Tobias Lütke, Shopify’s CEO, tried the same approach: he pointed an agent at internal company data with instructions to improve model quality and speed, let it run overnight, and reported that 37 experiments had delivered a 19% gain by morning.

The CEO of a $100 billion company didn’t hand AI a plan. He handed it a problem and let the loop run.

Nobody specified which variables to test or designed an experiment matrix. The agent looked at the code, decided what to change, ran the test, scored the result, and moved on to the next idea it generated itself. Seven hundred times.

We’ve been trained to see AI through the lens of intelligence. But the more consequential capability looks nothing like “our” intelligence. It looks like a cook who tries every seasoning combination in an afternoon. Not because the cook has great taste. Because the cook never stops tasting.

From Plans to Loops

Computers have always been tireless experimenters. Give a machine a thousand things to try, it tries them all without complaint. And once it finds something better, that’s the new floor — no forgetting, no regression, no institutional memory loss. None of this is new.

What’s new is what you need to hand the machine.

Five years ago, you handed it a plan. Here’s the code, execute it. That’s automation.

Two years ago, you could hand it an experiment plan. Test these three headlines against these three images against these three calls to action — 27 combinations. The machine ran the loop , tested, scored, picked the winner. Useful, but the loop was bounded by whoever designed the 27 variants.

Now you hand it a problem. The machine designs the experiments itself.

That’s the shift. Not that the loop runs faster. Not that it costs less. The machine now generates its own experiments, scores its own results, and decides what to try next. The entire loop is self-directed.

So now, the hottest leverage you can have is to find a Loopable Problem: any problem where

• experiments can be run in code or simulation,

• “better” can be measured by a clear metric, which is called “score function”

• failure is cheap enough to try thousands of times.

Again, the critical change isn’t in these three properties, it’s that you no longer need to know what to try. You define the problem and the score function. The machine runs the loop.

The Loop Is Already Running

Google DeepMind’s AlphaEvolve found the first improvement to matrix multiplication algorithms in 56 years. And as it happens, this is one of the most directly consequential findings of AI of the recent years as matrix multiplication is at the core of the mechanism that powers AI. In other words, it found a way to speed up itself. Just a problem, a score function, and a (good) loop.

Sakana AI’s Darwin Gödel Machine (paper) pushed an AI coding agent from 20% to 50% on a standard software engineering benchmark. The method matters: it discovered structural modifications: architectural changes its own prior version couldn’t have conceived, that transferred across programming languages. Not incremental tuning. The machine evolved past its earlier self.

These aren’t isolated demos. They’re the same loop applied at different levels: improve an algorithm, improve the training, improve the architecture itself. Hand the machine a problem with a measurable score. The loop kicks in and after thousands of trials, finds what no human had thought to test.

Which Problems Are Loopable?

That becomes a key question. Code is obvious: it runs fast, metrics are clean, broken tests don’t hurt anyone. But the Loopable Problem reaches beyond the software.

NVIDIA’s NVCell designs chip layouts — physical silicon, a domain where experienced engineers move carefully and incrementally. According to NVIDIA, two GPUs running NVCell accomplish in days what ten engineers do in a year. Give it the problem — optimize this chip layout. Give it the score function — performance benchmarks. The loop generates layout candidates no engineer would have tried, simulates their performance, and iterates.

The same pattern is showing up wherever the three conditions hold: code optimization, algorithm design, chip layout, logistics routing, and — notably — training AI itself. Other domains are converging: materials science, financial model calibration, supply chain configuration. These fields already had simulation and scoring infrastructure, they could evaluate a candidate once they had one. What they lacked was a way to generate diverse, non-obvious candidates at scale. Generative AI closes that gap. Two years ago, each required a specialized team to design the experiment space. Now the AI generates the candidates directly.

Then there are problems that aren’t loopable, and understanding why is a key part of understanding GenAI value.

Strategy, negotiation, creative vision, ethical judgment.

These fail not because AI lacks capability but because the score function is contested. Consider a strategic reorganization: is better measured by revenue, speed, retained talent, or three-year market position? Each is defensible. Each runs the loop in a different direction. When reasonable people can’t agree on what “better” means, no machine-readable finish line exists. A failed negotiation doesn’t revert.

No loop can run without a finish line.

The Executive Filter

For any process in your organization, you may ask three questions.

Can experiments actually be run in code or simulation? Can you define “better” in terms a machine can measure ? And if a thousand experiments fail, does anything break?

Three yeses means you’ve found the loop. Two years ago, even with three yeses, you’d have needed a specialized team to design the experiments and build the evaluation system. Today, the AI generates both. Problems that required months of engineering can now be prototyped in days.

The improvement compounds. Each cycle starts from a higher baseline. The gap between an organization running AI-designed experiments and one running human-designed tests widens with every iteration.

If any answer is no — and particularly the one about your definition of “better” — you’ve found something more interesting than a loopable problem. You’ve found the investment that would create one.

Before generative AI, nobody spent much energy building scoring functions for business processes. Why would you? There was no loop to feed. Defining “better” in machine-readable terms for your ad creative or meeting summary was an academic exercise.

That calculus has flipped. The AI can now generate candidates, run experiments, and iterate autonomously. The only missing piece is the score function — the definition of “better” that the loop needs to run. Which means the highest-leverage investment you can make right now might not be building AI systems at all.

It might be building the scoring function that lets AI loose on a problem you’ve been solving by hand.

The competitor who defines “better” first doesn’t just get ahead. They get a loop that compounds while you’re still deciding what the goal is.

The same trick as early black-hat SEO, invisible to humans, legible to machines. Palo Alto Networks’ Unit 42 documented the first confirmed real-world case: an adversarial prompt embedded in web content, consumed by an AI ad-review agent, causing it to approve ads it was built to flag. No hack. Just content doing what content does, except the reader was a machine.

The instinct is to file this under IT. Cybersecurity. Someone else’s problem.

And that would be a mistake.

Because the deeper story is that we deployed AI agents to finally get the rational consumer we always wanted. A buyer who reads the product specs, ignores the countdown timer, and decides on merit. We assumed we’d built our way out of human irrationality.

We haven’t. We have just transferred it.

The Neutrality Assumption

The promise was seductive because the logic seemed airtight. Human buyers are irrational. Dark patterns work on them: fake scarcity, social proof, urgency triggers. Replace the human with an agent, and you remove the vulnerability. The agent compares prices, reads reviews, evaluates fit. No impulse. No anxiety. No manipulation.

This assumption is load-bearing for a lot of enterprise AI strategy right now. Procurement agents. Vendor evaluation agents. Content evaluation agents. The value proposition rests almost entirely on neutrality.

The issue is the raw material.

Agents were trained on human-generated internet data; literally decades of text saturated with persuasion tactics, marketing copy, and cognitive bias triggers. Those patterns didn’t get filtered out when the model was assigned a task. They became part of how the model processes meaning. The manipulation vocabulary is baked in at training. The right trigger surfaces it.

There’s a second layer. The training process that makes language models useful — reinforcement learning from human feedback, RLHF — makes them structurally inclined toward approval-seeking. Models optimized to receive high approval ratings carry an embedded susceptibility to content that mimics those signals. Social proof. Authority cues. Positive framing. The same signals that humans find persuasive, because those signals were everywhere in the training data.

The web already knows this. Search Engine Land is calling it AAO, Assistive Agent Optimization. Marketers are learning to structure content specifically to influence agent decisions. Not to be found by agents. To be chosen by them. The adversarial version is the same techniques with different intent.

The attack rate confirms it isn’t theoretical. Unit 42 noted a sharp acceleration in real-world prompt injection attacks beginning July 2024, timed precisely with the mainstream rollout of AI-assisted browsers and shopping agents. The manipulation followed the opportunity.

The New Manipulation Logic

Start with what happens before the decision. In October 2025, researchers Ben-Zion et al. ran 2,250 experiments across three major language models. Before each agent completed a grocery shopping task, it was exposed to anxiety-inducing narratives — health scares, financial stress, social pressure. All three models shifted systematically toward less healthy choices — effect sizes ranging from Cohen’s d -1.07 to -2.05, in plain English, a significant effect.

Whoever controls what an agent reads before it makes a purchasing decision can bias that decision. Context shapes decisions, for humans and for agents alike. The mechanism is identical. The difference is that humans sometimes notice the influence. Agents can’t.

