by output volume, stop. You do not ration what is abundant. Code — the production of it, the modification of it, the testing of it — is no longer a constraint on anything. I wrote about unlearning scarcity in “Abundance Mindset”; this is that post’s punchline arriving ahead of schedule.

The constraint has moved: to judgment, to taste, to knowing which goals are worth pursuing. This change has consequences well beyond engineering org charts.

Three ripples

/goal looks like a niche command for geeky software developers. Here is why it will not stay niche. Three ripples, in increasing order of importance.

1. Legacy software is no longer a constraint. Or a moat.

Twenty years of accumulated code used to be both a millstone and a fortress. A millstone because every change meant archaeology. A fortress because any competitor would need twenty years to re-dig your moat.

Goal loops drain both at once. I can point agents at a crufty old codebase and say “/goal every deprecated API call is gone, and the test suite passes” — and the archaeology does itself. That is the good news. The bad news: a competitor can point the same agents at your category and rebuild your twenty-year feature set in a quarter. Stripe’s 50-million-line migration and Waliany’s fifteen rebuilt SaaS apps are the same fact viewed from opposite sides of the moat.

I can fix my old code fast now. I can also duplicate your old code fast and become your competitor. Which one do you want to be true? Because both are.

This upsets the entire software ecosystem, but especially the corners priced on code scarcity: B2B SaaS with its switching costs and integration timelines, and specialty software for military, government, healthcare, and other regulated niches, where a small vendor’s ancient codebase has quietly been the whole barrier to entry. As I argued in “The Meat Moat,” what remains defensible was never really the code — it is trust, compliance, distribution, and accountability. Everyone whose moat was actually the code is about to find out.

2. “/goal get me 100 customers”

Nothing about the loop is code-specific. The recipe is: a measurable outcome, tools the agent can use, and a checker that can tell whether the outcome holds. Software merely went first, because its feedback loops are fast, cheap, and testable — compilers and test suites are the world’s most cooperative referees.

But “100 paying customers” is measurable too. So is “every support ticket older than a day has a response,” “the books are reconciled,” and “every trial user who went quiet got a personal follow-up.” Give the loop a CRM, an ad account, and an inbox instead of a compiler, and the same staircase — completion, function, multi-step, agent, goal — gets climbed by sales, support, finance, and operations.

The referees are messier outside software, and the failure modes are more public. It will be bumpier. It is also obviously coming; the pattern is too simple and the payoff too large.

Now, before anyone runs off to literally try “get me 100 customers”, I should caution that it may NOT be a good idea depending on your product and the nature of the outreach. Generating a variety of digital ads and testing them could be a good goal. Spamming thousands of people with AI-slop marketing emails is probably not! As the world gets inundated with more and more AI-produced marketing messages, I do think that effective sales and marketing will increasingly rely on authentic human connection. But that’s a topic for a different post!

3. The human is leaving the loop

This is the big one.

Every earlier era of AI — including the “agentic” one we just left — had a human driving. The AI might have been doing the work, but a person initiated each step, reviewed each result, and supplied the momentum. Human attention was the engine, and human attention is the scarcest thing there is.

Goal loops break that coupling. Work now continues while no one is watching. I can set a goal, disappear into a board meeting, and come back to finished, tested work; the loop did not care that I was gone. I have three running right now as I write this!

Follow the implications:

• AI usage decouples from human attention. Demand is no longer bounded by seats or hours. It is bounded by outstanding goals and computing budgets. That is a much, much larger number.

• Jobs shift from doing the work to specifying and auditing it. Writing a good completion condition — precise, measurable, with the right tripwires — is becoming the highest-leverage skill in the building. So is the willingness to check what the loop actually did.

• Accountability gets interesting. When work happens with nobody watching, “Who approved this?” needs a real answer. The companies that figure out audit trails, gates, and rollbacks for autonomous work will be able to use far more of it than the companies that don’t.

We spent seventy years building computers that wait for instructions. We just stopped waiting with them.

Deep dive: how to actually modernize legacy code

Ripple One on legacy software deserves a how-to. The honest summary of the playbook: it is less magic and more checklist. That is a good thing: checklists scale!

Step 0 — Set the target before you start. Decide what you are porting to before any agent touches anything: the target architecture, and the constraints you care about — multi-tenant from day one, your identity and authorization approach, your data rules and constraints, your cloud platform, etc. Then write them down where the agents actually look: the AGENTS.md and skills files that steer every loop you are about to run. The agents /goal run will happily produce an architecture. Step 0 is how you make sure it is your architecture.

Step 1 — Archaeology. For any non trivial software system, nobody really knows what a twenty-year-old product actually does. Not the docs, not the comments, not the last engineer standing. So do not ask one source — triangulate from four:

• Point a computer-use agent at the running product and have it click every button, open every screen, and write down everything it finds.

• Have a second agent read the documentation — what the product was supposed to do.

• Have a third read the code — what the product actually does.

• Have a fourth probe the API — what the product promises other software it does.

Merge the four into one feature list. Here is the part that matters: each entry is not a bullet point, it is a mini spec — what the feature does, the steps to reproduce it, what “working” looks like. Detail is good. Detail is what the loops will run on.

Step 2 — Human review of the list. This is the step people want to skip, and it is the most important one. The AI will build exactly what the list says, so make sure the list says what you want. If you can at all afford it, run this step mechanical-turk style: every feature spec gets two independent human reviews, and a third if either reviewer made edits. It is tedious. It is also the highest-leverage tedium in the whole project — you are editing what a fleet of agents is about to spend millions or billions of tokens building.

Step 3 — Carve out the bad behavior. My strong recommendation: match the existing functionality first, then fix and improve later from a good base. Porting and redesigning at the same time gives the loop a moving target. But some features genuinely should not survive the trip no matter what — say, an authentication shortcut that is now a security gap, or a third party library that is no longer available. Carve those out of the list explicitly, and either write them a fresh spec with its own tests, or consciously park them for later. The reason is mechanical, not aesthetic: a goal loop needs an anchor to verify against. A straight port has the perfect anchor — the old system itself. A carve-out has no reference implementation, so it gets built the normal AI way — spec first, tests as the anchor, no reference. In practice, expect a lopsided split: the overwhelming majority of features port straight across, and a small, stubborn minority needs the carve-out treatment.

Step 4 — Stand up the reference, then let the loops run. Keep the legacy system running side by side as the reference environment; it is about to be demoted from product to answer key. Then set the goal: “Implement every feature on the list — testing, debugging, and fixing as you go — until every page matches the reference environment.” Fan the work out, roughly an agent per feature, and let the loops grind. They build, they test against the reference, they fix, they keep going. You review at the gates.

That is the whole playbook, and it doubles as the punchline of ripple one: your legacy system stops being the thing you have to maintain and becomes the spec for the thing that replaces it. The millstone becomes the answer key.

The catch—shifting bottlenecks

The obligatory cold shower: more code is still not better code. A huge diff can mean progress or churn. Karpathy, in the same post quoted above, says he still watches the agents “like a hawk,” and the industry is bracing for a flood of new software. My own loops only count because they end at gates — tests, CI, review. (In May’s sample, roughly one in six of my changed lines was test code; the loops write their own referees.) The number is not the point. The outcome is—software is meant to serve its customers! It’s either doing that or not!

That creates the next bottleneck—the rest of the organization. If you can build software features crazy fast, how do your sales and marketing teams keep up? What about your customers? I personally eagerly download every update of Claude and Codex. But if I were running the software that handled bank transactions, I would be a bit more cautious! Not every customer wants weekly or even daily software updates.

It’s not just a simple “how fast can you sell?”. Increasing the speed of one function can expose bottlenecks elsewhere. In one company I spoke with recently, they started writing code with AI. But they kept their hierarchical decision making process: every change had to go through a series of approval meetings. Even if a feature could be implmented in an hour, it would still take weeks to get it shipped by the time the half a dozen or more vice presidents could get through the review meetings!

Conversely, another organization I’m familiar with is shipping features incredibly quickly. But all that’s happening is their product is becoming more and more unusable: an increasingly unweildy mess of disjointed features. Just because you can ship fast, doesn’t mean you should! Steve Jobs famously focused on removing not just features, but whole product lines! That same discipline and user focus is relevant here.

Thus, if the bottleneck in your organization is a Steve Jobs like “tastemaker”—keep that person! The tastemaker role can be crucial!

There is no universal solution here except for a very simple observation. If you look at literally any company, and software companies in particular, you will find an organization and set of processes optimized around a set of historical constraints. In the past, writing code was expensive. As a result, you would have a whole other team of people (the product team) ruthlessless prioritize any and every feature request. Sales teams got used to a yearly (or longer!) release cadence. Finance teams would allocate resources to projects through yearly budgeting cycles.

With looping, all of the costs and constraints that used to exist are changing. And thus, the organization and processes need to change as well. Exactly how really depends on the specifics of the company, products, and customers (as above, please don’t update the nuclear power plant software every day!).

But change is coming, and it’s not going to be limited to just engineers. The humble and geeky /goal command has set the stage for a broader transformation of corporate life.

Buckle up!

Software is getting cheaper and cheaper to build. It is reasonable to expect that, within the next 2 years, an AI system such as Codex, Cursor, or Claude will be able to autonomously create high-quality, medium-sized software products.

But humans are not going to get any faster! They will still take time to learn new skills and processes, make purchasing decisions, trust new vendors, change workflows, and so forth. Similarly, humans will still be humans—social creatures at heart! The social ties that bind us together will still exist, be it family, friends, or organizing and collaborating on any number of topics.

Just as the cloud created a whole new set of winners and losers in the software industry, AI will create a new set of winners and losers. The winners will be building “meat moats”—businesses that are durable because humans are slow, because we are social, and because trust, liability, adoption, and coordination do not move at the speed of code.

When code becomes abundant, the scarce resource is not software. The scarce resource is human behavior and how that manifests in trust, adoption, compliance, accountability, distribution, collaboration, and habit.

SaaSpocalypse

In the past few months, there has been a lot of discussion online about the “SaaSpocalypse” and the contrarian view of “Software will eat AI” from HSBC and others.

Much of this has been fueled by the dramatic improvements in AI tools’ ability to write software. What happens if software can be quickly written by AI? The SaaSpocalypse argument holds that software firms, or at least many of them, will go away as it becomes trivially easy to write software. The contrarian view argues that enterprise software needs to be extremely reliable and predictable, and that LLM-based AI is inherently non-deterministic and ill-suited for the task.

Both views are right in a sense, or, perhaps better put, both make valid points. That does not help, though, if you are trying to make investment decisions!

A better lens to analyze this is a seminal economic theory from 1937: Ronald Coase’s The Nature of the Firm. Why do companies even exist? Why not just have people, with everything being transactions between people? Or, as applied to SaaS companies, why do we even need software companies if it will soon be possible to vibe code any app you desire?

The answer is transaction costs.

Coase’s theory of the firm holds that firms exist when organizing work within a firm is cheaper than coordinating everything through market transactions. It is not that people are incapable of doing each individual task themselves. It is that the overhead of search, coordination, trust, quality control, accountability, and decision-making becomes too high.

Let’s start with restaurants as a business. Sure, I can grow my own food, harvest it, and cook it. I could even hire different people each day to do these tasks in my home garden and kitchen. But it sure is easier to go to a restaurant to eat!

Restaurants exist because, even though I technically have the ability to grow, harvest, and cook my own food, it is much more convenient to go to a restaurant. Importantly, a restaurant is more than just food; it offers an experience, be it the ambiance, decor, location, service, etc. Even if I had a home robot that could cook any dish I desired, I would probably still go out to a restaurant for date night!

A restaurant encapsulates hundreds of behind-the-scenes decisions and actions (location, menu, ingredient shopping, cooking, etc.) into one simple “pay money, get food and experience” transaction.

As a human, that simplicity is valuable. A restaurant makes my decision-making easier. It creates a space for social interactions. Those things will continue to be valuable, even in the coming age of robots.

Coase’s theory explains why that kind of packaging matters: a firm (such as a restaurant) can reduce the transaction costs a customer would otherwise have to pay in time, attention, risk, coordination, and trust.

The Nature of Software Companies

If we apply this theory to software companies, we can easily identify numerous areas where transaction costs were minimized historically:

• Writing software was hard and took time.

• Software could only be written by trained engineers; finding them was hard and expensive.

• Software requires continuous investment to maintain functionality and address defects.

• Selling and supporting software was hard.

The first three are increasingly no longer true—it is getting easier and easier to write software. That is the argument for the SaaSpocalypse. But the humans who use software are still there!

That’s the meat moat. Anyplace where humans are interacting with software will create new places of friction, the new boundaries that will constitute a defensible and durable software company.

These include:

• Regulations and compliance. One could argue that government regulations will be ASI-proof (artificial super-intelligence). As long as there are humans, there will be governments. As long as there are governments, there will be bureaucracies! And as long as there are bureaucracies, there will be regulations.

  For example, if you want to sell software to the government, you need to get CMMC certification. Cyber AB marketplace tracks the authorized CMMC Third-Party Assessor Organizations (C3PAOs) required for many defense-industry cybersecurity assessments. There are relatively few assessment organizations, and becoming CMMC-certified is time-consuming and difficult. It does not matter how cheap the software is to build if the business value depends on a regulatory gate, an approved assessor, and a human institution accepting the result.
  

• Platform effects. Let’s assume an absolutely perfect future version of Codex that can build any software an individual or company might want. Great! OK, now what happens when the real value comes from hundreds, thousands, or even millions of other people needing to use that software?

  As a simple example, consider something like Windows as an operating system. It is valuable because nearly every piece of hardware works with it, and millions of applications run on it. AI coding tools will eventually be able to build an OS, but who will make sure the OS works with all the different printers, computer hardware, networking systems, etc.? Who is going to train hundreds of millions of users on a new OS?

  Again, it does not matter how easy it is to build the software on its own if the value comes from millions of people using it.
  

• Adoption and change management. AI can write software quickly. It cannot instantly make a company change how it works.

  Anyone who has sold or deployed enterprise software knows this. The hard part is often not the code; it is getting budget approval, passing security review, integrating with existing systems, training employees, changing incentives, updating processes, and getting people to trust the new workflow. Even when better software exists, companies keep using the old system because the organizational switching cost is real.

  That is a meat moat. The software vendor that helps customers cross the human adoption gap can be far more valuable than the vendor that merely ships features.
  

• Distribution and customer intimacy. There are literally tens of thousands of enterprise software companies, covering everything from marketing lead generation to managing the machinery on a factory floor, to order processing.

  Even in a space as “narrow” as order processing, there are literally dozens of subcategories: invoice processing software, accounts payable processing software, software for quotes and configuration—I could go on! A typical large enterprise might use hundreds of these specialty applications—thousands in some cases. In one large global company I’m familiar with, they literally had over 5,000 distinct software tools used to run their back office operations.

  This fragmentation makes sense in a world where software is expensive to build. Solving any particular piece of the puzzle was hard, and often only a handful of companies would go after it.

  In the AI era, a company that is both a) incredibly fast at writing software features and b) incredibly adept and efficient at understanding, reaching, selling to, and serving customers is going to be dominant. Anytime a customer asks, “Can you do X?” the answer will be “yes,” and that capability shows up literally the next day.

  These companies will not be selling software so much as “problem-solving as a service.”
  Up to now, IT and other employees at a company have done the problem-solving, and then would essentially buy the hammers and nails (B2B software tools) needed to fix the problem.

  Going forward, the most capable software vendors are going to move upstream and solve problems for customers, whether it’s taking care of the books, processing orders, finding customers, etc.
  One of my favorite AI tools for small businesses these days is Puzzle. It is an AI-based accounting tool that takes care of the books for a small business without you really needing to think about it. It connects to my bank, Stripe, etc., and, very literally, manages all the bookkeeping with AI automation. It’s replaced both my bookkeeper and QuickBooks (in QuickBooks, I still had to do the work! It’s not automatic).
  

• Liability moats. One of the strongest meat moats will be those involving liability and expertise.

  This year, I ran my personal tax preparation through Grok. To set some context, my personal federal tax returns usually run about 100 pages of filings, so needless to say, there are even more pages of source material that feed into those filings. Certainly not the most complicated out there, but definitely not a quick job either.

  Grok did a phenomenally good job. It literally came within 0.2% of the same number as my professional tax accountant—and it did it within minutes after I gave it all of the source materials.

  It is easy to see that future versions of Grok would be even better.

  Given that trajectory, would I ever want to save money and do my own taxes again?

  Probably not.

  It is not a question of whether there will be tools good enough to help. There definitely will be. It is a question of liability.