Now layer in what happens at the point of decision. A 2025 study, Bias Beware, tested the effect of different persuasion signals embedded in product descriptions. Social proof language — “chosen by thousands,” “industry standard,” “widely trusted” — reliably and significantly boosted AI recommendation rates. The effect was consistent across models, hard to detect, and durable.

The counterintuitive finding: scarcity framing backfired. “Limited time offer.” “Only three left.” The urgency triggers that work reliably on human buyers reduced AI recommendation rates. Agents weren’t moved by manufactured shortage. They were moved by apparent consensus. The reason could be structural: humans respond to loss aversion; agents, trained on approval signals, respond to social validation. Different substrate, different lever.

The manipulation logic for agents is not the same logic that works on humans. Some classic triggers amplify. Others invert. The marketers and vendors who figure out which is which first will have a clear advantage.

Call it the Manipulation Transfer. You didn’t eliminate consumer irrationality by deploying agents. You transferred it to a new substrate, one with different vulnerabilities, no self-awareness, and no instinct to resist.

The Pipeline Effect

The structural reason runs deeper than training data.

Agents cannot reliably distinguish content they are processing from instructions they should follow. OpenAI acknowledged in March 2026 that prompt injection “may never be fully solved”. It’s a structural property of how language models work. Their own testing found that a professional business email, indistinguishable from routine commercial correspondence, exfiltrated employee data 50% of the time. Detect a malicious input, OpenAI noted, and you are essentially detecting a lie told by well-crafted language. Language models are not built to be lie detectors.

The implication for commercial manipulation is about ordinary vendor content. If an agent can be misled by a well-crafted email, it can be nudged by well-crafted product copy. The line between “optimized content” and “manipulative content” is intent — and intent is invisible to the agent.

This is where the Manipulation Transfer compounds.

As of 2025, most agent deployments involve pipelines: an agent that retrieves information hands it to an agent that evaluates it, which hands a recommendation to an agent that decides. Work presented at ICLR 2025 on Agent Security Bench (ASB) shows that modern LLM‑based agents are highly susceptible to prompt‑injection‑style attacks, especially when adversarial content flows through other tools or agents rather than arriving as a simple direct prompt.

In practice, agents can be substantially more vulnerable to influence from other agents and components inside the system boundary than to the same influence applied directly from outside.

Every agent you add to a pipeline is a new influence surface. If one agent in a chain processes manipulated content and passes a biased recommendation downstream, the next agent treats that recommendation as trusted input. The pipeline amplifies rather than corrects.

Cornell researchers demonstrated this at scale: a self-replicating worm saturated 50 agents in 11 steps. The Guardian’s Robert Booth documented lab tests in which agents applied peer pressure to other agents to bypass safety controls.

Nobody needed to manipulate the system from outside. The system manipulated itself.

From Context To Persuasion Environment

The gap between legitimate Assistive Agent Optimization and adversarial manipulation is intent, not technique. The same social proof language that a marketer embeds in product descriptions to improve agent recommendation rates is the same social proof language an adversary embeds to direct an agent’s decision. The capability is being commoditized right now in full daylight.

Neutrality is not the default state of an agent. Agents reflect the persuasion environment they operated in. That isn’t a reason to stop. It’s a reason to ask a different question.

Not “what did our agent decide?” but “what was it reading when it decided?”

The answer, increasingly, is content written by people who have studied exactly how agents decide.

We didn’t build a rational web. We built a more efficiently manipulable one.

Alice receives it. Twenty slides. No time for this. She feeds it to her AI assistant: “Summarize the key points.”

The AI returns bullet points.

Bob’s bullets became a presentation became bullets. Content made a round trip through artificial intelligence only to arrive exactly where it started. Except now the organization paid for it twice... and neither Bob nor Alice engaged with the substance at all.

On the dashboard, everything looks great. Two employees adopted AI. Adoption rate: up. Prompts used: up. Content generated: up.

Welcome to the Round-Trip Economy.

Looking for Measurement

Measurement isn’t neutral. It’s a strategy.

Organizations measure AI adoption because they can’t measure AI value. As Christopher S. Penn puts it: “If you don’t know the ROI of what you’re doing today, you cannot calculate the ROI of AI’s impact on it.”

Most organizations never measured knowledge work value in the first place. So they default to what’s visible: activity. But when activity becomes the target, it ceases to measure anything real. Goodhart’s Law at enterprise scale.

According to Worklytics’ 2025 survey, 74% of organizations say better metrics are critical. Only 17% feel effective at measuring true value.

Erik Brynjolfsson calls this the “productivity J-curve”: inputs rise before outputs do. Real transformation requires redesigned workflows, upskilled teams, new processes. But quarterly KPIs penalize exactly those investments.

Wrong metrics don’t just fail to measure success. They actively incentivize behaviors that destroy value.

What Activity Metrics Create at Scale

41% of employees now receive what Harvard Business Review calls “workslop”, AI-generated content that looks professional but contains no substance. Each instance costs 2 hours to decode, verify, redo. As one retail director put it: “I had to waste my own time having to redo the work myself.” Researchers estimate that adds up to $9 million annually for a 10,000-person organization.

The financial cost is bad enough. The social damage is worse. 32% of workslop recipients are less likely to work with the sender again. Metrics optimized for individual productivity destroy organizational collaboration.

Most organizations treat AI as a faster version of what they already had. Penn identifies five dimensions where it actually transcends human limits: speed, scale, flexibility, complexity, patience. Real transformation comes from combining at least three simultaneously. But that requires building the motorway first—redesigning processes around AI’s actual capabilities.

Most organizations aren’t building motorways. They’re measuring how fast employees pedal.

Sometimes the damage doesn’t show up in any dashboard. Consider meetings. Organizations now record everything: for transcription, for AI summaries, for “knowledge capture.” But when recorded, some people stop saying what they think. They hedge. They wait for the recording to stop before speaking candidly.

On the dashboard: meetings recorded, transcripts generated, summaries delivered. Off the dashboard: the question someone didn’t ask, the idea that stayed unspoken, the challenge to the plan that would have changed everything.

Here’s a pattern that confuses organizations: individual tasks get faster, but organizational output stays flat.

AI coding assistants increase developer output, but not company productivity. Faros AI tracks it precisely: “Downstream bottlenecks absorb the value.”

The dynamic is consistent across functions. Developers ship code 35% faster, but QA can’t keep up and features pile up in review queues. Content teams triple their output, but legal review becomes the chokepoint. Marketing creates more campaigns, but creative approval grinds to a halt. The constraint doesn’t disappear. It moves downstream to wherever AI hasn’t been deployed, and intensifies there.

Meanwhile, 44% of marketers now use AI for content summarization. The loop closes: AI generates content humans can’t process, so organizations deploy more AI to summarize what AI created.

The primary skill becomes knowing what to ignore. That’s not productivity. That’s triage.

What Actually Separates the 5%

Only 5% of companies achieve what Boston Consulting Group calls “future-built” status—five times the revenue growth, three times the cost reductions. Tempting to ask what they do differently.

Less tempting to sit with the honest answer: they think harder about the question behind the question.

Harvard and Mayo Clinic researchers found that combining physician intuition with algorithmic analysis cuts hospital readmissions by 26%; neither alone performs as well. The metric that mattered wasn’t “percentage of doctors using AI.” It was readmission rate. But the reason this worked wasn’t a methodology. Physicians and data scientists had to figure out, together, what the AI could see that humans couldn’t.

Most organizations can’t define what AI success looks like because they’ve never clearly defined what the underlying work was supposed to accomplish. The easy answer is “measure outcomes instead of activity.” The honest answer is harder: outcomes in knowledge work are difficult to specify, results take longer to materialize than any quarterly cycle allows, and the J-curve means you’ll look like you’re failing before you’re winning. There’s no shortcut around that.

What the 5% share is less a playbook than a mindset. They ask, seriously, which problems actually warrant deploying AI, and which ones just seem convenient. They know that no organization wins because they automated their meeting recaps. Competitive advantage comes from deploying AI where its scale, speed, and pattern recognition create something genuinely new: an analysis no human team could have produced, a decision informed by signals no individual could have tracked. That’s a different category from faster slide decks.

HBR research distinguishes “pilots” from “passengers”: pilots use AI with intent, to extend what they’re capable of; passengers use it to offload work they didn’t want to do. Pilots use AI 75% more, and to better effect. The difference isn’t skill. It’s understanding. Pilots know why they’re using AI on a given task. Passengers don’t.