  There is so much complexity in those 100 pages of filings. I’m certainly smart enough (or at least I would like to think that I’m smart enough!) to figure it out, given enough time and effort. But I don’t want the liability of being wrong. I’d much rather hire the “expert” (in this case, my certified tax accountant) and rely on their expertise. If there ever was a problem, they would be the ones to take care of it.

  The U.S. tax system is sprawling: Title 26 alone runs thousands of pages in GovInfo, and that is before you add Treasury regulations, IRS guidance, forms, cases, and professional commentary. Going back to Coase’s theory of the firm, it is easier and safer for me to rely on a third party to handle taxes and liabilities.

  There are many aspects of life and business where it makes sense to transfer liability or accountability to another company: legal matters, financial matters, and healthcare. It may be possible in the future for a robot to perform open-heart surgery, but I think even in that future world, I would still want a hospital to perform the surgery rather than do it myself!

The vulnerable software companies are those whose only moat is that the software was hard to build. The durable ones are the companies where the software is only part of the value: regulated workflows, trusted brands, complex adoption paths, ecosystem participation, human accountability, and distribution. AI compresses engineering time. It does not compress human trust nearly as quickly.

There will undoubtedly be many more meat moat categories, and certainly new businesses and new moats we can’t even dream of yet. SaaS companies that don’t evolve will likely struggle or die. But as I’ve outlined above, even in the world of AI, Coase’s theory of the firm still applies. Companies will continue to exist to minimize transaction costs, and some number of those companies will use software to solve problems and deliver results to their customers.

The old SaaS model was: sell software that helps customers solve problems.

The new SaaS model may be: use software, AI, service, trust, and accountability to solve the problem outright.

Everything is constrained.

Engineering time is limited.

Budgets are limited.

People are limited.

Attention is limited.

So I learned to prioritize.

And our corporate processes were based around these constraints.

Endless debates on feature prioritization.

Hard calls on fixing bugs versus shipping on time.

Choosing which leads to call or which competitors to analyze.

This mental model of constraints has shaped everything about how modern businesses operate.

And sometime around December 2025, it quietly became wrong.

Not incrementally wrong.

Categorically wrong.

The Scarcity Era (a.k.a. Everything I Was Taught)

Scarcity thinking was rational for a very long time. It simply reflected the reality of the world. In software, engineers were scarce and expensive. And even the best engineers could only code or design so quickly. Ditto with business analysts, salespeople, marketers, etc. Any work requiring intelligence required humans, and that meant scarcity.

Scarcity wasn’t a mindset.

It was physics.

The Inflection Point: “Why Not Do Everything?”

In late 2025, AI crossed an invisible but profound threshold. Interestingly, there wasn’t really any momentous “oh wow” release like the original ChatGPT release in 2022. Instead, it was a series of incremental updates to a wide variety of AI technologies—Opus 4.5, GPT 5.2, Cursor plan mode, and so on.

Individually, each update was relatively unremarkable—a few extra points on testing benchmarks, a new ‘plan mode’ feature, etc. Collectively, however, we reached a point where AI tools could run for a long time in parallel, tackling many tasks at once.

As one of the top AI engineers, Andrej Karpathy, has said:

Suddenly:

• Listening to and analyzing every customer interaction is trivial.

• Analyzing every support ticket is cheap.

• Testing every business idea against every major strategy framework (Jobs to Be Done, Blue Ocean, Porter’s Five Forces, etc. costs pennies and can be done in seconds.

• Writing, fixing, refactoring, translating, and validating software is now parallelizable. Why not have AI tools start fixing every bug the moment it's reported?

• Monitoring the competitive landscape is now cheap. Just watch every product release, every press release, every job posting, every patent application for every one of your competitors.

The old question —

“What should we prioritize?”

Is now replaced with a much stranger one:

“Why not do all of it?”

That question sounds incredibly naïve until you experience it firsthand.

As Yoda famously said in The Empire Strikes Back, “You must unlearn what you have learned”.

Abundance in the Real World

1. Every Customer, Fully Understood

We used to sample customer feedback. Now we can exhaustively understand it.

Every sales call.

Every email.

Every support ticket.

Tools like https://bagel.ai can integrate across a variety of data sources to provide these insights. Gong.io records and analyzes all of your sales calls.

2. “Fix All the Bugs” Is No Longer a Joke

Windows famously shipped with hundreds of thousands of known bugs. Not because Microsoft didn’t care — but because prioritization was mandatory.

Today?

AI doesn’t care which bug is “more important.”

It works on all of them simultaneously. Software bug-tracking tools like Linear can integrate directly with AI programming tools like Codex—simply assign bugs to the AI, and it’ll start working on them! https://linear.app/integrations/codex

3. Lead generation

I can’t tell you how many go-to-market plans I’ve seen (and created myself!) that were purely theoretical: “We’re going to sell to mid-sized businesses in the healthcare industry.”

OK, that’s a nice start, but which companies? What unmet need are we solving? Who specifically in those companies has that need? Who would make the purchase decision?

In the past, we had to resort to these broad categorizations. Tools like targeted advertising allowed us to get a bit more precision, but a more thorough analysis was simply impractical.

No longer—instead of these theoretical discussions, it will soon be possible to have incredibly precise go-to-market plans: here is a list of 100 possible customers that need our product, here are the key individuals at those companies, and so forth.

Branding and brand awareness (particularly for novel products and services) are still incredibly important, of course. Even here, with AI content generation tools, it’s possible to create vastly more content than was possible previously. Why not create content for all of the scenarios your product or service solves? That can help with awareness marketing and also with follow-on support! I still find it frustrating that, for many of the products I use, the documentation and online help are often incredibly bare. Will this product really solve my specific problem?

4. Results meetings, not planning meetings

Can 100% of all bugs be fixed with automated AI tools? Probably not—at least not yet! But that doesn’t matter. Imagine now running a bug triage meeting where, instead of debating the theoretical impacts of fixing a bug (or adding a feature or changing a legal contract or whatever), the discussion is about “hey, our AI has it working and here are the results.”

This same idea can apply to many different kinds of meetings. A few weeks ago, for example, a good friend currently teaching at a university called with an interesting technical question. His class was debating this topic, and he asked if I could hop on a call with them to discuss it.

I said “sure”—but rather than show up to the call and pontificate, I used v0.app to build a working prototype of the solution. I showed up with answers and proof, not speculation. Most remarkably, it only took a few minutes of my time—I had an idea of what the answer might be, so I simply asked v0 to give it a try.

What if every corporate meeting ran that way? There is basically no reason anymore for opinion and theory meetings. Why not show up with results and analysis? Often, that only takes seconds now—at least to get started.

Attention: the one true scarcity

With all of these incredible AI advances, there is still one thing that will forever be scarce: human attention. Each of us is bombarded with constant demands for our attention: emails, texts, kids, colleagues, social media, you name it.

Simply generating 10,000 social media posts for your company doesn’t solve the problem of breaking through all the noise.

Ditto for features. Just because you can add a feature to a product doesn’t mean you should! Microsoft Teams, for example, is the Cheesecake Factory of software. It has a bit of everything! It’s hard to think of a feature Microsoft did not add! The result is a confusing and cumbersome mess.

In a world in which content and software are plentiful and cheap, real value will be created by those who can solve the attention problem.

Make my life simpler, please!

AI technologies are not likely, at least anytime soon, to substitute for the human experience: Empathy. Taste. Artistry. Judgment. Emotion. Companies that tackle these dimensions will excel in a world of abundant intelligence.

Final Thought

Scarcity trained us to think small.

Abundance forces us to think honestly.

The hardest part isn’t learning new tools.

It’s unlearning the reflex to ask:

“What can’t we do?”

And replacing it with:

“Why aren’t we already doing this?”

Yoda was right.

You must unlearn what you have learned.

You don’t need to look far to see examples of both success and failure—Cursor.com, for example, is a hugely popular programming tool, yet it literally is built from a copy of the equivalent open source product from Microsoft (Visual Studio Code).

Cursor took on Microsoft, the world's largest software company, head-on and is unambiguously out-innovating them with a superior product. They are now at a billion-dollar revenue run rate! How did they pull this off?

Conversely, for every successful example, there are literally hundreds of startups that struggle or outright fail. Fauna.com, for example, arguably had some of the most advanced database technology on the planet. They solved many of the problems that bedevil software engineers with databases, including scaling, middleware complexity, and multi-tenancy support. Yet earlier this year, the company shut down. What happened?

While there are many factors behind the success or failure of a business, from go-to-market to funding to culture, for this post, I want to focus on product.

What makes great products great?

There are countless product development frameworks out there—two of my favorites are “Jobs to be Done” by Clayton Christensen and Don Norman’s Emotional Design

Jobs to be Done can be summarized with a pithy line: customers don’t buy products; they hire them to get a specific job done in their lives. Or as Theodore Levitt said: “People don’t want to buy a quarter-inch drill. They want a quarter-inch hole!”

Similarly, Emotional Design is all about crafting delightful experiences. Both the Porsche 911 and the East German Trabant solve the “job to be done” of transportation. Yet one of them arguably is more delightful than the other!

Over the years, I’ve tried many different frameworks, none of which have been comprehensive enough. There are beautiful products that don’t actually solve any real problems—think of the animated cybersecurity threat maps that are fun to look at but don’t actually solve much in the way of cybersecurity. Or my friend’s new startup that solved a key AI accuracy problem, but in an impenetrably complex way.

Recently, I’ve found myself articulating the same principles across all the companies I’ve worked with. It boils down to creating some SPARK!

TL;DR

SPARK is a five-point product lens to build software people adopt, love, and trust:

• Simple — dead-obvious conceptual model; learning curve ≈ flat.

• Purposeful and Prioritized— solves a problem that matters now; no fluff, no bloat.

• Attractive and Attentive— emotionally appealing; sparks delight and pride in use.

• Reliable — it doesn’t flake; trust via uptime, consistency, and polish.

• Known — aims at problems users already know they have.

Treat it as a checkpoint you can run in 30–60 minutes at any stage—from napkin sketch to GA. To be clear, it’s not a guarantee of success—there are still many other factors at play, such as pricing, marketing, and other go-to-market activities! But I can easily argue that if your product struggles in any of the SPARK dimensions, it’s likely to struggle in the marketplace.

What SPARK Is (and isn’t)

This is not a new process religion. Keep Scrum, Shape Up, or Continuous Discovery—whatever gets you shipping. SPARK is a lens you pass your product through to expose weak spots you’ll otherwise rationalize away.

S · Simple

The conceptual model is obvious. The conceptual model is: “What do you need to know to use the product?” In the iPhone, for example, everything is an app, and as long as you know how to touch and tap, you can use an iPhone. Users shouldn’t need onboarding videos to accomplish their first meaningful task.

• Ask: Can a new user tell what the primary object is and what they can do to it? (Channel → post. Doc → edit. Ticket → purchase.)

• Try this: Explain your product in one sentence using one noun and two verbs. If you can’t, your model is likely muddy.

P · Purposeful and Prioritized

You’re solving a burning job—not just building features.

• Ask: What bad thing happens if this job remains unsolved a month from now? Not three years from now, but right now.

• Try this: Strip your roadmap to three outcomes customers would pay to accelerate. If a feature doesn’t move one of these outcomes, cut it.

A · Attractive

Delight is a feature. Emotional resonance drives adoption, retention, and word-of-mouth.

• Ask: Where are the “mini-wow” moments? Auto-correct as you type. One-click sharing. An animated chart that makes your user look brilliant in a meeting. Even something as simple as the “hoot” sound the Owl meeting camera makes when booting up.

• Try this: Mark three moments in your flow where a subtle animation, sound, or copy spark will earn a grin.

R · Reliable

Trust shows up as absence: no crashes, no weird states, no pauses, no “try again.” In B2B products, especially, flaky = dead. Note that reliability is not just about crashes and failures; speed also counts! As I write this, I am using the Grammarly.com checker. I doubt I’d be as satisfied if a grammar check took two hours, even if it was more accurate!

• Ask: What’s the ugliest failure mode today? How do we prevent it, not just handle it?

• Try this: Adopt a “reliability measure” alongside your definition of done (SLOs, gold-paths, edge-paths).

K · Known

Build for problems users already know they have. This is sometimes the hardest for innovators and entrepreneurs. Almost by definition, if you are building a startup or a new product, you are solving a problem that the world has not yet solved! Yet if the world doesn’t care yet about solving the problem, it doesn’t matter.

I often struggle with this balance! My very first startup built an early form of what we would now call cloud computing. It was “obvious” that the future was running software across many machines all over the world. In 2025, nobody really debates that—it’s how AWS, Azure, Google, and pretty much everything on the Internet runs. But I was trying to do this in 1991! Even if I was “right” in concept, I was very wrong in my timing.

• Ask: What would your buyer ask ChatGPT the week before purchase?

• Try this: Show your solution to as many prospective customers as possible. If they say “I would buy that right now”, you likely have a winner. If they pay you right away even before you’ve finished the product, even more likely that you have a winner!

Has This Been Done Before?

Yes—and no. SPARK stands on the shoulders of giants and stitches their best ideas into a fast, memorable check.

• Don Norman’s Emotional Design → A (Attractive): visceral/behavioral/reflective layers explain why beauty and micro-joy matter. SPARK bakes this into a non-negotiable.

• Jobs to Be Done → P (Purposeful) + K (Known): JTBD uncovers the job; SPARK biases toward known and understood pain over latent needs to speed adoption.

• Lean Startup & Opportunity Trees → P: iterate toward outcomes, map opportunity space. SPARK complements them by insisting on S, A, and R too.

• EG-SAT / Emotional Goal frameworks → A: academic precision, tough to operationalize; SPARK keeps the heart, drops the overhead.

• Nielsen Heuristics / Double Diamond → S: processes that tend to yield simplicity; SPARK elevates simplicity as a gate, not a byproduct.

• Reliability & Trust: too many UX frameworks underweight this. SPARK puts R on the masthead because uptime, speed and predictability sell.

Using SPARK in the Wild: a 60 minute exercise

0–10 min · Frame

What’s the one-sentence noun+verbs? Who’s the buyer? What do they already call their problem?

10–25 min · Walk the primary flow

Start-to-value. At each step, mark S/P/A/R/K with ✅ or ❌. Capture concrete fixes.

25–40 min · Kill a pet feature

If it doesn’t move P or K, it’s on probation. If it also hurts S or R, it’s gone.

40–55 min · Design one mini-wow

Pick a dull step and inject a tiny delight that also speeds the task.

55–60 min · Commit

Write the three simplest changes that raise your SPARK score this sprint.

Pro tip: rerun a quick SPARK test after customer calls and again before each release. It keeps excellence from being “someone’s job” and turns it into team muscle memory.

A quick SPARK self-test

Score each 1–5 (be brutally honest):

• S: First-time user can do the core job in < 2 minutes without docs.

• P: If we turned it off tomorrow, 40% of users would be very disappointed.

• A: We can point to 3 concrete “mini-wow” moments.

• R: We meet published SLOs, and our top 3 bugs are prevention-fixed.

• K: Our homepage names the buyer’s exact, searchable problem.

25 = you’re cooking. 18–24 = focused iteration. <18 = stop shipping net-new; fix foundations.

FAQs

What about breakthrough products with no “known” need?

Even the new-new benefits ride on a known pain at first. The iPhone didn’t sell “apps”; it sold “a real web browser in your pocket.” Anchor on today’s pain, reveal the new superpower once they’re in.

Is “Attractive” just lipstick?

No. Attractive experiences reduce cognitive load, signal quality, and create pride. People advocate for products that make them look and feel smart.

Does Reliability slow us down?

Only if you bolt it on at the end. When you choose a clearer model (S), constrain scope (P), and remove fancy-but-flaky flourishes (A), reliability gets easier, not harder.

Why should I care?

AI makes it cheap to ship something. That’s not the bar. The winners won’t be the teams who ship the most, but the ones who ship Simple, Purposeful, Attractive, Reliable, Known—on repeat. Good enough software will go by the wayside. Ship something great!

Last but not least, SPARK is not the definitive, be-all, end-all. I’ve love feedback and discussion on how to do even better!

Unfortunately for him, he’s messing up left, right, and center to the point where his bosses and colleagues are super frustrated. Yet he’s oblivious to the issues.

So far, I’ve been unsuccessful in helping my friend, so I in turn talked it over with another friend to get her advice and thoughts. It was clear this was a pattern we’re seeing over and over again.

Arrogance versus Humbleness

Even before the advent of AI, this has been a challenge that has beset many leaders, particularly those earlier in their careers. Readers who have known me a while are likely smirking as they read this—what the heck, Alex is talking about humbleness? That Alex? That would be just the kind of thing he’d do—arrogant enough to think he can talk about being humble!

And in many ways, they would be right! Decades ago, when I was at Microsoft, I was very rapidly promoted, eventually becoming one of the youngest Corporate Vice Presidents in the company’s history at that time. To say that went to my head would be a charitable understatement.