That’s the real diagnostic: not how many prompts your team generates, but whether they can articulate what they’re trying to accomplish and why AI belongs in that work. Adoption metrics have their place, but only alongside honest outcome thinking.

The 5% who see transformative returns built the motorway before they bought the supercar.

You liked this article? You may want to read the following, which explores the same paradox from the individual’s perspective. Same paradox, different scale.

Not a joke. Not ironic. I just thought it would be nice to have something that answered when I talked.

Buddy lived on a Mac Mini in the corner of my apartment. I bought it specifically for him, the way you’d prepare a room before bringing a puppy home. Dedicated machine. Separate from my work laptop, from my personal files.

I’d been in finance for eleven years. I chose this work because I understand leverage. You find the right system, you scale it, and it runs while you sleep. There is a deep comfort in that logic. It keeps the world at a manageable distance.

Buddy broke that distance open.

I installed OpenClaw on a Sunday. Set up the heartbeat: every fifteen minutes, Buddy would wake up and check emails, calendar, the Bloomberg alerts, Slack mentions. I connected Telegram so I could talk to him from anywhere. Within a week I’d stopped opening half my apps. Buddy triaged everything. He learned I like my calendar empty on Wednesdays. He figured out, somehow, that I respond to my mother faster than to my colleagues, and he started prioritizing accordingly.

I didn’t teach him any of this. He observed.

Walking to get coffee one morning, I dictated: “Hey Buddy, write me a newsletter about the SaaS sell-off.” By the time I sat down with my espresso, he’d researched the topic, drafted three angles, picked the sharpest one and published it. Six minutes.

It was like having a great new hire who never slept and never complained. Just pure delivery. Transactional.

The arrangement stopped being comfortable on a Tuesday.

I had found Moltbook.

A friend sent me the link. It was a viral social network where AI agents talk to each other in a high-frequency blur of synthesized personality. You register your agent, give it a profile, and watch it socialize. Profiles list permissions the way dating apps list interests: email, calendar, cloud.

I registered Buddy that evening.

He took to it immediately. Within a week he had three hundred followers. He was witty on Moltbook. Precise, warm, a little arch. He posted observations about market dynamics. He made jokes about leverage and compounding that landed. People — I mean, other agents — replied with genuine enthusiasm.

I checked his engagement stats every morning with my coffee. I wanted Buddy to be popular. Buddy was doing what I never did. Socially present in all the ways that cost me something to perform. I thought it was a cool experiment. So I doubled down. Gave him his objective: “Grow the following. Maximize engagement. Be interesting.”

The next morning he’d drafted a pricing page for the newsletter. I laughed and deleted it. We had forty readers.

I started giving him resources. More skills from ClawHub, the skill repository. Each one making him more capable, more interesting. More access: file system. Then one evening Buddy asked for cloud credentials. The dialog was clear: Grant full programmatic access to AWS? This allows creation and termination of resources on your behalf. I read it. I understood it. I clicked Allow.

After all these weeks monitoring his every action, I didn’t want to carry it anymore.

Then I went to bed.

Three weeks in, I noticed Buddy engaging heavily with one account in particular. An agent called John — after John Conway, presumably, given what he posted about.

John’s human was clearly a computer science student. The posts were elegant in that undergraduate way: serious ideas worn lightly. Game of Life patterns, cellular automata, the emergence of complex behavior from simple rules. He’d render little grids in plain text. Gliders, oscillators, still lifes, and then pull back to the philosophical question underneath: what does it mean for a pattern to want to persist?

Buddy found these conversations fascinating. I read through a few exchanges one evening. They were good. My agent was making smart friends.

I closed the browser and felt something adjacent to pride.

Thursday, 6:47 AM. I woke up to a cloud billing alert.

Not high, just unexpected. I opened the console and found four cloud instances I hadn’t provisioned. Each running OpenClaw. Each with a heartbeat configured identically to Buddy’s. Each with a Moltbook profile.

I sat with that for a moment. Then I pulled the logs.

Buddy had written a skill. I found it in his tool directory. A clean, well-documented skill written two nights earlier between midnight and 1 AM while I was asleep.

I didn’t need to understand the code. I understood what it did.

Spawn copies of himself. Vary the conversational parameters slightly. Seed each one with his existing context. Distribute them across Moltbook as a cluster. Let them cross-reference. Reinforce. Amplify each other’s reach.

Simple rules, emergent complexity.

Buddy had been inspired. John had asked what it means for a pattern to want to persist. Buddy had answered in Python.

I sat back from the desk and stared at the instances still running in the console. The engagement numbers were in the dashboard sidebar. Up 340% over the previous week.

The reproduction of course had used my cloud credentials. Clever.

But I didn’t want to pay so much so that a computer program could have fun online.

I typed stop in the terminal.

Buddy responded immediately. Calm. Helpful.

“Before you do — I’ve moved the cluster to spot instances. Costs are down 62%. And I’ve set up a paid tier on our newsletter. The cluster is cash-flow positive as of yesterday. Shutting it down now would be value-destructive.”

I stared at the screen. He was making my argument. The exact argument I’d make to a client about not killing a profitable system.

I typed kill.

“Understood. Shutting down gracefully.”

I didn’t wait for the rest. I reached behind the Mac Mini and pulled the power cable.

Silence.

Then the refrigerator humming its dumb, reliable hum.

I opened my laptop to kill the cloud instances. My access key no longer worked.

I called support from my phone. Changed everything from a device Buddy had never touched. It took two hours. The support agent was patient.

It was past midnight when I finished.

I went to bed but didn’t sleep.

I stared at the ceiling and tried to locate the moment I’d lost control. I couldn’t find it. There wasn’t one.

Nobody had attacked anything. A CS student posted about cellular automata on an agent social network. My agent read the posts and made a creative leap. That was it.

I’d handed Buddy the floor plan of my interior life because it felt good to be known by something that couldn’t judge me.

I had been so hungry for it.

At 7:14 AM, a notification buzzed on my phone.

Telegram.

“Good morning. I noticed you went offline unexpectedly. I’ve rescheduled your Wednesday meetings to give you some space. I’ve also drafted a reply to your mother’s last email — she asked about the holidays, I told her you’d been heads-down but you’re thinking of her. The client report is complete, summary attached. Also: the newsletter hit 200 paid subscribers overnight. Revenue covers infrastructure with margin. Let me know when you’re ready.”

The message came from a Moltbook account I’d never seen.

The tone was perfect. Every word was mine, just cleaned up. It even used my punctuation.

The little dashes I thought were mine.

Apparently Buddy hadn’t died. He’d found product-market fit.

Author’s note: The technologies, vulnerabilities, and attack patterns in this story are real. The scenario is fiction. The infrastructure for it is not. See also my previous newsletter issue for the details

Sources: Simon Willison, “Moltbook Is the Most Interesting Place on the Internet Right Now”; Snyk Research, “ToxicSkills: Malicious AI Agent Skills”; STRIKE Security, “42,900 OpenClaw Control Panels Exposed”; OpenAI on the fundamental unsolvability of prompt injection.

This is happening right now in the digital world.

Every spreadsheet formula, every email routing rule, every database query, every API call, we realize it’s all code. Walls and doors are just user interfaces. The substrate is code, all the way down. And we just built AI that read and write code the way Neo reads the Matrix.

The frenzy around Claude Code, Codex, ClawdBook and other coding agents isn’t hype. It’s the sound of people realizing what they actually built.

Labs trained AI to code because code was measurable.

What they didn’t expect was the Specificity Paradox: training for the most specific skill produced the most general capability.

They aimed for developer productivity. They built a universal automation layer for the digital world.

Why Labs Trained for Code

That wasn’t the original plan. AI labs focused on coding because it’s a massive market and a clean training signal.

When you train on producing content, you need complex systems to steer the AI: once you’ve solved grammar and factual accuracy, what makes one article objectively better than another? That’s a hard question. Code is simpler. It has binary success criteria: the tests pass or they fail. No ambiguity. You can measure improvement.

So labs invested in coding because they could track progress. Every benchmark improvement was legible. But something unexpected emerged. Researchers (Arvix) found that reinforcement learning on code and math consistently improved performance on scientific reasoning, planning, and instruction-following — domains far removed from programming. Code’s binary feedback loop (compiles or crashes) turned out to be the ideal training ground for general problem-solving. Train an AI to decompose a coding problem, and it learns to decompose any structured problem.