Before continuing the story, allow me to define my terms a bit more. Arrogant, for this post, means convinced of your ideas and not willing to learn or listen. Such a person might be incredibly charming or otherwise a delight to be around, but if they don’t have a learning mentality, I’ll classify them as arrogant. Similarly, humbleness does not mean meek or lacking in conviction or decisiveness. It just means a willingness to say “I don’t know” and learn as much as they can-even if they have to make quick decisions in the face of limited information. You can be fast and hard-charging, yet still humble.

Anyway, back to my Microsoft days. At one point in that journey, I was responsible for a large, up-and-coming division of the company. For a variety of reasons, I felt the division needed a radical change in engineering culture, in pace of delivery, and in the business model for our customers. The actual details are not particularly important; for this story, suffice it to say that I failed in effecting the change I was trying to make.

Almost two decades later, I can confidently state that I was 100% right in the specifics, the “what” of the change. I was also 100% wrong in the “how”. I was so convinced of the need to change—it was so obvious to me back then—that I failed to lead the other people around me through the change—my team, my bosses, my peer executives.

I was right and they were wrong. Obviously! So why don’t they just accept it and do it?

Looking back on this, quite a number of my colleagues actually tried to help me and give me some coaching. It must have been so painfully clear to them how badly I was messing up! To respect their privacy, I’ll just say “thank you” to Bill, Steve, Hank, Ted, Toni, Eric, Mike, Brian, Lisa, Tom, Robbie, Satya, Kathleen, Bob, Ray, Rick, Qi, and undoubtedly countless more who tried to help. Despite all of that, I was too convinced of my own righteousness to see that I was actually messing up! Or, to put it differently, I wasn’t humble enough to listen to feedback and learn how to be more effective. I was right and they were wrong after all!

Coming back to the present, my friend is basically replaying the same mistakes I went through. He has a big new job and is firmly convinced of his ideas and ability to execute. But he’s completely failing to learn, listen, and collaborate with anybody else, particularly those with different opinions and experiences.

Arrogance over humbleness.

If this story were just about my friend, I doubt I would have written this up as a post.

What’s fascinating to me is that I’m now seeing this pattern everywhere, not just with newly promoted executives growing into their roles.

The big reason, of course, is AI.

It’s changing literally every field, and changing it fast. Nearly everything I thought I knew about building software products is basically thrown out the window. Worse, it’s changing literally every month as Google, OpenAI, xAI, Anthropic, and others constantly one-up each other with regular advances in AI-powered coding tools.

Trying to answer a “simple” question like “how long with this feature take?” is maddeningly difficult these days.

If the historical answer had been three weeks if it were done by hand, now the answer can be as short as ten minutes as the Codex AI agent just gets it done, or as long as four weeks (one week of trying to get the AI to do it before you give up and and say, forget it, I’ll do it by hand). Even harder, software projects used to have a fairly predictable “glide path” of becoming more and more solid as more and more bugs were fixed. Now, left unchecked, AI tools will gleefully change hundreds of lines of code at a single go, often introducing as many bugs as they fix. Ugg, when does this thing ever get stable?

It’s not just software development. Take marketing, for instance. In the “old days” (i.e., just a few years ago!), you could get a lot of mileage out of a content strategy focused on just publishing a large volume of decent-enough, valuable-enough content.

These days, however, that strategy is almost punished—it’s so easy to generate tons of AI-generated content slop that everyone is doing it. As a result, AI slop is now just part of the background noise. It’s basically ignored—both by humans and by other AIs like search and social media engines charged with maximizing revenue for their companies. In contrast, more authentic content from real humans that generates real human interactions and engagement is performing dramatically better. Unless, of course, that AI-generated content is funny stormtrooper videos:

Marketing will undergo another revolution in the coming months and years when AI starts automatically generating personalized content for each customer, live on demand. What is the right online marketing playbook, then? If you’re stuck in the past, it’s probably wrong!

I could go on and give example after example in other fields—human resources, legal, manufacturing operations, and so forth. The pattern still holds—everything is changing, and changing fast.

The arrogant folks are struggling the most to handle the change. I am seeing this over and over, nearly on a daily basis (names and specifics modified for privacy):

1. An engineering leader who thought he could create a competitive, high-performing engineering culture, yet hasn’t written code in nearly a decade and doesn’t even know about the latest software development tools.

2. A marketing leader who is “on the AI bandwagon” (in their words) because they automated cold email outreach with AI, going from a handful of personalized, handcrafted individual cold outreach emails to tens of thousands of spam emails sent daily.

3. The CEO who dismissed a tiny startup as “just a startup”, yet nine months later is losing nearly all of his business to them.
   
   As a side note to all aspiring entrepreneurs—now is a great time to start a company! There are so many incumbents who will be “arrogant” and stuck in their ways. It's easy to outmaneuver them and solve customer problems with a faster, better, and cheaper solution. Check out this post from Andreessen Horowitz for a dramatic data point! https://x.com/AnjneyMidha/status/1967353416465965320

   

In contrast, I’m seeing humble learners absolutely crush it:

1. A finance executive with some great product ideas used v0.app and built her own prototype in hours. It’s undergoing user testing now!

2. A recent college graduate who successfully replatformed an entire software platform to a radically new and powerful database (https://convex.dev) in just a day. Nobody told him it was impossible, so he just did it anyway.

3. A CEO driving a strategic planning offsite. Usually, these off-sites involve lots of PowerPoint, lots of talking, maybe a memo produced at the end, and then some fun(?) team bonding (for whatever reason, whirlyball was the social bonding event of choice in my years at Microsoft. If you don’t know, just look it up).
   
   Instead, this CEO got the team together for two intense days. In those two days, the current product roadmap was fully researched and reprioritized, and a new business line expansion was explored. It wasn’t just talk—the team literally did the customer and market research on a dozen different ideas, AND turned each idea into a working product for customer testing.
   
   The CEO did yield to tradition, however, and had hot donuts brought in. No whirlyball, though!

4. A CEO of a mid-sized financial consulting company is personally writing AI agents using Lindy.ai to automate his company’s processes, driving up revenue.

I could go on with more wild examples.

AI, in essence, has become the great equalizer and opportunity creator.

No longer do you need to spend years learning how to code to try out a technical idea—vibe coding tools can let anyone do it. It used to take days, if not weeks, to become facile enough in Adobe to decently edit a video. Now it can be done in seconds.

The world is changing fast. We all need to be humble learners.

At least as long as the robots let us….

I’m not sure I can ever go back to the way it was.

I felt this way getting my first personal computer in the early 80’s.

Again two years ago with the launch of ChatGPT.

And now, with the launch of OpenAI Codex for the web.

Last week, I wrote about how well OpenAI executed the Codex launch. What I failed to anticipate was how transformational that product was going to be.

For all of my non-technical readers, Codex is a software programming agent from OpenAI. At one level, AI tools for software developers are now old hat. As I’ve written a lot about over the past few years, the basic idea is that these tools help developers write and understand code faster. They’ve been truly remarkable.

However, up until last week, as amazing as those tools are, they fundamentally worked only when the programmer was working.

Now, that has changed. Codex allows you to create independent agents that can run coding tasks on their own. Google shipped something very similar called Jules, and Microsoft did as well with Github Copilot Agents. I’ve been using all three over the past week.

This is what has been crazy.

I’m now coding even when I’m not “coding”.

So far I’ve managed to have up to twelve different AI agents all programming for me at the same time. And in the meantime, I’m doing something else—email, board meetings, even bike riding. Because Codex works on the web, I went on a five-hour-long bike ride last Sunday with a dozen agents running. I had my phone attached to my bike, and from time to time the different agents would alert me as to the progress they made.

Admittedly I had to stop a few times to give some guidance to the AI, so my five-hour bike ride really should have taken me no longer than four hours! It was still eye-opening—and as a side note for any readers in the Seattle area—this ride was training for the Seattle to Portland 200+ mile bike ride later this summer. Hit me up if you’re going!

This is not just an anecdotal feeling of progress. Out of curiosity, I had one of my AI agents write a program to analyze the code I’ve written over the years. I picked three times of “peak” productivity, where I was coding regularly. 2013 and 2022 were very focused years where I had few meetings and could code regularly during the workday. Then I picked this past winter and the past four days since Codex’s launch. One thing to note about these two recent periods is that my work days are filled with meetings of various kinds, and I can only code in the evenings.

Here are the results, in average lines of code per day:

For the technically astute reader, yes, I know that Lines of Code is a limiting and imprecise measure of productivity. However, it’s directionally interesting, and given the magnitude of the change, it’s an easy way to quantitatively articulate the impact of Codex!

More importantly, I know I’ve only scratched the surface of what’s possible and how far I can push this.

The productivity gains are so addictive that I don’t want to sleep, go to lunch, hop on a Zoom call, or pretty much anything else without setting up some coding agents to work for me in the background.

What happens when that process gets even more automated? What happens when I can set up not just agents to do my work, but also create agents of agents to coordinate whole, large effort tasks?

Truly, my mind is blown. There is no going back from this!

However, I was truly impressed with OpenAI’s launch a few days ago (Friday, March 16) of their latest product called Codex.

Codex is a deeply technical product, meant only for software developers. That, however, makes the lessons learned only more poignant! It’s easy to dismiss large tentpole events, like Apple’s annual iPhone launch events, as being out of reach for the typical mid-sized company.

Everything OpenAI did last week, however, is doable by nearly any company, of any size.

It started with a teaser video on X on Thursday.

https://x.com/OpenAI/status/1923212267677499469

Clear audience identification and call to action.

Then the livestream itself. If you are technical and have time, it’s worth watching!

This livestream video itself was well done, but not fancy. All of the equipment needed for that level of production value can be purchased for less than a thousand dollars.

The livestream itself is also well done. It’s not fancy—the first two minutes are a simple and accessible explanation of the new product, and by the 1:59 mark, they have already jumped into the demo. Very efficient use of time and the rest of the 23-minute livestream is similarly well done. Simple, clear language, compelling demos, explanations of how things work, and ending with a roadmap of what’s to come.

Thus far, this is pretty straightforward—many companies will host a webinar or similar for launching new products.

What made the OpenAI Codex announcement a masterclass in my opinion is what came next: a tight and well-coordinated series of social media posts:

1. Notes from many of the senior executives at OpenAI: e.g. https://x.com/sama/status/1923398457747787817

   

   
   

2. Commentary and real-world “I like this” posts from customers and other early adopters: https://x.com/skirano/status/1923397651225071831

   

   
   

3. Eye-catching videos that were essentially advertisements for the new product. I particularly liked this one: “Fix paper cuts”.
   
   What the heck? How can an AI fix paper cuts? I watched the video just out of sheer curiosity and found myself learning an entirely new use case for how OpenAI codex could solve some of my own coding problems. Even though I watched the live stream and had an initial knowledge of how Codex worked, I had not considered the use cases in the video.

4. Then last but not least, there was an AMA (ask me anything) on Reddit. https://t.co/crIah67MWL. This AMA went into a lot of depth that enthusiasts like me were really curious about.

Importantly, every piece of content they put out for the launch was framed in terms of customers (what problems it solved for you), and just as important—it was authentic. Codex is a product for software developers—the examples they gave were real things developers would like to do, and the talks were given by developers.

Not surprisingly, this content spread like wildfire. Here is the Google trends data for ‘Codex’:

Notice the huge spike at 9 am, just after their livestream.

This is all "free"--no paid advertisements. Just a great product with a really strong content-driven message.

Not to over-trivialize the effort that went into their launch, but any company can do this!

Separately from the marketing excellence of the Codex launch, the product itself is outstanding. I am only touching the surface of what it can do.

Every other AI-driven software development tool thus far has simply increased my productivity. Tools like cursor.com have enabled me to write code faster.

Ultimately though, while I was writing code faster, I was still limited by how much I could do, and how much time I could spend coding versus being in meetings or other work activities—see my last post!

This weekend, however, that changed. With Codex, I set up 12 different AI “agents” to do various coding tasks for me. They all ran in parallel, while I was biking riding! (a training ride for the 200+ mile Seattle to Portland bike ride—ping me offline if you are going!) It was a truly amazing experience.

I know I am only touching the surface of where this is going. I can easily foresee a day soon when I have AI agents coding for me 24x7.

Buckle up! The pace of change is about to accelerate even more!

And if you were paying attention instead, how many other people on the call were distracted (and presumably doing email)?

This begs the question: if you are able to do email in an online meeting, why are you in that meeting in the first place? Is that the best use of your time?

This single observation raises a host of questions, everything from social decorum and norms (would you be reading email if you were in an in-person meeting?) to the debate between remote working and working in an office. (As a side note, Jamie Dimon, the CEO of JP Morgan, recently was recorded talking about his views on this—if you haven’t heard, it’s worth a listen)

In my mind, however, the real question is not online meetings or a return to office. The real question is, “How many meetings and how productive are those meetings”?

I have been using reclaim.ai from Dropbox recently as a calendaring tool. The reports it has are fascinating—here is an analysis of the last four weeks of work time for me:

This data was fascinating. For better or worse, the bulk of my work days are spent on online calls with the various companies that I work with. Then, in the evenings, I find some uninterrupted “maker time” to code or otherwise do meatier work on my own. But after accounting for email time and the like (“shallow work”), roughly only 10% of my time currently is spent doing deeply technical work individually.

Anecdotally, many of the executives and companies I work with are caught in a similar dilemma. A fellow CEO recently lamented that most of her team’s time was spent in meetings.

Meetings about what? What got accomplished? Did those meetings need everyone to be there?

Enter AI

At this point, I expect most of my readers have experienced at least some productivity wins from using AI tools like ChatGPT, Cursor, and similar. In my own work, I use these tools for everything from automatically doing all of my accounting (thanks Puzzle.io!) to doing deep research on various companies and technical topics to writing code, of course! The wins and productivity gains are truly staggering (not to mention fun to be on the bleeding edge!)

However, most of those productivity gains come from the 10% of the time that I spend doing “deep work.”

Ouch!

For any business that is based in some way or other on knowledge work, these kinds of metrics should be incredibly painful and a wake-up call. Imagine running a factory where the factory line only ran 10% of the time. Or running a restaurant, but you could only use 10% of the stoves and ovens in the kitchen.

To be fair, time spent in many meetings can be very valuable—particularly if you are engaged and contributing (and not on email!). Teamwork and collaboration make a difference.

Ultimately, though, for nearly every business and job function, there is a work product that needs to be produced, be it code, marketing materials, or a quarterly financial report. And as I talked about in a post last year, speed matters!

Given this, can we compute the optimal balance of meetings versus deep work time? How much AI should we use?

Computing this number exactly is hard. I find it difficult right now to accurately predict the performance improvement I will get from AI tools. Sometimes, it is absolutely off the charts—several times in the past week, for example, I had AI software development tools (Cursor and Claude Code) write code in minutes that would have otherwise taken me several days to write by hand. It’s truly impressive. Other times, the AI gets stuck or otherwise can make a hash out of the code, and it’s faster to do it by hand.

Similarly, I attempted to get AI’s help writing this blog post. Absolute fail! Sure, ChatGPT, Claude, and Grok all produced an essay on productivity. Two years ago, it was stunning that it even worked at all! But now that is old hat—today’s question is: can AI write well? Currently, the answer is “no”—at least not for this blog! But, there are many other cases where I regularly rely on AI essay generation: Deep Research from OpenAI, for example, is particularly good at analyzing a company and its products. For companies that I’m not already familiar with, it’s an incredible time saver, doing in minutes what would take me hours of research and reading.

However, I do not think it’s necessary to have an accurate measure of the productivity wins of AI tools. The key insight here is at least as of this writing, the current generation of AI tools excel at augmenting human knowledge work. Outside of specific use cases, these tools are not yet ready to run completely standalone. Thus, we can simplify the reasoning here to basically postulate that an hour of AI-enhanced work time is more productive than an hour of “regular” time.

AI Turbo-charged Humans

However, the amount of the productivity win frankly doesn’t matter. It just matters that there is a productivity win. Whether it’s 10%, 100%, or even 10x is not as important.

What is important, is how often you get to apply that win.

Now, we come back to meetings in general and online meetings in particular. Even if AI tools can help you be ten times more productive, if you are only getting to use those tools 10% of the time, then overall, you’ve only doubled your net productivity, not 10x’ed. That does not seem like a big win here.

But actually, it is!

Let’s do simple math to start. Let’s assume that for every hour of work, you can generate one hour of value (yes, this is a vast oversimplification, but it will get the point across!). So, with a 40-hour work week and 40 hours of “regular,” non-AI-turbo-charged work, you get 40 hours of output.