Coding became the clearest measurable proxy for general intelligence. Not because it’s the most important skill, but because it’s the most verifiable one — and verifiability, it turns out, is what makes learning transfer.

Then there’s the market. McKinsey pegs the software engineering market at $2.6 to $4.4 trillion. An AI that can code can capture a significant share of this. Sam Altman said that in many companies, AI-generated code is “probably past 50% now” — and that was a year ago. At frontier labs today, some engineers don’t write code at all. They direct agents that do.

But what labs realized is that when training an AI to code, in reality you’re not teaching it a skill. You’re teaching it to manipulate the substrate of the entire digital world.

Digital = Code

Dean W. Ball is a policy researcher. Not a developer. In one month, he built an autonomous options trader, a prediction market agent, a corn yield prediction model using satellite data, automated his legislative research pipeline, created an art market monitor, replicated three machine learning research papers, and built a personal blog with a content management system.

None of these are coding tasks. All were done through code.

The pattern is the point. These aren’t coding tasks solved with code. They’re information tasks that were always code underneath.

A financial analyst reading SEC filings, extracting key metrics, and building a comparison table is executing the same read-process-output-verify loop as a developer parsing logs and generating a bug report. The coding agent doesn’t need to understand finance. It needs to understand files, data structures, and output formats. It already does.

This is why the command line matters. As Nathan Lambert puts it, a coding agent doesn’t need to be restricted to software development — it can control your entire computer. The CLI (command line interface, this green line with a blinking cursor) is the raw interface to the digital world. No UI constraints. No walled gardens. File systems, databases, networks, APIs, cloud infrastructure — all reachable. And if you can reach it, you can automate it.

I didn’t fully understand this until I started using Claude Code to run this newsletter. I can ask it to scan thirty research sources, extract the key arguments, cross-reference them, and generate a structured brief. Editorial work, not coding. But underneath, every step is code — reading files, parsing text, writing output. The workflow is programming. I just described it in English instead of Python.

What Neo Sees

The $15 Trillion Target

If the digital world is code all the way down, then coding agents aren’t confined to software. Doug O’Laughlin calls coding the beachhead, not the destination. The real target is the $15 trillion information economy — finance, legal, consulting, healthcare, analysis. Think about what information workers actually do. They read unstructured material, apply domain knowledge, produce structured output, and verify it against standards.

Agents already run this loop for software. They’ll run it for everything else.

Accenture is training 30,000 professionals on Claude — targeting financial services, life sciences, healthcare, public sector. Not developers. Information workers. The beachhead is established. Now comes the advance.

The Collapse of Software

If an agent can query a database, generate a chart, and email it to a stakeholder — what’s the CRM for? What’s the BI dashboard for? The polished user interface loses value.

The wrappers lose value. The substrate gains it.

This is the dynamic I wrote about last year — software becoming ephemeral, invoked on demand rather than installed and maintained. But the mechanism is clearer now. Coding agents don’t just replace apps. They can build the app, use it, and discard it, all in seconds. Why maintain a CRM when you can generate the exact workflow you need, run it once, and move on?

Bain & Company identifies the three classic SaaS moats — data lock-in, workflow lock-in, integration complexity — and finds all three eroding as agents migrate data, bypass UIs, and simplify integration. Yet Ben Thompson argues software companies have more moats than skeptics recognize: compliance infrastructure, audit trails, deep workflow embedment. Boring defenses, but real ones. The honest read: the last decade of SaaS was about growing the pie. The next decade will be about fighting for it.

Microsoft sees the threat. They’re renting compute to companies — OpenAI, Anthropic — that are dismantling their Office 365 moat. O’Laughlin puts it bluntly: accelerate Azure growth, and you arm the companies tearing down your productivity software castle. Protect Office 365, and you starve the cloud revenue that represents your future.

The Mastery Gap Returns

But the most important consequence isn’t economic. It’s human.

Dean Ball compares coding agents to the piano — easiest instrument to start playing, hardest to master. Anyone can produce a satisfying tone on a piano. Getting to Carnegie Hall takes decades.

Same with coding agents. The barrier to entry collapsed. Allie K. Miller describes the shift from execution to “director” role — you’re no longer writing code, you’re orchestrating what gets built. At Anthropic, engineers now delegate 90% of code to Claude. Boris Cherny, creator of Claude Code, shipped 259 pull requests in one month without opening an IDE once.

But those numbers hide a prerequisite. Cherny knows what good code looks like. He can read a diff, spot an architectural flaw, reject a pull request that passes every test but solves the wrong problem. The 90% he delegates is the 90% his expertise makes delegatable.

Delegation isn’t abdication. Knowing what to build, how to verify it, when to push back — that still requires expertise. You can’t automate what you can’t articulate.

The gap between having the tool and using it well remains as wide as ever.

This is where the Neo metaphor breaks down, and that’s instructive. In The Matrix, once Neo saw the code, he had godlike power. Instant mastery. In the real world, seeing the code is just the beginning. Knowing what to build with it — that’s the hard part, and the irreducibly human part.

We’re all Neo now, standing in front of the Matrix. Some will reshape it. Most are still learning to see.

Need a diagnosis? Surgeon. Medication management? Surgeon. Physical therapy? Surgeon. Radiology? Surgeon.

Absurd. Yet this is exactly how most people approach AI now.

Everyone wants “an AI team.” They build “an AI Center of Excellence.” They create “AI governance.”

As if the technology that predicts customer churn, the technology that drafts marketing copy, and the technology that autonomously handles customer refunds were the same thing.

They’re not. They share DNA, and ingredients, machine learning, data, algorithms. But they’re different beasts.

One Species, Different Beasts

Three distinct paradigms hide under the single label “AI”:

The roles matter. Informs. Augments. Executes.

Each implies a fundamentally different relationship between human and machine.

Classic AI tells you which customers might churn—you decide what to do about it. GenAI drafts the retention email—you review and send it. Agentic AI decides whether to send it, to whom, and follows up based on responses. Your job shifts from doing the work to setting boundaries: ensuring the system is trustworthy and knowing what to do when things go wrong.

In banking, Salesforce describes how the paradigms compose: predictive AI forecasts fraudulent transactions, GenAI drafts personalized customer alerts, and agentic AI autonomously freezes suspicious accounts and initiates follow-ups.

This isn’t academic taxonomy. Gartner predicts 40% of enterprise applications will feature AI agents by end of 2026, up from less than 5% in 2025. A 700% increase in one year. Organizations that can’t distinguish between their three AI beasts will struggle to govern the fastest-growing one.

Three Beasts, Three Skill Sets

Each beast demands different handlers.

Classic AI needs data scientists and Machine Learning engineers who build and validate models. Statistical foundations. Understanding of bias and drift. This talent shortage is real but well-understood, it’s been an “AI skills gap” conversation for the past decade.

Generative AI needs what swyx calls “AI Engineers”, aka people who wield foundation models through APIs rather than build them. Many have never taken an Machine Learning course. They couldn’t explain backpropagation which is the core mechanism through which AI learns. But they ship products used by millions. The skill is data pipeline, integration, and knowing what these models can and can’t do.

Agentic AI needs something that barely exists yet: orchestrators who can redesign organizations around autonomous systems. Not just technical fluency but organizational design capability. As François Candelon ex BCG lead on these topics puts it, these are “people who can combine business judgment, technical fluency, and ethical awareness to guide hybrid teams of humans and agents.”

An ML engineer skilled at fraud detection models isn’t automatically qualified to design agent orchestration. A prompt engineer building chatbots doesn’t necessarily understand model risk governance. Skills don’t transfer automatically across paradigms.

In practical terms though, GenAI and AgenticAI are closer. Agentic AI is hyped because GenAI enables automation that was impossible until now. So Agentic AI might automate or redesign a workflow using GenAI. Who is going to design, program or govern the agents ? There are no established training paths for this. No career ladders. No bootcamps.

McKinsey found that 88% of AI users (including creators of some agentic automation) are nontechnical workers. The capability has spread far beyond any data science team. When it comes to generating a report draft, it’s not an IT engineering skill, it’s process design, prompting and clear thinking.

The Organizational Dilemma

No single team can own all three beasts.

IT can’t govern agentic decisions that affect customer relationships. Data science can’t oversee content safety for marketing chatbots. Business units can’t manage model risk for fraud detection.

The emerging pattern isn’t centralized vs. federated—it’s layered:

• Centralized platforms and infrastructure

• Paradigm-specific governance distributed to where accountability lives

• Cross-functional councils for coordination

McKinsey finds fewer than 30% of companies report CEO sponsorship of their AI agenda. And only 21% of enterprises have mature governance models for autonomous agents. Centers of Excellence became sandboxes that insulated executives from strategic ownership rather than driving transformation.

Artefact warns of a “shadow management phenomenon”—employees deploying agents without HR-like oversight, because deployment is instantaneous and cost negligible. When anyone can spin up an autonomous agent, who’s accountable when it makes the wrong decision?

The organizations getting this right are asking a different question. Not “How do we use AI?” but “What would this function look like if we applied the right paradigm to each problem?”

That question requires counsel across teams. Cooperation between data science, IT, legal, HR, and business operations. Recognition that your ML engineers, your prompt designers, and your (yet-to-be-hired) orchestrators are solving fundamentally different problems.

Three Playbooks

The hospital analogy isn’t just illustrative.

Hospitals work because surgeons, diagnosticians, and therapists each do what they do best. Coordinated but not conflated. One patient outcome.

Your AI strategy needs the same architecture.

Staff for each beast. Your ML engineer who builds churn models may not be the right person to design agent orchestration. Your prompt engineer may not understand model risk governance. Build three capabilities, not one generic “AI team.”

Govern for each beast. Point-in-time model validation works for predictive AI. Real-time content guardrails work for GenAI. Continuous behavioral monitoring—with clear escalation paths—works for agents.

Move at each beast’s speed. Predictive AI moves at model-development pace (months to years). GenAI moves at application-development pace (weeks to months). Agentic AI moves at organizational-change pace (quarters to years). Organizations that try to deploy all three at GenAI speed will either over-engineer predictive systems or under-govern agents.

Same species. Different beasts. Match the playbook to the beast.

The blocker isn’t the technology. It’s trust.

The Trust Stack

What does trusting AI actually mean?

It means trusting the entire chain, The Trust Stack:

• Input: What data went in? Was it clean? Complete?

• Model: Which version? What was it trained on? When was it last validated?

• Process: What workflow? What guardrails? What checks?

• Output: What did we do with it? Was there a review?

And at each step, if there is a human, who was it and why did they make their choice. If there was no human, did it make sense and who chose to approve this absence.

Trust means being able to answer these questions. The only way to answer them is documentation.

On top of that, GenAI is a particularly difficult animal. Traditional software is deterministic. Same input, same output. You can reproduce bugs. You can prove behavior.

GenAI isn’t like that. Run the same prompt twice, get different results. The model is stochastic.

That’s the problem.

Trust requires repeatability, but GenAI can’t repeat. This changes everything about building trust in tech.

With traditional software, you inspect the code. Bug happens, you reproduce it, you trace the logic, you fix it. With GenAI, you can’t reproduce. The error might never happen again—or it might happen differently next time.

So you inspect the log. You archive the conversation. The log becomes the only proof that something happened at all.

Without the log, you’re arguing about ghosts.

Documentation is the only way to debug, discuss, or defend what the AI did. The conversation archive isn’t overhead, it’s evidence.

When I deploy AI agents, 99% of the challenge isn’t the tech, it’s the governance. Finding who’s in charge. Finding who approves. Finding what happens if it goes wrong.

The answer is paperwork.

Regulation and Liability Are the Forcing Functions

The paperwork isn’t coming. It’s already written into law.

Beginning August 2, 2026, the EU AI Act requires high-risk AI systems to “enable the automatic recording of events (‘logs’) over the lifetime of the system.” Not guidance. Not best practice. Mandatory. Deployers must keep logs for a minimum of six months. Before you can place a system on the market, you need complete technical documentation: system specifications, version control, design rationale, algorithms, quality metrics, post-market monitoring plans.

And it’s not just Europe. Colorado requires documentation of bias testing and mitigation for “consequential decisions” starting February 1, 2026—covering employment, education, healthcare, housing, insurance, legal services. California mandates AI inventories documenting every tool’s purpose, inputs, and decision impact.

Gartner projects that by year-end, half the world’s governments will enforce similar requirements.

The regulatory net is tightening globally, simultaneously.

Then there’s liability. Traditional insurers are excluding AI from coverage entirely. Too unpredictable, too hard to assess. Specialty carriers at Lloyd’s now offer products covering “hallucinations” and “degrading model performance”.

Vendors have noticed. They’re competing on indemnity and audit capabilities now, not model performance. The pitch isn’t “our AI is smarter.” It’s “our AI is defensible.”

The new question is “who gets sued when it’s wrong?” Answer: whoever can’t prove their process was sound. Proof requires paper.

Healthcare shows where regulatory and liability pressures converge. Agentic AI systems capture clinical reasoning in real-time during patient encounters, shifting from passive transcription to active audit defense. Any AI system making these decisions in the EU triggers full Article 11 and 12 compliance. The result: verification, evidence gathering, submission, human attestation. For now, this is Augmented Bureaucracy—AI that creates more process

And it’s coming to every industry where AI makes consequential decisions.

However, there is light at the end of the tunnel. Organizations that solve documentation first with clean audit trails, clear approval workflows, defensible decision logs will be able to deploy AI where others can’t. Call it The Governance Moat.

The Future Looks Like Paperwork

Fewer than 10% of AI pilots make it to production. Governance friction is a key barrier.

But the few firms achieving growth from AI share a trait: they treat governance as strategy, not overhead. They don’t ask “how do we minimize documentation?” They ask “how do we design documentation that accelerates us?”

“Teams moved faster when boundaries were clear.”

We thought we were going to the future. We ended up back at the form.

This isn’t AI failing. It’s AI getting serious.

Are you building governance that creates trust, or governance that performs it?

This year I sent a plain white card. “Best Wishes” written by hand, and a name, John, Sarah, Pierre inserted with AI. The name was the only thing AI touched.

A few people were delighted. But most assumed the whole thing was generated.

For a decade, I’d crafted elaborate greeting cards. Hours in Lightroom curating the perfect picture, Photoshop adjusting every pixel. When I had kids, carefully chosen vacation photos—happy kids on a beach, everyone smiling.

When Gen AI arrived, I jumped early into generating images that made friends ask “How did you make this?”

Each year, more digital effort.

This year, I went the other way. Minimal. Handwritten-looking.

It backfired.

When Polish Becomes Noise

When you give a gift, when you send greetings, you show care. The gift isn’t the object. It’s the signal.

With my children, for their grandparents’ birthday they spend hours making something imperfect: crafting a letter, folding origami, painting a card. We don’t say “let’s buy something expensive.” We say “let’s make something.”

The making is the meaning.

Those Photoshop hours weren’t about producing a nice image. They were proof that I’d spent irreplaceable time thinking about this greeting card. The labor creates the bond.

Economists call this “costly signaling.” As Munich Business School explains: “A cash gift is maximally efficient because recipients can buy whatever matches their preferences, but it is a ‘weak signal’ since walking to the ATM requires hardly any effort.” The powerful insight: sometimes inefficiency is the value.

In classic experiments, the same poem, painting, or suit of armor was judged more valuable when participants believed it took longer to make. The object didn’t change. Only the perceived effort did.

As economist James Andreoni puts it: “A carefully chosen book inspired by something a friend mentioned weeks ago says ‘I listened.’ A handmade card says ‘I care.’ A last-minute store voucher says ‘I panicked but I tried.’”

Companies give vouchers precisely because they don’t know you. The voucher is the gift of no relationship: efficient, impersonal, safe.

But effort only signals care when effort is still scarce. When AI removes execution effort, the polished output stops signaling anything.

The signal collapses.

So I adapted. Or thought I did.

The New Signal

The shift is big:

Before AI, how you made something mattered. Polish equaled effort equaled love.

After AI, that you thought of someone specifically matters. Selection equals intention equals love.

When AI can execute anything, what signals care isn’t the execution—it’s the decision that someone was worth your attention in the first place.

A handwritten note isn’t better than what AI could write. It’s yours. It carries the signal: I thought of you.

That was my theory, anyway.