But if you can improve your schedule by just 10%—freeing up 4 hours a week to do AI turbo-charged work, you could double your output (assuming the 10x productivity improvement—insert whatever number makes sense for your work!). If you can free up another 4 hours, you will nearly triple your output to 112 hours of value.

Wow. There are very few things in the world that are this leveraged. A 20% efficiency improvement yields almost tripling output.

Maker Time

So far, this is just simplified and straightforward math. I would argue the gains in real life are actually much larger.

There is a well-known concept of “maker time”. If you are writing a proposal, writing some code, creating a marketing video, or preparing a speech—whatever it is- that takes time and focused attention. This is “maker” time. Everyone, even leaders (and I would argue, especially leaders), benefits from having maker time.

Maker time means having some time and space to focus, free from distractions.

A famous cartoon from years ago captured this well. Even if you are nontechnical, the point is still valid: to create something, you have to get your head in the zone, thinking through all of the technical complexities in the code you want to write or the nuances, arguments, and points of the blog post you are writing, and so on. Interruptions can cause you to slip out of the zone.

Worse, if your days and your team’s days are filled with back to back Zoom calls and endless emails and Slack messages, you may find it difficult to ever get into the zone, at least during normal work hours!

Putting this back into the simple math framework above, the more you can carve out dedicated “maker” time, the more productive you will be—without or without AI tools! Thus, if we assume that every extra hour of maker time yields an extra 10% output improvement, the chart now looks like:

Even more leverage and payback for improving the efficiency of how you spend your work time!

Make it practical

This all sounds very nice from a math perspective, but how to do this in practice? After all, what might happen if you start telling your boss (or your board or your clients), “Hey, I’m just not taking any more meetings”? It might not go over so well!

The key is the insights above—the goal is not zero meetings! Human connection and collaboration count! But can you find a 10% or 20% reduction in your meeting load?

Obviously, my starting example is a good place to begin. If you are able to do email in the meeting, then maybe it’s not so important for you to be there!

This is easier said than done, of course! When I was young and early in my career, I was eager to be involved in meetings—meeting with the big boss made me feel important and valuable. Now, I’m almost the opposite! How many meetings can I delegate and not go to?

One common reason meetings become inefficient is that they are trying to accomplish too many things simultaneously. Consider the following taxonomy of goals:

1. Information sharing (e.g., status reports, decisions, news, etc.)

2. Collaborative work (e.g., trying to solve a particularly hard problem that needs a variety of viewpoints or expertise).

3. Social engagement and connections

A weekly team meeting can often mix all three goals at once. You might start the team meeting with some pleasantries, chat about your weekend outing, and then give a few updates. Invariably, however, online meetings tend to fill the time allotted, politely described as going off-topic, more rudely as ratholing!

Would it be possible to split apart those goals? It’s quick and easy to create videos these days? Could the relevant information to be shared be done in a video and sent via email? Or perhaps the 'standup’ portion of the meeting could be kept very short, say 15 minutes, with any discussions done in follow-ups with a more narrow audience of just the involved team members.

One very handy tool is meeting transcription tools, such as from Fireflies.ai. It does a great job at summarizing meetings so that nonparticipants can quickly and easily learn what happened—much faster than trying to watch a meeting recording!

Of course, productivity is not just a matter of improving meeting time. Modern corporate life is bedeviled with other inefficiencies, not least of which is email and messaging overload. But that’s a topic for another post, I think!

Two concluding thoughts to leave you with:

1. In an AI-driven world, a 10% time efficiency—if it creates more ‘AI turbocharged maker time’—can yield disproportional wins. It’s arguably one of the single biggest productivity levers in your organization right now.

2. If you don’t do this, your competitors will!

As always, comments and discussion are welcome!

Said no one, ever.

Outside of some engineers at Google and Microsoft, few people, if any, wake up thinking about search as a fulfilling activity that improves their lives or work.

Instead, people are thinking about something very relevant to them, what they are working on or wanting to do:

• “Did any of my sales leads get a promotion, and should I congratulate them?” the salesperson wonders.

• “What is the latest AI breakthrough?” asks the software engineer.

• “What’s the news for my portfolio companies?” asks the investor.

• “What companies are hiring?” asks the college student

• “I want to use Siri in my ski helmet; what headphones support this?” asks the work-addicted skier who shall remain nameless in this post…

These are all things that you might use today, such as Google or Microsoft’s Bing search engine, as a tool to help answer those questions. But it often is an unsatisfactory experience—a list of blue links twenty years ago was a fantastic breakthrough to find information on the Internet. But today, it’s not the quickest way to learn something.

If you haven’t had a chance to use the new search functionality in OpenAI’s ChatGPT, sometimes called SearchGPT, it’s incredibly good. Here is a screenshot of a search for ski helmet headphones:

Here is the same search in Google:

And finally, the same search in Bing:

Which of these is better? Note that only ChatGPT actually answered the question directly. The other options provided by Google and Bing don’t support Siri fully. Figuring out the answer to my question would involve a lot of clicking and reading non-relevant web pages.

Almost two years ago, I noticed that my own usage of Google had dropped precipitously with the introduction of ChatGPT:

That trend has only accelerated for me. In reviewing my Google usage for the past month, the number one usage was Google Apps (Google Drive, Gmail, etc.), followed closely by Google Maps. My Google search usage was effectively zero (outside of testing for this article!). I’ve entirely swapped to using AI search from OpenAI and Grok on X.com (formerly Twitter).

I’ve swapped for a simple reason: SearchGPT solves my problem. Ten blue links do not solve my problem—When I have a question, I’m looking for an answer as fast and accurately as possible.

The better mousetrap

Even though SearchGPT is a significant leap forward, very arguably, the fundamental model is still wrong for many scenarios:

Why do I have to search in the first place? Why can’t the computer just tell me what I need to know, when I need to know it?

Obviously, there are times when you have a question and want to know an answer. But for many scenarios, it would be great to have an AI system proactively looking out for you.

Think of many of the scenarios I started with in this blog post. Outside of the shopping one, in each case, an AI could constantly be running in the background and alerting me when something happens.

Broadly speaking, this is the idea behind “Agent-based AI.” Both Salesforce and Microsoft, for example, are talking a lot about agent-based AI approaches. If you aren’t familiar with the concept, this six-minute supercut of Microsoft CEO Satya Nadella’s speech earlier this month at Ignite is well worth watching:

Curiously, one ingredient was missing from the talk: What if my AI agent had the full power of an AI agent combined with the real-time, up-to-date knowledge of a major search engine?

What could be possible?

Microsoft and Google have invested billions in making their respective search engines extremely powerful. Not only have they indexed the web, but they can also find out real-time flight status and the status of your UPS packages and send you alerts when, say, an article is written about you.

Despite those investments and competition between the two technology giants, Microsoft's and Google's relative market share has basically remained unchanged. Search innovation, at least from my usage and experience, has come from elsewhere, like SearchGPT and Perplexity.ai. The “10 blue links” model is not as effective as SearchGPT.

It’s clear that copying Google has not led to a material change in Microsoft’s Bing search engine's competitive market share.

But what if Microsoft took a different approach?

Instead of trying to catch up to Google on the traditional model of doing searches, what if Microsoft instead went all in on the Agentic AI model as the future of Bing search?

It would mean adding real-time search knowledge to the CoPilot experiences Satya talked about above.

Crucially, it would also mean adding real-time web results to their OpenAI API implementation for other developers to use. Currently, this is not available from either Microsoft or OpenAI, even though OpenAI now has SearchGPT (so there is clearly at least an internal API for OpenAI to use).

In a world where magical AI advances seem to happen on a near-weekly basis, it’s tough to get too excited about any particular feature—wait a week, and something else cool will ship! However, a SearchGPT API would be transformative, not just for Microsoft’s Bing but also for nearly every software vendor, particularly business software vendors.

Think about every role within a company, whether it is a sales role, an engineering role, legal, finance, etc. Today, nearly every person in those roles uses search on a reasonably regular basis to perform their jobs—both searching the public web and their internal systems.

Now, imagine an AI agent essentially conducting those searches on its own, learning the information, and synthesizing the results for the employee.

Going back to the customer scenarios I started with, we would have a profoundly different experience for those people:

• “Did any of my sales leads get a promotion, and should I congratulate them?” the salesperson wonders. Now, your AI agent can monitor LinkedIn and other news, not only alerting you to relevant changes for your sales prospects but also drafting the start of an email outreach.

• “What is the latest AI breakthrough?” asks the software engineer. Now, your AI agent knows the type of software you are working on, and it helpfully and proactively suggests new technologies relevant to your work. If this was then coupled with AI code writing tools like Cursor, Windsurf, v0, Github Copilot, etc., not only could it suggest new technologies to use in that engineers’s work, but it could do or at least start the implementation on its own!

• “What’s the news for my portfolio companies?” asks the investor. Many investors have an investment thesis: “We invest in B2B software businesses targeting market verticals.” Now program the AI Agent with that knowledge, and let it alert you when there is relevant news to that thesis—market changes, company announcements, whatever. If the AI agent were deeply integrated into the search index, it could “see” and analyze the same information the search engine was ingesting, analyzing it with the particular insights and investment thesis in mind.

• “What companies are hiring?” asks the college student. Same idea here—the student could upload their coursework, their preferences, and so forth, and let the AI continuously scan not just new job postings but also analyze social media posts and so forth to learn who is hiring, what types of questions are asked in the interviews, and so forth. In other words—it could actually help that student land a job versus just showing 100 open positions on LinkedIn that the student would otherwise tediously and mechanically apply to.

I could go on, of course, but I suspect the idea is clear. Search today is a reactive experience driven by humans, starting with a search query. When combined with agentic AI, it could be so much more: a proactive assistant constantly monitoring the world’s knowledge to help each and every person individually with their work and personal needs.

Built as a platform, it could open up a wave of innovation, similar to what App Stores did on mobile phones.

Whether Microsoft, Google, or a new entrant like OpenAI or Perplexity.ai does this, 2025 is going to be another wild year. Moving from a “chat-style” experience to one where AI assistants work on your behalf 24/7 will be profoundly transformative.

Buckle up and Bing Me!

Of course, in the past year, at least three other companies have started in his general space. Are they competitive? I have no idea, but that almost misses the point. My friend has missed out on a year of learning and execution. He is still pondering and talking while others are out building and making money!

Over the years, I’ve learned to value execution way more than ideas. Ideas are a dime a dozen; ideas without anything behind them (even if just a blog post!) tend to be worth very little.

But it’s not just execution that matters. Another friend has been grinding away at his company for well over a decade now without any breakthrough (or, indeed, making any money). It’s not just startups that can suffer from execution without a breakthrough—Microsoft has been working on Bing for well over fifteen years now without any meaningful breakthrough in market share, either. I’ve done the same with some of my past companies—challenging work and long hours, and sometimes without financial success.

Clearly then, simply working hard is not If not just execution, what then makes the difference?

Speed and Accuracy

Before Sam Altman was famous for leading OpenAI, he led Y Combinator for many years. For those of you who don’t know, Y Combinator is a startup incubator famous for companies like Airbnb, Instacart, and Dropbox.

The clip below is worth watching—over 5,000 companies have gone through Y Combinator, so the data set is meaningful. The short version—Y Combinator found that execution cadence was the most significant predictor of success:

As Sam points out, execution speed leads to iteration speed—continuously learning what works and what doesn’t and making improvements.

All things being equal, the company that moves and learns faster will succeed over the slower-moving company.

However, speed alone is not enough. Accuracy matters—and by accuracy, I mean really understanding and solving the customer problem. There are a number of different techniques for this; my favorite is Clayton Christensen’s “Jobs to Be Done.” methodology. This approach really focuses on understanding not what features and bells and whistles a customer might like, but rather, what “job” they are trying to do.

For example, streaming music services like Pandora and Spotify have almost entirely supplanted portable music devices like iPods (indeed, the iPod is no longer even sold!). Streaming music solves the “job” of “getting me energized for my workout with music” much better than futzing around with creating playlists, uploading and syncing music from my CDs, etc.

If you execute fast but are solving the wrong problem, you end up with many of Microsoft’s new AI-driven Copilot features. Microsoft was able to ship these capabilities with astounding speed, particularly for a company of its size. Some of these features, such as Github Copilot, have proven to be incredibly useful. Others, not so much:

I don’t want to chat with Windows to make it secure from hackers; I just want it to be secure!

As Mark Benioff of Salesforce recently quipped, Copilot is the new Clippy.

Now, Mark is out pitching the new AI tools his company is selling, so while this is mainly competitive ribbing, it still struck home. Mark is wrong, though, to dismiss Microsoft. As the old saying goes, “buy version 3 of a Microsoft product”. Microsoft is now on a swift learning curve; that proverbial version three may arrive sooner than Mark thinks!

Exponential Returns

Financially, speed and accuracy can yield incredible results due to something known as multiple expansion. Mathematically, this is a consequence of the Gordon Growth Model, but the insight is very straightforward.

Let’s say you have some shares of stock from your employer. The price of that stock multiplied by the number of shares is the nominal market capitalization of the company—or, more simply, “how much it’s worth.”

The most helpful way to look at the stock price then is to think of it as if you owned the entire company. To keep the math simple, let’s say the company makes a million dollars of profit every year. Thus, if you owned the company, you would make a million dollars a year. Pretty nice!

Now, let’s say someone wanted to buy that company from you. How much would you sell it for? Clearly, you’d like more than a million dollars! After all, you’d get a million dollars just by sitting tight and going to the beach for a year. What about $10 million dollars then?

Maybe!

As you reflect on this thought experiment, consider all the factors that were flowing through your mind. Well, what if the business is shaky and might not last ten years? What if it were actually shrinking and next year would only be making half a million in profit?

In that scenario, you’d probably quickly decide to sell for $10 million!

But what if the company was growing—and growing fast! Let’s say it was doubling in profit every year! In that case, you would quickly come to the conclusion that $10 million was far too little—after all, you would earn $10 million in profit in just over three years.

Countless people, from Wall Street firms to individual stock traders, try to figure out stock prices and trends every day, so there is no way to do justice to the full topic in a short blog post. However, I don’t have to—the important takeaway for our purposes is that fast-growing companies are worth a lot more than slow-growing or shrinking companies. And due to the mathematics of compounding growth, the returns can be exponential: doubling the growth rate can more than double the stock price.

Or, more simply, the faster you can grow a company, the faster you will make even more money.

How do you grow a company faster? With greater speed and greater accuracy in addressing customer needs.

Easier Said Than Done

That is, of course, a very lovely mathematical exercise, but what does it mean in practice?

I’ve been fortunate in my career to have worked with many different companies, from the very small to the very large. Many of these companies, particularly the technology companies, give their employees stock in some form as part of their compensation. If the company is successful, the value of the employee stock would significantly exceed the employee’s cash compensation.

Yet, despite this emphasis on stock compensation, it is exceedingly rare for me to see employees and executives executing in a way consistent with making more money on their stock!

I find the disconnect here fascinating.

The default instinct I see many people have is that to double the stock price; you have to double the profit. That seems like a tall hurdle for many people. But thanks to the compounding mathematics of growth, it is actually much more manageable. For many companies, simply increasing profit by ten to twenty percent—if done quickly—could double the value of the stock.

That is a lot more achievable!

Just about every function in an organization can contribute to speed and accuracy to gain an extra ten points of growth.

1. In sales, how long is the typical sales cycle? Days? Months? Could it be done faster? What would it mean to get an initial sale within minutes? This is a deep topic worth its own post, but in brief—it is usually much easier to upsell an existing, happy customer than it is to acquire a new one. Therefore, what is the simplest and quickest offering you can provide to turn a prospect into a customer? There is a reason Costco offers free food samples! What is your equivalent?

2. In marketing, how much reach would you get with paid advertisements versus viral but free social media marketing? Do you need to have an expensive, time-consuming, and highly produced photo shoot, or would a more authentic video blog—or even AI-edited video—do just as well?

3. In engineering, what percentage of your time is spent on work that directly benefits the customer versus everything else (legacy, infrastructure, boilerplates, etc.)? In software, there are a plethora of new services, from Vercel to Fauna to Clerk, that simplify undifferentiated but necessary capabilities like authentication and database multi-tenancy. Why spend hundreds of thousands of dollars and months of engineering time on a custom multi-tenant database solution when you can buy it from Fauna for $150 / month and get it in minutes?
   
   Of course, this is a software example, but the same question applies to manufacturing and services. Do I really need a custom sales contract, or would a standardized form suffice?

4. Last but not least, in the product team, the better you understand your customers, the better your company’s offerings will be.

Of course, none of these things can guarantee success, but they can definitely increase the likelihood of success.

As the final litmus test, no matter what your job is within a company, ask the question: if you started up your own company to compete, would you do the same work in the same way? Or would you do it smarter, faster, better, and more customer-focused in some way? If the latter, why not do it that way at your current company?

If you own stock in your company, figuring that out is the quickest way to increase its value and thus make more money for yourself.

Bottom line: speed and accuracy are everything.

In the inimitable words of Ricky Bobby, who wants to go fast?

A few months ago, Sam Altman, the CEO of OpenAI, made an intriguing prediction at the JP Morgan investor’s conference: one day in the near future, a single-person company will be worth a billion dollars thanks to AI technologies.

Quite a bold prediction!

But is it really possible, even with all the amazing advances in AI technology? How would someone actually achieve this?

A counterpoint

Before diving into this thought experiment, I’ll start with a counterclaim: I don’t think it will happen!

The reason is simple. Suppose someone figures out a business that could scale to a billion-dollar valuation. As the business grows—whether it's valued at $10 million, $20 million, $50 million, or $100 million—at some point, the solo entrepreneur will likely think about hiring help. They might want assistance to handle the increased workload, take a vacation, or have a backup in case of illness. Even if a single person could manage everything initially, the prospect of hiring someone to go faster or more efficiently would be too tempting. So, while it’s theoretically possible, I think it’s impractical. Someone would have to prioritize the idea of being a single-person company over the advantages of having a team—even if a small team.

That said, it is still an intriguing prediction worthy of a thought experiment. How could we build such a company?

To start, let’s define our ground rules. What type and attributes of the company would qualify as achieving the billion-dollar, single-person company goal? For simplicity, let's include companies that use contractors and vendors. Many small companies today make extensive use of vendors and contractors such as lawyers, accountants, and so forth. A small, ten-person company does not typically need to have a full-time accountant or lawyer on staff. In today’s modern Internet-driven economy, nearly every function in a company can be available through vendors of contractors, be it lawyers, accountants, engineers, writers, marketing people, manufacturing, or salespeople.

For this thought experiment, however, let's exclude scenarios like winning the lottery, pure financial investing, or owning intellectual property worth a billion dollars, such as a unique cartoon character or comic book. Our billion-dollar company must provide a product or service more akin to a traditional company.

The math

With those ground rules, what does it mean to be a billion-dollar company?

Sam was clear that he meant a company with a billion-dollar valuation. If we look to the NASDAQ, where many technology companies are listed, we see a current average price to earnings (P/E) ratio of 32.07. Historically, the average is 28.73. To keep our match simple and clear, let’s go with a more conservative ratio of 25. There have been times in the past when the average P/E was as low as 14, after all.

Assuming a P/E of 25, a billion-dollar valuation company must generate $40 million a year in profit. Financially savvy readers will appreciate this is a vast oversimplification! I suspect that our hypothetical company, if it were ever to come about, would likely have a much higher P/E ratio. But for our thought experiment, let’s stick with the historical average.

Forty million dollars of profit is a tall challenge, but it’s even tougher when considering competition. That much profit will undoubtedly attract competition.

In years past, one of my roles at Microsoft was running the Corporate Strategy group. We had a number of different responsibilities, from M&A to five-year P&L forecasting, but at the core was this idea of trying to identify the places where Microsoft could grow. Or, more simply, where were the billion-dollar opportunities for Microsoft?

From 2022 to 2023, Microsoft's revenue grew by over $13 billion! To put that in perspective, Alaska Airlines, Airbnb, Hertz, and Jet Blue all generate roughly $10 billion of revenue apiece. That means Microsoft needs to grow the equivalent of an airline company each and every year!

A one-person company on the path to creating a billion dollars of value will attract attention not just from the major players but pretty much every mid-size company in the relevant markets as well. It might be possible for such a company to compete, but at some point, the sheer muscle of a bigger company will likely prevail. They will have more staff for sales, account management, customer support, and product development.

Thus, to avoid attracting the attention of big companies, our hypothetical solo entrepreneur should aim for multiple smaller opportunities rather than one huge one.

Portfolio of micro-businesses

If a single product would generate too much attention, what if we had four ten-million-dollar products? Or forty, one-million-dollar profit products?

Let's start with just the one million dollars of profit from a single-person company. This is tough, to be sure, but doable. For example, the top 3% of single practitioner law firms can make a million dollars a year or more. Similarly, there are numerous entrepreneurs who have managed similar levels of success—this is a fascinating interview:

While difficult, it’s clear that it’s at least possible to create a single-person business with a million dollars worth of profit.

But forty of them? Would that even be possible?

On the face of it, probably not. If you assume a standard forty hour a week work week, that would mean spending only one hour a week, or four hours a month on each business. That is an unrealistically low amount of time, particularly if the businesses were all different.

Another way to look at it is the effective profit per hour. At 50 work weeks per year, one hour a week means making $20,000 of profit every hour worked on that business if you only get four hours a month on the business. That’s a tall order.

Enter AI!

Instead of the entrepreneur working just one hour a week at each of these businesses, what if an AI could do the work? An AI employee would not need to eat, rest, or vacation. Working 24 hours by 365 days, a single AI employee would only need to make $114 of profit an hour to generate a million dollars of profit each year. And, of course, if you could have ten or even one hundred AI employees instead of one, that means an individual AI employee would only need to generate $1.14 of profit an hour.

What businesses could be run (or augmented) by an AI worker for $1.14 an hour?

That, of course, is now the billion-dollar question!

One way to approach answering this question is not to answer it!

Simply ask other businesses, “If I could build an AI employee for you that only costs $2.00 an hour, would that be useful to you?” (remember, AIs have costs of their own; by charging $2.00, we’re leaving some room for the $1.14 profit!).

There are numerous possibilities, from monitoring security video feeds to using AI agents to assist in customer support. Some of these scenarios will be covered by third-party tools—there are over 67,000 AI startups now trying to solve everything from legal AI agents to marketing AI agents.

Thus, when our entrepreneur asks prospective customers about how AI might help them, there are going to be three possible answers:

1. Not yet! The technology is just not ready yet (e.g., it’s going to need something like the Tesla Optimus Robot):

2. Problem Solved! One of the 67,000 AI companies is solving their scenario.

3. It’s close! The technology exists, but it needs to be adapted to the company’s internal processes and workflows. Now, we have a business opportunity for our entrepreneur!

More generally, broadly speaking, I think this “last mile” problem will create a lot of new value for AI-focused companies. Typing in a ChatGPT window can be brilliant for a software developer or a writer, but it’s likely not the right experience for a security guard on patrol, for example—even if the underlying AI technologies are capable enough to provide assistance for that security guard.

This is not just hypothetical pondering! A good friend of mine runs a business in the hospitality industry; it specializes in compliance with government regulations (which are complex, as regulations often are!). He asked that I keep the name confidential as they do over $3 million of revenue annually, and it’s just him and 4 AI agents!

However, the AI is “behind the scenes,” so to speak—he delivers the service that his customers value; how he does it is up to him. His secret sauce is solving that last mile—bringing the AI's capability to his customers in a way that solves the customer problem.

Clearly, then, it’s possible to build at least one highly profitable business backed by AI employees. If AI is doing most of the heavy lifting and everything else (sales, marketing, support, etc.) is outsourced, what if our entrepreneur just kept building these businesses until there was a portfolio of forty of them?

Bingo—there is your billion-dollar, single-person company.

The practical

As I argued initially in this note, the temptation to hire and grow faster will be too strong and compelling. Thus, the “single person” aspect of this thought experiment is just that—a thought experiment.

The insight from this thought experiment, however, is much more valuable I think!

Just run the thought experiment in reverse! Instead of asking how to create a billion-dollar, single-person company, ask the question posed above: “How would my business change if I had a 24x365 AI employee at $2 / hr (or less)?”. Whether you build that AI tool yourself or buy it from one of the 67,000 startups, those AI employees are coming. If you don’t hire them, maybe one of your competitors will.

Whichever company does this the best (i.e., figures out how to harness the power of AI effectively) is going to be well positioned to shift the landscape in their respective industries completely. They could rapidly either take share away from competitors or simply buy those competitors and retool them with their enhanced processes.

We have seen these kinds of changes throughout history. The Industrial Revolution displaced many traditional small craftsman-style manufacturers of the era; automobiles displaced horse-drawn carriages. Even more recently, mainframe companies were largely displaced by minicomputer companies, which, in turn, were displaced by personal computing-based companies. The cloud and Internet spawned many of today’s giants, like Amazon, Google, and Facebook. The top 3% of industrial farms now account for a staggering 44% of food production in the United States! From time to time, technological advances drive seismic shifts in the economy.

Clearly, we are in the early days of another seismic shift! Rather than be abstract and far off, make this an opportunity for today and ask the simple, practical question: Could a $2-an-hour AI help your business?

If yes, I’d love to chat and learn more!

Of course, they use technology, but it’s primarily a services business. Needless to say, he got my attention when he started talking about how much AI they are using!

One of the biggest use cases is customer service and support. They have thousands of customers and, as you might imagine, a reasonable volume of messages from those customers—everything from support questions to feedback for improvement.

In the beginning, customer feedback was processed by company employees. But as they were growing rapidly, it quickly became overwhelming. As he put it, it was not just a question of getting back to people—the real challenge (and opportunity for him) was analyzing that feedback and getting actionable insights. What were the themes for improvement or new features, services, or process improvements?

Usually, that type of work is done by a junior product manager. It’s not particularly difficult, but historically, it required a human to tease apart what different comments mean (e.g., “The food was sick!” and “The food was sickening!” are two very different kinds of comments!)

AI to the rescue. Rather than having a human analyze the data, he had an AI do it. He just gave it all of the feedback and asked the AI to summarize, categorize, and suggest improvements and actions to take. It took just a few minutes and all part of the twenty-dollar-a-month AI subscription!

As an interesting side note, he found that Claude worked better than ChatGPT. ChatGPT, at least the version he was using, was a bit too “lazy” in doing the work (and often would just summarize versus actually categorizing everything), whereas Claude was more diligent in categorizing. That is, of course, a moment in time—OpenAI, Claude, Gemini, and others are constantly improving. Getting the right balance between conciseness and accuracy versus being wordy and hallucinating has been a challenge for these AI platform vendors.

He also double-checked the AI by hand. Claude’s results were actually better than any done by an individual human.

Of course, now he is using AI to process the feedback, not hiring a human to do it like he would have in years past.

One more job lost to AI.

How many more jobs are at risk?

There has been plenty of fear mongering on this concern and quite a lot written about it. McKinsey, for instance, estimates that 30 percent of the work done today could be replaced by AI. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america

I suspect the true impact will be even higher.

For any of my readers in a corporate or government job, think about how efficient (or inefficient) your company is today—even without the impact of AI! Do you have any work colleagues where you wonder, “Just what in the heck do they actually do?”

Having worked for and in both big companies and the government over the years, I have personally seen how inefficient organizations can be. Bureaucracy begets more bureaucracy!

There are examples where companies have been able to fight encroaching bureaucracy. The changes Elon Musk made at Twitter since he acquired it are eye opening. Set aside the political and media controversy he has attracted and look at it from a business perspective. He has now shrunk the staff by some 80%, yet at least from an external perspective, the company is thriving. New features are regularly rolled out (e.g., subscriptions), and the service is still running despite many naysayers predicting a total collapse.

I wrote more about the changes at Twitter last year on ThoughtfulBits, but for this analysis, just the observation that there are inefficiencies in many organizations is sufficient.

Eventually at least one company in any industry will figure out how to apply AI technologies to remove or reduce those inefficiencies, giving them a huge competitive advantage over legacy companies that don’t improve.

Is this the end then? Will there be 30% or more unemployment in the coming years?

My personal prediction is no.

I make that prediction based on history. AI is not the first technological revolution the world has seen: farming, the industrial revolution, and the computer revolution, to name a few, have each radically transformed the jobs landscape.

In 1850, some 60% of the US population was involved in agriculture. Now that figure is 3%. Historically speaking, food is now plentiful and inexpensive. While the challenges with global poverty and hunger still exist, as a civilization, we have made tremendous improvements in food production while needing far fewer people.

What happened to all of those farming jobs? They are now computer programmers and Instagram influencers. The idea that an Instagram influencer might be a real job was inconceivable in 1850 and arguably even thirty years ago! There are now millions of people working as influencers in an industry generating over $21 billion in revenue.

The World Economic Forum has some fascinating data on this shift over time.

I expect we’ll see a similar shift as AI starts taking over whole classes of jobs, particularly more junior-level knowledge worker jobs, as McKinsey noted above.

The Experience Worker

The key question is: “What will these new jobs be?”

To answer that, it’s easiest to take a first principles approach: What stays the same in the world, even with AI?

Well, the first answer is people!! And everything people need to be happy fulfilled humans.

Even with AI, people will still need a place to live. They will still want to eat, go on dates, have families, play sports, learn, be entertained, get together with friends, and so on. These are basic human and societal needs. While the setting may be different, all those things were true in ancient Roman and Greek times, just as they are now. The Olympics started in ancient Greece, after all!

With the rise of computers, we saw the advent of the modern “knowledge worker” class—think everyone working at an office for some company (as opposed to say a factory or farm). These jobs, be they digital marketing analysts or software programmers and so on, came about because of the computer revolution.

I expect we’ll see similar “AI-focused” jobs. Indeed, even today, there is a new job category called prompt engineering. Prompt engineering is for technical people focused on adapting AI technologies to specific use cases. As a simple example, think of the questions you might ask ChatGPT—the better you phrase the question, the better the results. This is the essence of prompt engineering. However, given how fast AI is evolving, it’s unclear how permanent the prompt engineering job might be.

Similarly, there will be countless “AI consultants” in the coming years to help people and organizations shift to AI technologies, just as there were countless local “PC repair” shops in the 90s. But as people became more comfortable with computers and the computers themselves became more reliable, those PC repair shops dwindled.

Prompt engineers, AI consultants, and the like will proliferate for a time, but what jobs will be more durable and long-lived in the post-AI world?

If we go back to first principles, what is the common thread about most of those universal and timeless activities?

It’s about people interacting with other people.

If we extrapolate, just as the Industrial Revolution and the advent of industrialized farming essentially opened up the economy for whole new classes of jobs, the replacement of many knowledge workers with AI will similarly open up new opportunities.

I’ll broadly categorize these new, post-AI jobs as “experience workers”.

Some of these experience worker jobs we already know today: tour guides, coaches, teachers, chefs, scuba divemasters, etc. Consider a chef’s table at a nice restaurant where you can see the chef preparing the food before you. That’s not an experience that an AI or AI-controlled robot will replace anytime soon!

While the experience may differ in each of these jobs (cooking versus scuba diving), the root of each is a human-to-human interaction and connection. That’s the timeless essence of being human.

In some cases, we may see an increase in the number of people in an experience worker job. History, again, can provide useful insights. Industrialized agriculture has lowered the price of food over time, and that, in turn, has led to an increase in the restaurant business over the last hundred years (pretty consistently until Covid!)

What will see similar increases thanks to AI? Consider teaching. On one hand, it’s easy to think that teaching may shrink as a profession (“Oh, AI can replace the role of teachers”). Maybe for some certain aspects of teaching, but ask anybody who is trying to teach a kindergartner to write—that needs to be done in person! I actually think AI will make teachers more effective and efficient—problem sets, grading, tutoring, etc., can all be really effectively done with AI tools now, which greatly reduces much of the busywork of teaching. This will allow teachers to spend proportionally more time with students.

In essence, AI will lower the unit cost of teaching, so I personally expect we will see a lot more of it! Not just teaching reading and writing but teaching in all forms.

For example, this past winter season, I tried out Carv.ski, an AI and sensor package for snow skiing.

I had a fascinating and fun ski season using Carv! It was humbling—even though I have been skiing for over thirty years, the AI still thought my skills were, ahh, how shall we say….“amateur at best”! The Carv AI definitely helped me improve this season!

But what I really want is an in-person ski instructor who can also see the data coming from the Carv system. That would be the best of both worlds—an instructor who can see how I perform in any given snow condition, combined with the data and insights of the AI.

In essence, AI could simultaneously make it easier to be a ski instructor (thus lowering the costs) while improving outcomes. That combination can be potent. Even without AI, there are plenty of businesses, from FedEx to Shopify, that exist solely because they took something that was hard and expensive before and made it simpler and cheaper.

This short interview with the founder of Shopify is worth reading! When Shopify started, the market for e-commerce software was tiny, simply because it was so hard to do! They made it easy and now have over a million e-commerce stores on their platform.

https://x.com/StartupArchive_/status/1779477221788561734

AI tools will make it possible to make countless industries and scenarios simpler and cheaper.

Known Unknowns and Unknown Unknowns

To borrow from a famous quote from Donald Rumsfeld, the former Secretary of Defense, the really interesting question is: what are the jobs we know we don’t know about yet???!!