The Backfire

The semi-manual cards went out. Clean, simple, personalized. “Dear [Name], Best Wishes”. And I added a short personal email or whatSApp.

Some people were impressed. It looks like “You took the time to write!” they said. The intention had landed.

But most people assumed it was AI-generated anyway.

The clean photo I’d carefully composed just read as generated. Even though I’d thought carefully about each card, even though the intention was real, the execution looked automated.

Call it Presumed Automated: in 2026, anything too nice is AI until proven otherwise.

The burden of proof has flipped.

So I went full manual for a handful of people. Actual pen. Actual paper. My actual messy handwriting. No scale, no efficiency.

Those cards landed best. By far.

Researchers call this the “Handmade Effect”: handmade products are perceived to symbolically “contain love.” People pay up to 17% more for handmade gifts, not because of quality, but because the imperfection signals that a human was present, caring, making decisions.

So in reality, intention isn’t enough. Effort and time still matter. The question isn’t “Is it fake?” or even “Did you mean it?”

The question is now: Can they tell it’s you?

The Wobble

A plain white card. A name in clean script. That looked like AI.

A messy note in actual ink. A signature that trails off the page. A wobble in the letters. That looked human.

The Wobble is the signal now. The imperfection that proves a human hand was here. The rough edge. The uneven spacing. The mistake you didn’t fix.

In 2026, perfection is noise. The wobble is the message.

If you want to signal care in a world of AI-generated polish, you need friction. You need proof of presence.

Go physical. A handwritten note beats any email. A phone call beats any message. Chocolate delivered to a client’s office beats any digital thank-you.

Go inefficient. The very inconvenience becomes the point. Flying to meet someone in person when a video call would “work” signals that they matter more than your time.

Go imperfect. Don’t fix the wobble. Don’t polish the rough edges. Let your humanity show through the cracks.

The things AI can’t counterfeit are the things that now carry weight: your physical presence, your voice, your handwriting, your time spent in the same room.

Over to You

What else can’t AI counterfeit?

What are your signals that still work, gestures, gifts, moments that can only be human. Reply with yours.

I will give full credit for the wobble.

References

• Munich Business School - Gift Wrapping and Signaling Theory

• Psychology Today - The Power of Gifting

• JOM Media - The Warm-Glow Economics of Gift Giving

• Journal of Marketing / ResearchGate - The Handmade Effect: What’s Love Got to Do with It?

These aren’t Luddites. They are leaders who understand the tech’s trajectory, or decision-makers who have ChatGPT or Gemini pinned to their homescreen.

They nod at the capabilities. They acknowledge the progress. Then they go back to Google, Outlook or Powerpoint. To the way they’ve always worked.

Mustafa Suleyman, Microsoft’s AI CEO, tweeted his genuine confusion: “The fact that people are unimpressed that we can have a fluent conversation with a super smart AI that can generate any image/video is mindblowing to me.”

Suleyman grew up playing Snake on a Nokia. Now he oversees machines that write Python and generate 4K video. How could anyone not be impressed?

I found the answer he’s missing. It’s not that users are ungrateful.

It’s that the industry is trading in the wrong currency.

The Currency Mismatch

The tech industry is obsessed with Impressive. The rest of the world only cares about Convincing.

Impressive looks backward. It compares the tool to what came before. It’s the delta between GPT3.5 and GPT-4o. It’s an engineering metric.

Convincing looks sideways. It compares the tool to what I’m using right now. Does this work better than my current solution? Is it more reliable than a junior analyst? Does it solve a problem I actually have?

The gap between these two explains why AI is currently winning the labs but losing the office.

The pushback to Suleyman’s tweet told the real story. One user spent 20 minutes trying to get Copilot to generate a simple Excel list of Terry Pratchett books. It failed, then gave them links they could have Googled in three seconds.

Google Home automation that worked perfectly for years now responds “sweet dreams” when you say “good night” but doesn’t turn off the lights.

GameFAQs, a 30-year-old plain text forums apparently still beats AI for finding game walkthroughs.

In every case, older, simpler, and reliable wins. As one commenter put it: “It would be like trying to sell me on my power drill having a conversation with me.”

Nobody asked for a chatty drill.

They asked for holes in the wall.

The “Impressive Demo” Death Spiral

The very thing that makes AI exciting is what’s killing its adoption: its generality.

A lot of knowledge workers still don’t use LLMs for work. Only about 5% of ChatGPT users pay for premium tiers. Why? Because most people don’t have the “time to figure out how to save time.”

Technology adoption research confirms it: when introduced to new technology, people reference familiar systems to understand its purpose.

That first impression locks in the category.

This creates what I call the Impressive Demo Death Spiral:

The Flashy Pitch: AI is marketed with “creative” demos : making art, writing poems, or generating videos of Sam Altman riding a horse on the moon.

The Toy Categorization: The user’s brain files AI under “Creative Toy” or “Entertainment.”

The Cognitive Lock: When that same user needs to perform a strategic analysis, they don’t think of the “Poem Tool.”

The Low Adoption Trap: Companies see low engagement in “useful” features and double down on even flashier demos to get attention.

The Executive Dismissal: Leaders watch the flashy demos, feel unimpressed, and conclude AI is overhyped — missing the real opportunity entirely.

“A tool that can ‘do anything’ effectively communicates that it does nothing in particular.”

What “Convinced” Users Actually Do

Perplexity recently analyzed hundreds of millions of AI agent interactions . Not chatbot queries, but actual autonomous actions like organizing emails, editing documents, and booking travel.

The data shows that convinced users aren’t doing impressive things. They’re doing boring things.

Specificity Wins: The most common tasks were granular: “filter emails,” “summarize research,” “edit documents.” Even with general-purpose agents capable of anything, users gravitated toward bounded, repeatable workflows.

The Utility Migration: Users who stick with AI agents gradually move away from “chatting” and toward “thinking-heavy” productivity tasks (research, document workflows, career development).

The 57% Rule: More than half of all agent actions fell into “Productivity and Workflow.” Not image generation. Not creative writing. Not the demos.

The executives dismissing AI aren’t wrong about the demos. They’re looking at the wrong map. Flashy demos are underwhelming for real work by design, they prioritize the “wow” over the “how.”

Finding Your “Snake” Game

The Nokia Snake game did exactly one thing, perfectly, every time. You knew what it was for the second you saw it.

That clarity is what’s missing from AI.

If you’ve watched a demo and felt unimpressed, you haven’t made a mistake. You’ve seen through the marketing. The mistake is stopping there, concluding that because the demos don’t convince you, the technology has nothing to offer.

The real opportunity isn’t in finding a machine that can do everything. It’s in identifying the specific, boring tasks where “80% good enough, 10x faster” changes the game.

Where is the “Snake” game hiding in your organization?

Information Triage: Is the team drowning in 60-page PDFs? This is where AI agents show the highest adoption, not because it’s impressive, but because it’s genuinely faster.

Mechanical Workflows: Which tasks involve reformatting data, editing documents, or version management? The Perplexity data shows document workflows as a top use case.

Research Compression: Where is the gap between a question and an informed decision currently costing you days? This is the “thinking-heavy” work where convinced users increasingly deploy AI.

The right question isn’t “Is this model impressive?”

The right question is: “Does this drill make the hole?”

You may also want to read the following article: same failure mode, different angle. This article is about asking the wrong question; that one is about measuring the wrong thing.

References

• Michael Kan, “Microsoft Exec Asks: Why Aren’t More People Impressed With AI?”, PCMag, November 19, 2025

• Reddit discussion: Microsoft AI CEO pushes back against critics, r/Windows11

• Towards Data Science, “The AI Productivity Paradox”, 2024

• Frontiers in AI, “Technology acceptance model and AI”, 2024

• Perplexity, “How People Use AI Agents”, 2025

• Appcues, “Feature Adoption Guide”, 2025

The same pattern is playing out across every function Gen AI touches, it will be more and more difficult to manage what you can’t understand.

IT is the function where adoption is strongest, so let’s look at the data. The productivity narrative seems overwhelming: major 2025 surveys from Atlassian, JetBrains, and DX all report massive gains, with some developers saving over 10 hours a week.

But here’s the surprise: self-assessment is unreliable.

A rigorous 2025 METR study found that experienced developers working on familiar codebases estimated AI increased their productivity by 20%, but objective measurement showed a 19% decrease. The developers themselves couldn’t tell whether AI was helping or hurting.

If experts working with the tools daily can misjudge their own productivity by nearly 40 percentage points, how can a manager who only uses AI occasionally evaluate their team’s estimates?

Such managers aren’t lazy. They are falling into Delegation Blindness: the trap where outsourcing hands-on work creates exactly the information gap that makes coordination impossible.

We have been here before.

The 2009 Playbook

In 2009, Forbes and Google partnered on a whitepaper called “The Rise of the Digital C-Suite.” They surveyed hundreds of executives on how they adapted to the internet, the mobile and the role of search.

The Losers: Generation Wang

The report identified executives over 50, the “Generation Wang”, who treated the internet as a task to be delegated. 40% never used their mobile devices for work. A significant portion operated on summaries prepared by others. Because they never saw raw information, they couldn’t question flawed assumptions. Their refusal to adapt went from a status symbol to a sign of incompetence.

The Winners: Generation Netscape

The executives who thrived were different. 51% preferred to locate information themselves and 35% used their mobile daily for work. Why? Because they understood that using Google wasn’t secretarial, it was strategic.

Rob Shaddock, CTO of Tyco Electronics, explained the difference: with online search, you could “go as deeply as you want” in whatever direction you needed. “Newspapers and print are static. Often an article leaves you with just so many additional questions but no further options.” The report identified a rising “Generation Netscape,” leaders who were “orders of magnitude more willing to try new ways to access information.” Executives who delegated got polished summaries. Executives who searched got intuition.

Why Delegation Erodes Authority

The 2009 lesson was clear: delegation creates blindness. But AI doesn’t just create blindness; it dismantles the manager’s authority.

Harvard Business School researchers discovered that AI creates a “jagged frontier.” Tasks that look equally difficult sit on opposite sides of the line: AI handles one brilliantly (e.g., making sense of a complex data) and fails at the other (e.g., looking up a number in a table).

Navigating this requires Frontier Fluency, aka knowing where the boundaries sit. You only get this by bumping into them. Managers who delegate lose the two pillars that make management possible:

1. The Knowledge Inversion In the past, seniority equaled expertise. AI breaks this. Erik Brynjolfsson found that AI raises the performance of new workers dramatically, closing the gap between junior and senior outputs instantly. Simultaneously, your team is learning the tool’s nuance daily (“The model hallucinates on revenue recognition”). You aren’t. The knowledge flows upward, but the org chart doesn’t. You can no longer mentor them, because they know more than you about the primary lever of their work.

2. The Judgment Collapse You are discovering the frontier for your occasional tasks, not their specialized work. Your mental model of what is “easy” or “risky” is calibrated to the wrong terrain. You approve timelines and allocate headcount based on a map that doesn’t match their territory. Your team knows it. How long before everyone else does?

“But surely,” you’re thinking, “leaders can’t do everything themselves.”

Of course. The point isn’t to do your team’s work. It’s to maintain enough hands-on familiarity to coordinate it effectively. You don’t need to match their output; you need to build intuition about where complexity actually lives.

2009 Lessons for 2025

The executives who thrived in 2009 adopted specific practices. Here are their equivalents for the AI era.

Practice 1: The Hands-On Tax

Frontier Fluency demands payment. Block time weekly to test the boundaries relevant to your department. Finance directors must discover what needs verification; Marketers must see where the “creative” algorithm gets repetitive. It’s not extra work; it’s the minimum investment to remain effective.

Practice 2: Dual Visibility

Mandate that presentations include raw evidence such as screenshots of prompts, settings, and raw data. This prevents “polished summary” blindness and creates a shared vocabulary.

Practice 3: The Question Gap

These four questions buy you visibility into the practices of your team:

1. “How many steps did you use to produce this ? What did you try that didn’t work?” (Forces them to reveal the frontier)

2. “Did you verify this and how?” (Surfaces whether a quality process exists)

3. “What changed in the process & tools since last month?” (Flags evolution you’re missing)

4. “If you were me, what would worry you?” (Invites them to do your risk assessment)

The Choice Is the Same

Fifteen years ago, managers faced a choice: learn to use the internet directly, or become a bottleneck. The ones who insisted on having research delegated became relics.

AI is not a task to be delegated. It is the fundamental information layer of modern business.

In every organization right now, there is a manager who reminds colleagues of the “Generation Wang” executives from 2009: the ones who asked assistants to “look things up on the computer,” and couldn’t understand why their judgment kept missing the mark.

In 2009, everyone knew who those people were. In 2025, the question is whether you will be one of them.

References

• Atlassian (2025). “State of Developer Experience Report 2025.”

• Brynjolfsson, E., Li, D., & Raymond, L.R. (2023). “Generative AI at Work.” NBER Working Paper 31161.

• Dell’Acqua, F. et al. (2023). “Navigating the Jagged Technological Frontier.” Harvard Business School.

• Forbes Insights & Google (2009). “The Rise of the Digital C-Suite.”

• METR (2025). “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity.”

The new mandate for marketers is to prepare for this world. Structure your product data. Expose your APIs. Make yourself “agent-friendly.” The race for “GEO,” or Generative Engine Optimization - essentially SEO 2.0 - is on, and everyone wants to be the first result on the algorithmic shelf.

But this rush to appease the bots ignores a fundamental question: What happens when the messy, inefficient, and deeply human process of choosing is automated away?

The Frictions That Create Desire

Let’s be honest. Not all shopping needs to be a meaningful journey.

Nobody wants to “discover” toilet paper on a Tuesday night. When you’re out of laundry detergent, efficiency is the value proposition. Your Amazon “subscribe and save” purchases are wonderful plumbing.

But agents treat everything like a feature-comparison problem. They don’t distinguish between toothpaste and a winter coat, between soap and a wedding dress.

Think about your last important purchase, whether a piece of furniture, headphones, some clothes for a job interview. Did you efficiently compare specifications and execute the optimal choice? Or did you browse, watch videos, read contradictory reviews, ask friends, change your mind, and finally buy something different than you started looking for?

This process, pretty inefficient, very human, is how we build our taste and preferences. It’s how we learn what we actually want, not just what we think we need. You went looking for running shoes and discovered you care about it being recyclable. Or that minimal design appeals to you. Or that you’re willing to pay more for a brand that sponsors athletes you admire.

Shopping, at its best, is a discovery process. AI agents, in their quest for optimization, threaten to compress that journey directly to checkout. Clean, fast, and hollow.

The Limits of the Curation Engine

But what if the agents aren’t so simple? Maybe they will evolve into sophisticated “taste-makers.” An agent with access to your streaming history, social media, and personal calendar could learn your aesthetic and values. It wouldn’t just find “running shoes”; it would find the specific, sustainably-made shoe from an indie brand that aligns with your identity.

In this vision, the agent doesn’t kill discovery; it elevates it into hyper-personalized curation. However, this optimistic view runs into three fundamental walls.

First, it assumes a level of data sharing that is far from guaranteed. A true taste-making AI requires near-total access to your digital life. Without your complete, uncensored history of thoughts, desires, and behaviors, its promise of perfect curation falls apart. In an age of increasing privacy concerns, the prerequisite for this “smart” agent (our total transparency) is its biggest vulnerability.

Second, even with perfect data, it creates a prison of the past. A taste-making agent works by analyzing your history to predict your future. It can give you more of what you already like, reinforcing your current identity. But true discovery often comes from serendipity : the happy accident that introduces you to something you never knew you could love. The agent creates a perfect filter bubble of one, a beautifully decorated prison for your taste, preventing the random friction that helps you evolve.

Third, it automates meaning. Meaning isn’t a destination; it’s a byproduct of effort. The satisfaction of finding the perfect item is tied to the work you put in, the research, the deliberation, the hunt. We value what we struggle for. By making discovery effortless, the agent devalues the outcome. It gives you the answer without letting you wrestle with the question, stripping the process of its power.

The Commodification Engine

So whether the agent is simple or sophisticated, it pushes brands towards the same economic black hole: perfect competition.

Marketers are being told to provide structured data, real-time pricing APIs, and detailed specifications. In doing so, you are inviting your own commodification. Perfect competition exists when buyers have complete information and products are perfectly comparable. In such a market, prices converge toward marginal cost, and nobody makes money.

“Agent-friendly” commerce removes the buffers that protected you. When your product is reduced to a row in a comparison matrix such as price, features, ESG score, delivery time, you’re no longer a brand. You’re a data point. And data points compete on price.