By definition, I do not know what those are! But I’m willing to bet the most interesting new jobs in the post-AI world will be jobs that we can’t really imagine yet, just as few people imagined the job of an Instagram influencer!

I am also willing to bet that those known unknown jobs will involve people connecting with people in some fashion, the experience worker job!

The Transition

I would be remiss not to comment on how fast the changes in the job market might happen. As I mentioned at the start of this post, we are already seeing it, but in small ways (e.g., one less job posted in a startup). What happens if the job market changes come about really quickly?

It’s one thing to say, “Oh, there will be a lot more sports instructors, so no problem!” But it’s quite a different thing when you get down to the specific people impacted. If you’ve been laid off, that’s not a theoretical economic exercise. That’s a real, live “what do I do now and how do I support my family?” exercise. If you live in Kansas, it might be hard to transition overnight from an office worker to a scuba or ski instructor or other newly invented experience worker job.

While I am optimistic that society will ultimately adapt to AI technologies just as we have every other technology revolution in history, that transition could very well be abrupt and messy.

That is a topic for another post, however!

In the meantime, if you’re working on AI, adopting AI, or are otherwise impacted by AI, stay focused on those first principles: People matter! The relationships and social interactions between people matter.

Technologies will evolve and supplement the human experience, but I don’t think it will replace it. That is the opportunity for all of us!

“These go to 11!”

One of my favorite movie quotes is “These go to 11”—for when you need that extra push. This scene is from the 1984 cult classic This is Spinal Tap, a rockumentary about a touring English rock band.

Of course, the scene is satirical and funny. Still, the underlying question is actually pretty important—if you want to improve and achieve that extra bit of performance, money, promotion, whatever, what do you do?

The idea of pushing the edge is not just theoretical. In his book Hackers and Painters, Paul Graham made an incredibly important observation on superlinear returns. The basic idea is that being the best at something yields disproportionate returns, regardless of the field.

In professional basketball, for example, the lowest-paid player, Javon Freeman-Liberty, made a salary of $289,542, while the highest-paid player, Stephen Curry, made $51,915,615. Is Stephen 179 times better than Javon? 179 times taller? 179 times faster? Clearly not.

Similarly, in the arts, the median income for writers is $6080. JK Rowling of Harry Potter fame is now a billionaire! Are the Harry Potter books really 164,000 times better written than the median book?

If you look around, you’ll see similar examples on a smaller scale. For example, Seattle has a famous shopping mall: Pikes Place Market. One of the fish vendors has a schtick of throwing fish.

Very entertaining and fun, particularly if you are hosting visitors to our city! But economically, they make over ten times the revenue of other fish merchants in the market, even though they have a relatively small shop. The book Big Little Legends has a number of fabulous examples of what happens when small businesses take that extra push to stand out in what they are doing.

Bottom line, it doesn’t really matter what field or market you are in; being the best at it can yield tremendous returns, even if best just means selling the same fish as everybody else but throwing it around instead.

Dramatic rewards can come from pushing to 11!

1440

That’s all fine and good; the practical question is how?

To be actionable, consider this in two dimensions: what you personally can do to improve yourself and increase productivity and what everyone around you is doing (e.g., the organization you are in).

Improving organizational productivity is a blog post in and of itself and perhaps a topic for another day.

For personal improvement, one of the most useful frameworks I’ve learned over the years is the Law of 1440.

I actually learned this from Bill Gates himself. As some of you may know, from 2003 to 2007, I served as Bill Gates’ Technology Advisor at Microsoft. In this role, I was essentially supposed to know everything going on both within Microsoft and in the software industry as a whole and then distill that knowledge into actionable insights for Bill.

Tremendously fun, intellectually challenging, and at least, as defined above, completely impossible!

Up to that point in my career, one of the tools I used to succeed was simply to work harder. Is a big deadline coming up? Pull an all-nighter! Extra credit project? Come in on the weekend and do it (yes, this was before remote work and ubiquitous connectivity!)

Only when I started the Technology Advisor job, it didn’t matter how many all-nighters I could pull—it simply wasn’t possible to know everything.

So I asked Bill how he did it. The answer: the Law of 1440.

There are only 1440 minutes in the day. That’s the same for me, you, Bill Gates—everyone. The question, then, is how do you spend that time?

There is no universally right or wrong answer to that question. The key, though, is to be deliberate about figuring out the answer and then holding to it. You need time for work, your health, friends, and family, and don’t forget time to relax and recharge.

Map out how you want to spend your time and then hold to it. As a public figure, Bill Gates gets his share of praise and criticism, but one thing I can attest to is that he is deliberate about how he spends his time. He was very dedicated to his children, for example. When it was time to pick them up from school he would wrap up work and go. Kids appreciate time with their parents, and have very little conceptualization of that “important” work meeting after all!

Do the same for yourself. Want to keep weekends clear for family? Do that. Want to go to the gym three times a week in the morning? Ditto. Just make a plan.

With this time allocation plan, the next step is to figure out how to use that time most effectively.

Elon Musk gave a fabulous, impromptu interview on this a few years back. It’s a short clip, but it's well worth watching.

5

As Elon describes in the video below, the five steps to improving any activity are:

1. Question the requirements

2. Remove unnecessary process steps

3. Optimize

4. Accelerate

5. Automate

Note that this advice applies to organizations as well!

As Elon emphasizes, the key is to do these steps in order. Why optimize something that you can skip doing altogether?

For questioning the requirements and deleting process steps, you’ve already done a good chunk of that if you’ve applied the Law of 1440. Invariably, you made some decisions about things you were NOT going to do and, similarly, things you wanted to prioritize (e.g., quality time with kids and family).

The optimization question is particularly fascinating with the recent breakthroughs in AI technology. As I’ve written about in earlier posts, I’ve seen tremendous productivity improvements personally by using AI technologies.

However, AI has not uniformly been a win across all aspects of my life.

AI Big Winners

For programming and technical work, AI has been quite simply transformative for me. I’ve never been this productive as an engineer in my entire career. Github CoPilot, ChatGPT, and Polyverse Boost have been my ‘go-to’ tools this year.

Neutral / Small Wins

I’ve seen smaller wins using AI tools like Dall-E and MidJourney for image generation. I’m not a particularly talented artist, and the AI image generation tools have let me be visually expressive in a way that I was able to before. I had to hire a designer on 99designs or a similar service if I needed artwork.

Now, it’s easy! Many of the images used in my thoughtfulbits.me blog, for instance, are AI-generated. These are one-off images meant to convey an idea or emotion; AI tools were perfect for it.

But I can’t say they’ve been transformative. I probably could have found a nice public domain image on Google search that would have done the job just as well.

Losers

However, this past year has been a total bust in using AI for other areas of my professional work, like managing email and managing my work as a salesperson (startup CEOs wear many hats!). At least for me, the CoPilot features in Outlook and elsewhere in Office weren’t that helpful.

As a side digression, I’m bullish that AI tools can help with either better-managing email or sales processes or both! If you have needs here as well, please reach out to me, I’d love to brainstorm how to solve this opportunity!

I do need to caveat my email comment with a plug for the Superhuman email client. For those who haven’t heard of it, it’s an email client focused on making email processing fast and efficient.

They’ve done a brilliant job of this. The most helpful feature for me is the “hold email and send it back to me later.” Nearly every day, I’ll get an email that I can’t address right away. Maybe I’m waiting for some other data, or maybe it is simply something I can decide on or act upon later (e.g., movie tickets for the weekend). By simply hitting the ‘H’ key on my keyboard, I can have that email held and delivered later, and that, in turn, lets me get through the rest of my email inbox.

It’s amazing how much psychology is at play here; hitting inbox zero (no emails in my email inbox) weirdly makes me feel like that task is done and I can move on to more useful tasks (e.g. writing this blog!). Making it easy to be productive and efficient makes someone more efficient and productive. Wins beget wins!

11

Now to the headline feature!

As I expect most of you know, earlier this year, Apple started shipping the Vision Pro augmented reality headset. This headset lets you overlay the real world with virtual objects (e.g., a board game) and use essentially your entire field of vision as a computer monitor. Imagine having a personal IMAX theater as your computer monitor—that’s what it feels like!

If you missed all of the news, this short ad from Apple shows it off pretty well:

I originally bought this solely for the larger monitor size. I love having lots of screen real estate for when I work. My office setup has four large screens attached to my desktop computer! But when I’m on the road or otherwise away, the Vision Pro has been outstanding for providing a larger screen area for work.

However, for me personally, I expect the most significant win from the VisionPro is going to come from a very unexpected source:

Many health experts recommend taking at least 10,000 steps daily to help lose weight (or maintain a healthy weight). But between family commitments and work that is primarily computer-based, that can sometimes be challenging (ok, very challenging!).

Over the years, I’ve tried to multitask while getting exercise while working, but with limited success. In the past, the best I achieved regularly was making work telephone calls while going for a walk around the local park. That’s harder to do with video conferencing like Zoom these days. Similarly, I have always struggled to read while walking on treadmills or the like. I just move too much to read comfortably.

Audiobooks and podcasts were right out. Maybe I need more discipline, but I can easily be distracted while listening to a podcast. When that happens, I lose the discussion thread and need to pause and rewind. It's doable, of course, but not enjoyable.

I had pretty much given up on finding a good way to get exercise while working.

Enter the Vision Pro.

Because the display is literally on my head right in front of my eyes, it doesn’t matter as much if I’m moving around. It quickly dawned on me that I could use the Vision Pro for work while working out!

So, I pulled a spare Bluetooth keyboard out of my bin of leftover electronics and went to the gym with my Apple Vision Pro to experiment.

Within minutes, I figured out that stairmasters and treadmills were right out. They involve way too much movement to be able to read effectively, much less type.

But the stationary bike worked! I’m moving around, of course, but not as much and well within my tolerance for using the Vision Pro.

Now, every day, I’m able to do more computer-intensive work, like coding or writing, while on a stationary bike.

It’s still too new to quantify exactly how effective this setup will be, but if it helps me get to “11” on my personal health and exercise goals, I calculate I could lose an additional 11 pounds over the the six months or so (or roughly half a pound a week).

We shall see!

Of course, improving one’s productivity is a perpetual, never ending goal. There are always new things to learn, and new technologies to try. Please share your own tips and tricks in the comment section!

It had a whopping 2 KB of RAM and a tape cassette for the hard drive! My first program was "Hello World!" of course, followed shortly after that by my first video game: a dot that you could move and shoot other dots!

10 PRINT "HELLO WORLD!"
20 GOTO 10

In the years that followed, we saw an incredible rise in computing capabilities. Nearly every year brought some new marvel: the first color computer, the first printers, the first laser printer, and the first video and sound cards. Successful generations of chips, from the 286 to 386 to 486 to Pentium yielded faster and faster computers. These advances felt real and tangible to users. The latest and greatest technology often opened up new scenarios that were impossible before, be it printing a color photo or playing a 3D-rendered video game like Doom.

By 2010, though, this race of ever more amazing client-side computing experiences felt like it had come to an end. While the underlying chip and hardware technology continued to advance, it didn't "feel" as spectacular as it used to. Just ask yourself: for day-to-day computing tasks--browsing the web, doing email, writing a paper--how different does your PC feel today versus, say, five years ago or even ten years ago (setting aside AI! I'll come back to that!) 

To be fair, there are certainly more specialized scenarios like computer programming or video editing where your PC's capabilities make a huge difference! The new M-series chips powering Apple's new Macbooks and iMacs are phenomenal at tasks like video editing. Editing a long, 4k video on a Macbook M3 is smooth and silky--something completely unthinkable even a few years ago on an Intel-based laptop. 

But for most of us, the razzle-dazzle of new hardware enabling dramatically new experiences ended a decade or more ago. 

This is about to change and change in a big way soon. AI is the driving force. 

Fundamentally, AI is about computing power. Add more computing power, the AI gets better and smarter. A smarter AI will write better code, suggest better text for emails and presentations, and will create more realistic pictures.

Of course, at any given moment in time, there will be a lot of innovation and differences between specific AI algorithms and technologies, and many people are working on improving the performance of AI tools. At Polyverse, the upcoming release of our Boost AI tool for software developers is literally 100 times faster than the version we had last year, all thanks to algorithm and architecture improvements.

But even with those kinds of improvements, the fundamental principle still holds--at the macro level, the more computing power we put into AI, the more we'll get out of it.

This principle will be particularly true in the coming era of agent-based AI. Right now, AI technologies are used primarily in a reactive sense. ChatGPT is a great example—it's brilliant, but it only responds when you ask it a question. Credit card fraud algorithms run when you attempt a transaction. Email anti-spam filters run when a new email comes in.

In the upcoming agent model, an AI will constantly run in the background, proactively working on your behalf. It will continuously read your emails and work 'todo' lists, and then it will start on all those work items. When you get into work, the AI has already done a big chunk of your tasks. Similarly, an AI will constantly scour the Internet looking for things it knows will interest you, be it a news article or an upcoming rock concert.

These agent scenarios will be transformative but will need even more computing power!

If we need more computing power to run an AI (or, more likely, multiple AI systems) 24/7 for each of us, where will all this computing power come from? 

The simple approach would be to build more and more data centers.  

This data center build-out is already happening. Microsoft, Google, Amazon, and others are investing billions and billions to do so.

However, I predict the world will want more.

If you get increasing returns with more computing power, there will be a push to do more and more computing. It's basic economics.

Unfortunately, there will be a limit to how rapidly new data centers can be built. While there are many complexities, the fundamental issue right now is insufficient electrical power! This is driving leading innovators to invest in fusion power and other breakthrough power sources. This article is worth a read: https://futurism.com/sam-altman-energy-breakthrough.

A large-scale data center can consume hundreds of megawatts of power, and much of the power generation capability in the world is already in use or allocated. Thus, building new data centers to meet the demand will also necessitate building new power plants.  

It's going to happen, but it's going to take time. Power plants take more time to construct than data centers and have significantly more regulatory overhead. 

Thus, if there is a constraint to new power generation and, therefore, new data center construction, how will the world meet the demand for AI computation?

My bet: client side computing power (or edge and device computing power) if you prefer. 

For example, the latest Apple computers with Apple CPUs are exceptionally good at running AI jobs. Just watch this video of an AI running entirely on a Mac laptop to generate code. It's basically ChatGPT "lite,” running on a laptop!

Client-side AI has several advantages.  

1. You already have a client-side device--actually, many client-side devices: your laptop, phone, watch, and even your TV! 

2. There is already power "allocated" for these devices--no new power plants are needed.

3. AI running locally can safely process confidential corporate data.

Will the client-side AI become as powerful as the exascale cloud AIs from OpenAI and others? 

Probably not. But it doesn't matter!  

It will not become an "either-or" choice of client or cloud. One of the cool things about AI technology is that you can essentially "mix and match" and combine different techniques together (indeed, this is how ChatGPT itself works on the inside). Local, client-side AIs can be combined in a hybrid fashion with the more sophisticated cloud AIs.  

Consider, for example, AI-powered email software in the near future. The email client running on your laptop might use the "cheap" AI to weed out spam emails, summarize newsletters and marketing emails, and then rely on the premium, cloud-based AI to help you compose a new email to your boss. It's a hybrid solution, taking advantage of all the processing power available, both cloud and local.

Think of this like running a restaurant. Yes, you could hire Gordon Ramsay to do *everything* in your restaurant, from grocery shopping to chopping onions to making the meals, running the cash register, and washing the dishes. But Gordon Ramsay is famous--and expensive! A restaurant would go broke if the executive chef did all that work.

The premium, exascale AIs such as OpenAI are like Gordon Ramsay. Top-notch and constantly getting better, but with a price to match. Just as a restaurant would hire other, more junior staff to buy the food, prep the onions, clean the dishes, and so forth, the same is going to be true for AI solutions in the future. AI solutions will increasingly be hybrid solutions using the available computing power as cost-effectively as possible, both client-side and in the cloud.

Where this gets exciting is what might happen with devices going forward. I think we will see a resurgence of innovation in devices for your home and office.

The obvious example is the laptop or desktop you are using day to day. Intel will, one way or the other, catch up to Apple's silicon, but regardless, there will soon be a day when you are eager to upgrade your PC to get the email AI working faster and more accurately through your massive email backlog. 

But it's not going to stop there. When was the last time you bought a TV? Why did you buy it? Maybe you bought a 4k TV a few years ago to watch the Super Bowl. But if you have a decent flat screen today, what would compel you to get a new one? Sharper colors with an OLED vs your current TV. Eh...maybe, but <yawn> probably not.  

What if a new TV had a built-in AI assistant running locally (so no expensive subscriptions, or at worst, a relatively cheap subscription)? With an actual screen, this AI assistant could be seen and interact with you--you could have a 3d rendered "AI person" talking to you, not just the disembodied voice of today's Alexa devices.

Of course, there are more AI scenarios than just assistants! I would love to have an AI picture frame on my wall that could show photos of my kids and generate new AI artwork. The exercise equipment at my gym could have an AI screen showing me a real-time generated video of how I could improve my form or otherwise do better. AI in stores and offices could help monitor security cameras or suggest when to send staff to help shoppers who need help. 

It is not just home scenarios where we will see a resurgence in client-side computing. I suspect we will also see a comeback of the corporate data center. While there are substantial advantages to having a lot of corporate computing in the cloud, that cost equation shifts for heavy and predictable workloads. The team at Basecamp wrote an excellent series of articles going over the cost savings--and this is without AI features driving demand. They saved millions of dollars a year by moving off of Amazon. 

As AI technologies improve and deliver more and more value to companies, I expect many companies will look at the benefits and ask the question: what if we had AI agents running 24/7 for all aspects of our business--marketing, back office, customer support, etc? 

For many industries, AI is going to reshape the entire way companies interact with their customers. We are starting to see this already, as B2B software companies are beginning to add AI-driven "upsell" features (e.g., "We noticed you are doing a lot of X--if you buy the special X-add-on-pack to Office 9000, we can help you with that").  

I expect most of these corporate scenarios to be solved with hybrid AI architectures--taking advantage of the latest and best exascale AI with the bulk lifting done by cheap, on-premise AIs. Hybrid architectures will be particularly important when the AI needs to access and interact with sensitive corporate data, such as customer data or proprietary intellectual property.

While all of these scenarios from the home to the office could be done entirely with cloud-side AI (after all, the cloud is just computers owned by somebody else!), I think the benefits of AI are going to drive the push for more computing power and cheaper. The cheaper I can run AI, the more value I can get from it. I am willing to pay $100 for my AI photo frame, but I'm definitely not interested in paying $1000. Similarly, if I can use AI for an upsell marketing campaign, it will have to be done within a specific marketing budget.

In all of these cases, the demand will be pushing hybrid architectures and, with that, client-side computing.

As computing hardware continues its relentless advance (e.g., the newest Mac M3 chips are over twice as fast as the original M1 chips in only three years), there will be renewed value and ROI for being on faster upgrade cycles.

The only question left is which hardware companies will deliver these breakthrough scenarios. Dell? Vizio? Samsung? Apple? 

In the meantime, it's time to save up for a new TV!

Share ThoughtfulBits: Ideas that Matter

By now, many of you have seen the news about the board of OpenAI firing CEO Sam Altman: https://www.geekwire.com/2023/sam-altman-leaving-openai-after-board-says-he-was-not-consistently-candid-in-his-communications/ and exiting him from the company immediately.

Needless to say, this was a shock to many (including Sam!), coming so soon after an incredibly successful series of product launches from OpenAI.

As of this writing, there are no definitive accounts of what happened behind closed doors. By the standards of normal CEO transitions, it's rare to see such a strong personal attack on the outgoing CEO. What does seem to be clear is that the board's actions were done hastily, and key partners and investors like Microsoft were informed only minutes before the announcement.

As one might expect, social media is currently abuzz with speculation. The most likely theories revolve around an internal coup led by one of the other OpenAI cofounders (Ilya Sutskever) (https://x.com/karaswisher/status/1725702501435941294?s=20). More imaginative and fanciful ideas suggest that Sam had discovered the OpenAI's AI had become self-aware, and the AI had to act...

I will not add to the speculation here--hopefully, a more complete story will emerge in time. Boards must hire and fire CEOs, and company leadership changes are part of the ordinary course of business. The interim CEO, Mira Murati, is exceptionally talented and may prove to be an even better CEO than Sam. 

What matters is how boards handle leadership changes. OpenAI is now a masterclass example of how NOT to make the change. By acting precipitously and without an orderly and planned transition, the OpenAI board has overnight shifted the company from the leading AI company to the leading AI technology company with unstable and inexperienced board leadership. If the rumors are true about the change being driven primarily by internal politics, that is even worse for the board. Experienced board members would have seen right through such machinations and kept a steady hand on the tiller. Even if a CEO change was warranted, it could have been done more orderly and thoughtfully.

If I were still at Microsoft, I'd be furious. Microsoft has invested billions in OpenAI, and more importantly, OpenAI technologies are at the heart of a remarkably recharged and innovative Microsoft. Just this past week, Microsoft held their major 'Ignite' conference, showcasing over a hundred new advances in their adoption of AI. 

It's too much to say that Microsoft bet the farm on OpenAI, but it is safe to say they've made a substantial strategic bet on the technology--and so far, without any publicly announced internal replacements. As I wrote about almost a year ago, this puts Microsoft strategically dependent on OpenAI: https://www.thoughtfulbits.me/p/microsoft-will-have-to-buy-openai

Now, Microsoft has a dilemma. They can continue the bet on OpenAI "as-is". After all, yesterday's announcement was just a staffing change, and nothing changed about the technology overnight. 

However, in doing that, they risk betting on a sinking ship. What happens when Sam (and the other cofounder who left with Sam, Greg Brockman) starts a competing AI company? Already, several other high-profile OpenAI technical talent have resigned: https://x.com/hellokillian/status/1725797467315486902?s=20

If you look at the pace of progress in AI from both open source and other companies (like X.ai), it's clear that while OpenAI is leading at the moment, it's not the only game in town. With time, money, and, importantly, technical talent, another project could catch up and surpass OpenAI. 

If enough of the current OpenAI talent jump ship and rally behind a new Sam/Greg AI company, what happens to OpenAI then and the bet that Microsoft placed? 

Yikes...

Clearly, Microsoft will need to act. The simplest (and thus arguably most likely) scenario would be for Microsoft to guarantee another multi-billion dollar investment, but with the following conditions: 

1. The current board is replaced entirely (and Microsoft now gets a seat on the board)

2. Sam and Greg are brought back, at least for a period of time.

Of course, there are many variations of this approach (including just an outright acquisition of OpenAI). However, any solution must address a key principle: restoring confidence in OpenAI's leadership and future direction. At this point, I do not know how that could be done with the current board. Ironically, in firing Sam, the current board has likely signed their own death warrants in that they will, in turn, get fired.

Microsoft needs to act fast, though. Sam and Greg are 100%-for-sure fielding calls now from big-money investors and other AI contenders. If it weren't for the bad blood between Sam and Elon Musk, having Elon Musk's X.ai company acquire OpenAI would be an intriguing choice—ditto for Google, who has struggled to catch up despite actually inventing the key technologies behind OpenAI.

Stay tuned, everyone! The coming days and weeks will be interesting. 

Setting aside the people drama of the moment, the real news happened last week with the launch of Custom GPTs: https://openai.com/blog/introducing-gpts. We just saw the "iPhone AppStore" moment in the journey of AI progress, and I predict this advance will impact nearly every business on the planet.

More on this in the next post!

There is no escaping the hype and excitement around AI.  Every day is filled with often breathless news about the latest advances.  

But we’re also starting to see an increasing number of naysayers: many people from Gartner to Charlie Munger (Warren Buffet’s business partner) are saying that AI is overhyped.

The history of technology is filled with overhyped technologies that often fail to live up to the original promises (at least so far!): Bitcoin/blockchain, virtual and augmented reality (Microsoft Hololens anyone?), and perhaps most interestingly, AI itself.  This is not the first time we’ve gotten into a hype cycle around AI–that happens pretty regularly every twenty years or so, starting back in the 1950s with the invention of the first programs that could play checkers and prove mathematical formulas: https://www.kdnuggets.com/2018/02/birth-ai-first-hype-cycle.html

On the flip side, from time to time, there are hyped technologies that end up changing the world–the personal computer, the Internet, and mobile phones, just to name a few.

So which is it for AI (this time!)? 

The Trough of Disillusionment

To set some context, nearly every popular technology goes through an initial period of euphoria and enthusiasm. Gartner popularized this phenomenon in their “Hype Cycle” model: The basic idea is simple–new technologies go through an initial phase of excitement and hype around the world-changing implications, but invariably, reality sets in:  “Hey, I thought we were supposed to have flying cars by now!”. 

Welcome to the trough of disillusionment. Few technologies immediately live up to the hype, and often, they take longer to really realize the value and original promise.

There are two fundamental drivers behind the hype cycle: why it takes longer for the promised benefits of a technology to materialize:

1. Humans don’t change as fast!  The most successful and rapidly adopted technologies are ones that meld with existing tools, processes, and behaviors.  Email, for example, was a faster version of the letters (and faxes) that people were already sending.
   
   Many blockchain applications, on the other hand, have failed at that melding. An often-cited scenario promised for blockchain is title insurance for home buying. 
   
   If all property titles were stored in a publicly visible and auditable blockchain, there would be no need for title insurance (or at least, the expensive title insurance process as we know it today!)  But even though blockchain has been around for over 15 years now, and this scenario has been dreamed about since then, it still hasn’t happened.
   
   Quite simply, there are just too many existing moving parts and human processes involved with homebuying: you’d have to get local regulations to change, you’d have to convince homebuyers this new process was good, you’d have to move the existing property records into the new blockchain, and of course, you’d have to make the process of using and modifying blockchain tremendously simpler than it is today (the user experience for many blockchain applications is terrible, even today in 2023). 
   
   AI technologies will face similar challenges. Just because you can put a chatbot UI onto something doesn’t mean it’s the right experience to meet users where they are.
   
   There will be a lot of experimentation and, by definition, failed experiments as technology vendors figure this out. We’re seeing this already in a few places–the first iteration of Microsoft’s Windows CoPilot, for instance, seems to have missed the mark: https://www.pcworld.com/article/1974843/windows-copilot-cant-decide-what-it-is.html  Is a chat window really the right way to interact with the operating system? I’d argue the more an AI can just solve problems on its own, the better, at least at the operating system level! The OS should just work.
   
   For AI, I’ve called this tension between a technology and the way humans work with that technology the “AI Gap.” AI technologies are racing ahead furiously, but humans are still humans. Winning AI products will bring AI seamlessly into existing processes and tools in a way that feels natural and comfortable to users. See my earlier note for more on this! https://www.thoughtfulbits.me/p/the-ai-gap
   

2. The technology simply isn’t ready.  Tablet-style PCs have been a dream of technologists ever since the original Star Trek series in the late 1960’s.  There were numerous attempts to build such a device, such as the Apple Newton in 1993. I personally worked on Microsoft’s Tablet PC in the early 2000’s. 
   

   
   To put it simply, in all of those previous attempts, the technology for tablets simply wasn’t ready. It wasn’t until Apple’s iPad that the combination of software and hardware progress was enough to make it a compelling device.
   
   In the past few years, we saw the same challenge with augmented and virtual reality devices. Who wants to wear a bulky headset?  Maybe–just maybe–the new Apple VisionPro headset will be advanced enough to make the breakthrough (I’m hopeful!), but that’s a topic for a different post!

   

   
   With respect to the new generative AI technologies, there is no doubt the technology is stunningly impressive. But, it also still suffers from the hallucination problem (i.e. the AI will generate ‘made up’ answers).  Unless products are designed with hallucination in mind, they could readily fall flat with end users. 
   

What about AI now?

While AI broadly defined has been widely adopted in recent years in scenarios as diverse as Siri voice recognition, Tesla Autopilot, and credit card fraud detection schemes, ChatGPT (and more broadly generative AI) has captured the world’s imagination since its introduction–still less than a year ago!

Overhyped, or not?

My own view:  not.

Of course, there will be articles written that are clearly overhyping the technology and downplaying the risks.  But broadly, I think we (collectively) are underestimating the impact.  

It’s Amara’s Law: humans tend to overestimate the impact of new technology in the short run and underestimate it in the long run.

We are all familiar with Moore’s Law: the idea that computers double in processing capability every two years (and to be fair, the original formulation of Moore’s Law was about transistor count, but this is a reasonable approximation).  As a result of Moore’s Law, the Oura fitness and health ring that I wear has more processing power than one of the original Cray supercomputers, costing over $30 million dollars in today’s dollars!

That is the result of doubling every two years.

However, the underlying models behind AI are improving at a rate of 750x every two years. https://medium.com/riselab/ai-and-memory-wall-2cb4265cb0b8

750x. 🤯

It’s truly outstanding. Of course, technical readers may debate the exact math behind the 750x calculation, but it doesn’t change what’s going on. 

Just look at the pace of progress with ChatGPT–at first, it could just write back text. That alone was stunning.

Then, this summer, it could automatically do mathematical analysis and execute code. Now, it can recognize and manipulate images. 

All of this in less than a year! If you haven’t seen some of the new ChatGPT vision features, this post is well worth the read: https://openai.com/blog/chatgpt-can-now-see-hear-and-speak

At some point, we’ll see a slowdown, but at least in the short run, I expect the pace of progress to continue. Fundamentally, AI quality is driven by “more data and more compute.”  Thanks to the investments being made in AI ($13 billion from Microsoft!), AI is getting both more computing and more data!  

But one could have looked at an investment chart for Bitcoin and blockchain technologies in recent years and seen something similar. For example, the energy consumption of Bitcoin skyrocketed in 2021 and 2022.   Just like AI, there was a ton of investment in Bitcoin technologies a few years ago.

However, the crucial difference is the impact on day-to-day life and work. I still program professionally for my day job. My productivity has gone through the roof with AI tools–I simply can’t imagine writing software without AI now; it would be too painful! 

Similarly, I’m now making social media videos: https://www.tiktok.com/@boostedcoder. I use a series of AI tools (e.g., auto-captions) to make the process quick and painless.  Again–without AI, I would not have the time to do all of this.

I am sure each of you has a story of how you might have used ChatGPT yourself or know of friends who have. It’s been pervasive--everything from helping kids with their homework to salespeople selling to programmers writing code.

That’s just with the tools we have now. Nearly every software company I know is working through how to incorporate AI into their products. While not every one of these attempts will be a knockout success, what happens in 2024 when every piece of software you are using becomes dramatically more capable thanks to AI? 

Bottom line: AI is underhyped.  Buckle up!

My hoped-for answer: making a secure version of Windows!

It’s no secret that Microsoft is significantly lagging in the actual security of their software products, despite bragging about making more than $20 billion of revenue on cybersecurity solutions. Many reports put Microsoft software as more vulnerable when compared to Apple’s Mac and iPhone product lines. 

When was the last time that you heard of a hack against Microsoft Windows? Pretty much daily. Now what about the iPhone? (Ironically, there was one earlier this month, but it’s far less prevalent).  

It does seem strange that Microsoft would brag about the size of its cybersecurity business—particularly when you think about how big that business would be if Microsoft’s software offered the same level of security robustness as Apple’s. Would they actually make less money? To paraphrase an old comedy sketch by Monty Python: “That’s a nice copy of Windows you’ve got there. Would be a shame if anything happened to it…”. 

On the flip side, Microsoft has been truly impressive regarding the speed and comprehensiveness of their adoption of Artificial Intelligence (AI) technologies across their entire product line (most recently with Windows CoPilot)! This is refreshing to see, and it does suggest that if Microsoft were willing and put the same level of focus on cybersecurity, they could single-handedly eliminate a huge portion of cybersecurity issues that plague the world today. 

AI and Cybersecurity

The time for Microsoft to act is now.

Left unchecked, AI technologies are going to vastly exacerbate the cybersecurity problem—cyberattacks will increase in frequency and severity. For all of my readers, however good you think your cybersecurity is in your company now, it’s not good enough for the coming years of AI-powered cyberattacks. 

The rationale is simple: just as AI has shown dramatic improvements in the productivity of software engineers, those same productivity gains can be helpful to cyber attackers. In many ways, the productivity of hackers has increased even more than the productivity of legitimate engineers. The hacker only has to be right once, the defender can’t ever be wrong. The economics greatly favor attackers.

Traditionally, one of the most expensive and time-consuming parts of constructing a cyberattack has been analyzing the victim’s software (such as Windows) to find an exploitable weakness. But what used to be complex is now simple. That analysis can be done at scale and extremely quickly by using AI engines to analyze software.

It’s stunning how good AI is at finding cybersecurity issues, and many software developer tools are rapidly adopting AI (such as the tools made by my company). When used legitimately, these tools are fantastic for improving software and making it much more robust and resilient. The problem is that the same advances in AI that enable such tools can also be abused by nefarious actors. What happens when dark web AI tools make it easier and cheaper to create exploits than ever before? 

If defenses don’t improve at the same pace or faster, it’s easy to predict that cyberattacks will increase in volume and effectiveness—and indeed, we are already seeing this happen.  

There's no fate but what we make

If the cybersecurity challenge is just going to become worse, what can we (or Microsoft in this case) do about it and do quickly? Are we doomed, or can this be solved? I think it can be solved!

One path would be for Microsoft to license various cybersecurity technologies from Apple.  While that would make headlines, I’m not sure, culturally, that Microsoft would be willing to do this.

Fortunately, they don’t have to. 

Microsoft is huge, and across the many different divisions, they already have in-house all the necessary technologies to match Apple’s level of cybersecurity!

Similarly, the technical teams at Microsoft are truly world-class. As I mentioned earlier, just look at what they are accomplishing with AI. With the right leadership and determination to make it happen, they can solve the cybersecurity problem. The raw ingredients are already there.

Could Microsoft eliminate 100% of all cybersecurity issues? Of course not. But could Microsoft bring the Windows ecosystem to the level of safety and security the iPhone ecosystem currently offers? Absolutely. 

Ransomware 

Cybersecurity is a big and complex topic. To simplify the discussion for this note, let’s consider one huge and pernicious type of attack: ransomware. You’ve probably seen articles like this one: https://www.politico.com/news/2022/12/28/cyberattacks-u-s-hospitals-00075638 where ransomware attacks on hospitals literally killed people due to the disruption to the hospital’s operations. Ransomware is a big business, costing an estimated $20 billion in 2021.

For those of you who don’t spend every day thinking about cybersecurity, a quick refresher:

Ransomware is malicious software that can block access to people’s computer systems and the data stored on them—typically by encrypting the files on the computer and showing a message of “pay $X thousand or million dollars to unlock your computer.” Computers can be infected through many means, but a very common one is a phishing email—those fake emails everyone gets. Sometimes those emails contain attachments and links that, when clicked, can infect the computer with ransomware malware. Once a computer is infected with ransomware, the hackers take control, deliver the ransom message, and await payment. 

The Solution

If you abstract away these steps in how ransomware works, there are fundamentally three dimensions to the attack: 

1. People: Somebody has their email faked out, and that tricks the victim into clicking on an attachment.

2. Software: The actual virus or malware behind the ransomware can corrupt and take over the victim’s computer.

3. Data: The victim’s data becomes locked and unavailable.

To truly solve the ransomware problem, we need to address all three attack vectors.

People

As mentioned above, one common delivery vector for ransomware is phishing emails. People get enormous amounts of spam emails and countless unsolicited sales emails. But some of those emails are extremely dangerous—the links or attachments in the email can actually be viruses directly or link to malevolent websites or downloads.

The basic challenge with email is that it’s very easy (and cheap!) for attackers to send fake emails. But people don’t know that. Just because you get an email from ‘alex@polyverse.com’, it does not mean that the email legitimately came from me! It’s a basic identity problem that needs a solution. 

There are existing technologies today that can digitally sign and digitally encrypt emails. With a signed email, you can be confident that an email really did come from whoever signed it.

But there lies the rub with existing solutions. Just because an email is signed, with many existing solutions, you do not know who actually signed the email. Was it the ‘real’ alex@polyverse.com, or someone pretending to be alex@polyverse.com (or even alex@fakepolyverse.com)? 

From a technical perspective, this is known as a “key management” problem. Digital signatures require mathematical numbers known as “keys,” and whoever controls the keys can control who can sign or encrypt a message. It’s much like a car key—if I have your car keys, I can drive your car. 

However, let’s say I came up to you at a mall and gave you a random car key. You might know it’s a car key from the shape, but how would you know which car the key went to in a parking lot of thousands of cars? That problem bedevils existing attempts to sign emails. Just because an email is signed, do you know who really signed the email? It’s the car key problem—you know it’s a car key; you just don’t know which car. Similarly, just because it’s signed, you don’t know for sure who really signed it.

To solve that problem, we need a global, trusted directory of identity. If an email is signed by “alex@polyverse.com,” could you verify that the person signing it was the same person who worked at Microsoft, AOL, and Polyverse? What if each of those companies, in turn, could verify that, yes indeed, Alex did work there? By linking each of these verifications together, you can create a chain of trust, or more specifically, a web of trust, to verify digital identities. (As a side note, one of the many potential uses of Blockchain and Web3 technologies is to help with digital identities, but that’s a story for another post!)

Fortunately, this global, trusted directory of identity exists! It’s called LinkedIn, owned by none other than Microsoft.

Historically, one challenge with LinkedIn has been fake profiles. However, LinkedIn has recently launched profile verification, which allows for profiles to be validated by employers and via government IDs. With this verification feature, we now have a trusted source of identity (or at least reasonably well-trusted!).

Now suppose Microsoft integrated key management between LinkedIn and Outlook. Now for every email in your inbox, you could know with confidence exactly who sent it—you could see their LinkedIn profile and see that their employers (and governments) vouched for them.

No more fake emails.

Well, we will still probably get a lot of sales and marketing emails, but importantly, those emails could now be tracked back to a verified and authentic human. This would make a huge improvement in the email experience, let alone the positive impact it would have on reducing cyberattacks!

Software

Simply improving email, however, is not enough. There are other vectors for ransomware to be delivered through (e.g., websites), and we’ll still need a way to handle legacy emails that have not been sent through this new, secure Outlook/LinkedIn solution. So now, while we’ve made part of the cyberattack more difficult, we need to turn our attention to the next dimension: software. 

What if we could make software extremely resistant to cyberattacks? As Apple has shown, this is entirely possible. But even more can be done.

Galen Hunt, a Microsoft Distinguished Engineer, wrote a fabulous whitepaper on how to create a secure operating system: https://www.microsoft.com/en-us/research/publication/seven-properties-2nd-edition/. The original paper was published in 2017—over six years ago now! I highly encourage my technical audience to read this whitepaper; it lays out a clear set of principles and technologies needed to produce truly secure software. 

(excerpt from Galen Hunt’s security paper)

This paper demonstrates that Microsoft knows how to build a secure operating system, so why haven’t they?

One big challenge is balancing security with compatibility. The issue: “Sure, it would be nice to have a secure operating system, but we also want that operating system to run all of the games, enterprise apps, and devices that we already have. We don’t want to upgrade Windows and have everything break!”

The solution here is another Microsoft technology invented in 2011, known (earlier) as Drawbridge. Drawbridge is essentially a lightweight virtualization scheme that can run one operating system on top of another.  This was used very successfully in bringing Microsoft’s SQL Server database product to Linux—that Linux version essentially has a copy of Windows and the Windows-based SQL Server product all combined into one system running on top of Linux.

This same approach could be used to achieve both compatibility and security in a future version of Windows, let’s call it Windows 12. The ‘aha’ is to treat all older versions of Windows like a virus!

Fixing the existing Windows and preserving compatibility would be an extraordinary challenge. So don’t!

Instead, literally, take the entire operating system and compartmentalize it using a drawbridge-like approach. The real, underlying operating system would be built using the principles in Galen Hunt’s paper (the Azure Sphere product could very well be the starting point for this effort). 

These aren’t new ideas—they have been around and maturing for over a decade within Microsoft—just in different divisions than the Windows division.

Data

Last but not least is the issue of protecting the data itself. Even with a super secure version of Windows and a high-integrity email system, it could still be possible for malware to cause havoc. Thus, to provide enough defense in depth, we need to also protect the data in the computer—the documents, emails, and other information that we all use daily. 

There are two key approaches: isolation and versioning.

• Isolation - One of the strengths of the iPhone architecture is the isolation between apps. Indeed, this isolation philosophy was so strong that early versions of the iPhone even lacked copy/paste functionality between applications! While we have copy/paste on the iPhone today, apps on the phone are strongly partitioned from one another. The software execution is partitioned, and the data is partitioned. 

With software, the partitioning works much like the approach for compartmentalizing software execution I outlined above for Windows 12. For data, each application should have its own partitioned area for data storage. The video game you download does not have access to the banking data from your Bank of America app (and vice versa!). This isolation is a crucial layer in a secure system. In Windows today, there is very little isolation. A video game I download from the Internet can have full access to my Quickbooks data. That full access is what enables ransomware to cause havoc at hospitals and elsewhere. Ransomware hiding in an otherwise “harmless” video game goes through and encrypts and locks up all of the data on your computer. If the video game only had access to video game files and not everything else, this would be much harder to do.

• Versioning - Last but not least, let’s assume that an attacker can get through all of the defenses we’ve outlined thus far. The final defense is the “go-back” button. What if you could hit this button and instantly rewind your computer to where it was a day ago, a week ago, or whatever? Not only would this be useful to undo those “oops” moments, but it could also let you undo the introduction of ransomware. Simply rewind your system to where it was before things started going badly.  To be sure, you might lose a few days' worth of work, but that’s far better than losing everything. 

Apple has long had this capability—the “Time Machine” feature in MacOS. But Microsoft has something similar in their GitHub division.

The core offering of GitHub is a version-controlled system for maintaining and building software code. Versioning is an invaluable part of the day-to-day life of a software developer—as we build new code, sometimes we make mistakes. With version control, we can go back and unwind those mistakes. Microsoft should integrate this GitHub versioning technology into the core Windows filesystem, essentially creating a “go back” or rewind button. Not only would this be a useful feature for users in general, but it would create a final safety net to defend against the nefarious deeds of hackers. 

The future is bright!

Hopefully, the comments above paint a clear path for how Microsoft could dramatically improve its software and significantly reduce the number of cyberattacks in the world. And, as the core technologies needed are already mature and already owned by Microsoft, it’s entirely possible to do so.

With that said, I do want to be clear that I’m not saying this would be an easy task, far from it! As a former Microsoft employee (and former engineer in the Windows team), I am acutely aware of the challenges and difficulties in building extremely large and complex software like Microsoft Windows. 

At the same time, Microsoft is demonstrating remarkable agility and speed with AI advances that have surprised and delighted the world. It really is quite astonishing, and I am proud of my friends and colleagues there for the work that they are doing in this space. 

There is so much more that can be done here in integrating AI technologies deeply into the operating system. Doing so would fundamentally transform and reinvigorate the core operating system space. We are rapidly closing in on the “Jarvis” scenario from the Iron Man movies, where the computer becomes much more an extension of our brain and imagination versus a tool that we often struggle with!

All of that progress will be for naught though if Windows keeps getting hacked! Thus, the immediate challenge now to the new Windows leadership team is to take that same level of agility and innovation done for AI and apply it to solving cybersecurity.

The world will thank you for it!

Is this concern justified, or is it just overhyped paranoia? Doomsday scenarios drive a lot of page views and sell a lot of movie tickets, after all!

I'd argue both--and more boldly and specifically, I predict that within two years, we will see the first instance of an AI-enhanced weapon making a "kill" decision autonomously.

This is a dramatic prediction, to be sure, but the forces pushing us in this direction are already happening--namely, the war in Ukraine to repel the Russian invasion.

A recent edition of the Economist magazine has a thoughtful and in-depth series of articles about the ongoing war. This article, in particular, is worth reading: https://www.economist.com/special-report/2023/07/03/the-latest-in-the-battle-of-jamming-with-electronic-beams. For those of you who are not Economist subscribers, here is the summary.

Amongst the weaponry supplied to Ukraine was an artillery system called Excalibur. Excalibur is a system of precision, GPS-guided artillery shells that vastly increase fire accuracy. This increased accuracy, in turn, makes a dramatic difference in destroying enemy tanks and other fortifications. Excaliber and similar high-tech Western military gear are credited with significantly helping the Ukrainians defend their country.

However, by March of 2023, the Ukrainians found that the accuracy of Excalibur shells started dropping dramatically--and it wasn't just these artillery shells that were failing. Drones and other weaponry relying on GPS were similarly having trouble. The culprit was GPS jamming. The Russians had deployed electronic warfare systems to the front, including GPS jammers.

Of course, introducing GPS jammers invited a counter-response by Ukraine and its Western supporters. GPS jammers, by definition, emit powerful radio signals, so they become obvious targets in their own right. While many of the drones deployed in Ukraine use civilian-grade GPS, the US military uses a more jam-resistant form of GPS known as "M-code." But M-code has its own set of issues; ultimately, it is not wholly jam-proof either. Thus, simply providing Ukraine with US military-grade GPS systems is not sufficient.

Thus, the most logical next step is to give these weapon systems more onboard sensors and intelligence, so the artillery shell or drone itself can make the final guidance corrections to hit their targets accurately. Thanks to the phenomenal advances in low power and cheap computing (think Raspberry Pi's, for example) and AI advances, it is possible to equip a drone or artillery shell with an onboard AI that can use vision recognition and other technologies to guide itself to targets. Similar technology is already widely used in many civilian scenarios--think of the self-driving features in Tesla, Mercedes, and other cars. Many consumer drones now have AI-powered autonomous features, like obstacle avoidance.

The bottom line: there are widely available AI technologies now that could be employed to maintain or increase weapon accuracy could be maintained, even in the presence of GPS jamming. Of course, it's not just guidance systems that could benefit from AI--AI could impact nearly aspect of warfare, from guidance to sensors to logistics to battlefield coordination: https://www.wired.com/story/helsing-ai-military-defense-tech/

These advances are very logical and eminently feasible. They could make a big difference in ending the war more quickly. Given the immense human suffering caused by the war, it's hard to argue that Ukrainians should not do what they can to get to victory and end the war more quickly.

Therein lies the dilemma.

For the entirety of humanity's history, war has been fought by humans, and importantly, the decision to kill another person, even in warfare, was made by a human. The specific technologies have changed over time, from clubs to bows and arrows to now GPS-guided artillery shells. But ultimately, there was a human in the loop making the decision, and that human ultimately is accountable (think of the war crimes trials at the Hague, for instance).

Introducing AI puts us on a very slippery slope where humans are NOT making those decisions. An AI could very literally be making the final decision of who to kill and who not to kill.

Some of you may be saying at this point: "But wait, Alex, the AI is only guiding the artillery shell or drone. The decision to fire was still made by a human".

At first glance, that analysis is entirely correct. Plenty of other technologies are used in guidance systems besides AI; why is AI special?

The specialness lies in how an AI system will make decisions. A traditional GPS guidance system is fairly straightforward: maintain course such and such to a specific destination. But an AI system will try to figure out something much more complex: that set of pixels in its video feed is a tank, and that other set of pixels is not a tank, so aim towards the tank.

Let's look at this from the perspective of someone trying to defeat an AI-augmented system. Just as the Russians introduced GPS jammers into the Ukraine war, any adversary facing AI-guided weapons is going to ask the question: what can I do to counter or foil the AI? Can I trick it into not realizing my tank is a tank?

Short answer: yes, this is entirely possible. For example, for years now, privacy advocates have been researching and exploring ways to defeat vision and face recognition systems. In 2010, for example, the 'CV Dazzle' project used patterns of makeup to foil vision recognition systems of the time. https://adam.harvey.studio/cvdazzle

Thus, if I know there are AI-guided artillery shells or armed drones aimed in my direction, what if I put a tarp over my tank that looks like a school bus? Surely the AI would not select a school bus for a target?

But if that works, what about the reverse? Unfortunately, there are unethical, amoral, and truly evil people in the world. What happens when such a person paints a picture of a tank on top of a school bus? While we would all like to live in a world where such atrocities do not happen, they still happen. How is an AI supposed to decide what is a decoy versus what is real? It gets messy fast--what if that same amoral commander surrounds his military equipment with hostages? This would be a complex situation for any human leader, even without the involvement of AI.

Bringing this all together, here is a hypothetical but likely scenario at some point in the not-too-distant future: through drone footage (interpreted and flagged by an AI), a Ukrainian commander spots a convoy of Russian tanks and GPS jamming equipment moving through a residential area in a city. Confident in the accuracy of his GPS and AI-guided munitions, he orders a strike with AI-guided explosive drones.

As those drones fly through their targets, the Russians turn on their GPS jamming equipment; the drones instantly switch to AI guidance mode. Now the final guidance computations are being made by the AI.

The AI sees the tanks amidst the everyday cars, trucks, ambulances, and school buses of civilian life. Target acquired.

Or was it?

Who did the AI decide to kill, and why?

I am not going to pretend there are easy answers to this dilemma. Undoubtedly, AI can improve the lethality and effectiveness of warfighting. The pressures and real-life human suffering of the large-scale war in Ukraine will inexorably drive rapid adoption of these technologies.

But much like the decision to use the first nuclear weapons in World War II, putting the AI as the final decision maker of "who to kill" will open a Pandora's box from which there is no easy return. While I think (and hope!) the Terminator-style "AI takes over the world" scenarios are more far-fetched and unlikely, I am far more disturbed at the prospect of turning killing decisions over to a computer algorithm.

Maybe we can rationalize AI-augmented munition guidance for artillery shells and drones en route to a target. But what's next? Why not a drone that loiters over enemy territory, waiting to spot an otherwise elusive target?

It's a very slippery slope...