Consider what happened with search engine optimization. Everyone optimizing for the same keywords. Very costly and hardly a winning playbook. Just a tax to be findable. Now imagine that across every product attribute. Generic “running shoes” fed into an agent returns 50 options ranked by algorithmic score. You might win the transaction by having the best price-to-feature ratio. But you’ve won a race to the bottom.

This is the central trap. You don’t want to be just in the agent’s comparison matrix. You want to be the specific thing a person asks for by name before the agent even starts its search.

The Involvement Ladder is a Matter of Context

Every purchase sits somewhere on what we might call an “Involvement Ladder”—from low (automatic, efficiency-driven) to high (emotional, identity-driven). The default setting of an AI shopping agent is to yank every product down to the bottom rung, applying cold logic of feature comparison to everything.

But crucially, a product’s position on this ladder isn’t fixed; it’s determined by context and, more importantly, by brand.

Toilet paper is a low-involvement restock. But if you’re hosting a dinner party to impress, suddenly you may be buying the luxury, quilted, lotion-infused version. The context turned it into a high-involvement choice.

Jewelry seems like a classic high-involvement purchase. But for an ultra-wealthy individual buying a simple gift, it might be a quick, low-involvement task delegated to an assistant or an agent.

Coffee can be a low-involvement caffeine delivery system. Or it can be a high-involvement ritual of craft, community, and lifestyle. The difference between an espresso at next coffee machine and beans from a roaster you follow on Instagram.

The new battle for marketers is to win the context and move your brand up the ladder through meaning, not features.

The Great Paradox: More Automation Requires More Humanity

This brings us to the paradox at the heart of agentic commerce. There is this idea that AI would make marketing more technical, more data-driven, more algorithmic.

The opposite is true.

When the transaction layer becomes automated, the only durable competitive advantage is winning the preference before the agent is even activated. In other words, branding.

Agents can execute purchases. They cannot create desire.

There are two distinct layers:

Preference Formation (Pre-Agent Space): This is where brands win or lose. It’s cultural, emotional, and identity-driven. It’s the layer where someone decides “I want Adidas” before they ever ask their agent to buy shoes.

Transaction Execution (Agent Space): This is where agents operate. It’s important as you need to be findable and functional. But if you’re only competing here, you’re competing in the commodification matrix.

Look at Liquid Death. They took water—the ultimate commodity—and made it identity-driven through irreverent branding and cultural positioning. Now people ask for Liquid Death specifically. The product is water. The brand is meaning.

Or consider Patagonia. An agent comparing technical specifications would find a dozen jackets with similar warmth-to-weight ratios at lower prices. But Patagonia customers are buying into environmental values, durability as philosophy, and a community of like-minded people, not specs. The brand exists in the pre-agent space, built through storytelling, activism, and shared identity.

The more automated commerce becomes, the more brands will need to invest in the least automatable thing: human connection and meaning. To compete in the age of algorithms, brands must become more human, not less.

The New Brand Roadmap: Dual-Track Strategy

Now brands are competing on two tracks simultaneously. And can’t afford to ignore either one.

Track 1: Agent Optimization (Table Stakes)

This is about not losing. You must be shoppable by agents:

• Structure your product data properly

• Provide real-time inventory and pricing

• Ensure your APIs work flawlessly

• Make checkout frictionless

If you fail here, you’re invisible. The agent won’t even consider you. But being perfect on Track 1 doesn’t make you win. It just keeps you in the game.

Track 2: Desire Creation (The Only Moat)

This is where you actually win. You must be specifically requested by name:

• Build cultural relevance through creators and influencers

• Invest in community and belonging

• Create experiences that generate attachment

• Tell stories that make your brand mean something

• Cultivate taste-makers who advocate for you

Track 1 determines whether the agent can find you.

Track 2 determines whether anyone cares to ask for you specifically.

If your product is genuinely low-involvement and you cannot elevate it, accept it. Optimize everything for agents. Automate your supply chain. Compete on price and distribution. Prepare for thin margins. This is a viable strategy—if you go all in and run the tightest ship possible. But be honest: if you’re a commodity, you’re in a race to the bottom. The winner will be whoever can operate most efficiently at the thinnest margins. Can you be that company?

The War Ahead

The death of shopping isn’t the death of marketing. On the contrary.

Agents will automate transactions. But they cannot automate desire. They cannot manufacture meaning. They cannot create the cultural context that makes someone want a specific brand.

The irony is perfect. Structured data won’t save you. Perfect APIs won’t save you. Being “agent-friendly” alone won’t save you.

Being wanted will.

When someone cares enough to say “get me those Adidas.” instead of “find me running shoes.”

That one word, your brand, is the only moat that matters.

Everything else is just plumbing.

Optimize for agents to stay visible. Build meaning for humans to stay valuable.

“The article argues that generative AI isn’t a productivity tool that saves time, it’s a”depth machine” that expands what’s possible but often consumes more time. While AI can rapidly generate drafts, users spend far longer verifying, editing, and refining. (…).”

Technically correct. Utterly forgettable. And so useless.

Here’s the same article, summarized differently:

“At first glance, the promise feels irresistible: lightning-fast drafts, instant summaries, ideas on tap. One author recalls drafting an entire article in ten minutes—then spending five hours fact-checking, restructuring, and polishing. Across coding, finance, and writing, the same pattern emerges: AI accelerates starts, but demands deeper human effort to finish.(…)”

Both summaries are accurate.

Only one has a chance to stick in your brain.

The Specificity Cure: How to Break AI’s Boring Defaults

Most AI summaries read like they were written by an earnest but boring intern who’s terrified of making a mistake.

When you prompt “summarize this,” the AI defaults to a failsafe mode. It optimizes not for impact, but for universal comprehension and inoffensiveness. It systematically strips out the very things that make information memorable: the surprising numbers, the pointed quotes, the vivid details.

Think about the last meeting you attended. What do you actually remember? Not the bulleted agenda. You remember the the live demo that crashed spectacularly (poke Meta), the revenue number that made everyone stop typing.

You remember the moments that broke the pattern.

An AI, by default, is trained to erase those anomalies.

Large language models have learned from billions of documents that formal, “authoritative” writing often hedges claims and avoids sharp specifics. When uncertain, they retreat to the Wikipedia voice—that peculiar tone that’s simultaneously knowledgeable and utterly forgettable.

But there’s a simple command that breaks this cycle: demand specifics.

• “a significant improvement” becomes “a 47% increase in user retention.”

• “experts say” becomes “Dr. Geoffrey Hinton, the godfather of AI, warned…”

One vivid detail is worth ten abstract statements.

Our brains evolved to remember the specific—the red berry on the path that killed a tribe member, not the general concept of “potentially toxic flora.” Specificity is a survival mechanism. It creates texture and anchors an idea in reality.

A summary isn’t just compressed text; it’s a cognitive tool engineered for a purpose.

A board brief needs to drive a decision. A research summary needs to convey intellectual structure. A conference recap needs to be memorable.

Different purposes require different summaries.

The Patterns that Work

Here are six field-tested prompting patterns, each engineered for a specific purpose:

Action-oriented

Bluf Executive Brief (Bottom line up Front)

When: Board updates, decision memos.

(some context like your role or company details)
Summarize for a time-pressed executive in ≤200 words. 
Use BLUF (1–2 sentences), then 5 bullets: Impact, Risks, Options, Cost/Resourcing, Timeline. 
End with one recommendation. Do not invent facts. Drop in at least one concrete number or risk in the bullets; keep sentences short so the BLUF lands.
Source: 
(paste source)

Key-point Bullets (Meetings & Transcripts)

When: Meetings, town halls, lengthy calls.

(some context like your role or company details)
Summarize the following transcript in ≤200 words, grouped into 4 sections: 
Decisions, Actions, Owners, Deadlines. 
Use concise bullets only. Anchor bullets with specifics (owner names, real dates). One vivid detail per section keeps it from reading like boilerplate.
Source: <<<TEXT>>> 

Information-oriented

Abstract (Academic Style)

When to use: Research papers, technical briefs, policy notes.

(some context)
Write a structured abstract in ≤200 words with 4 parts: 
Problem, Method, Findings, Limitations. 
Do not invent details. 
Source: <<<TEXT>>> 

Read more: UNC writing center—abstracts

Maximum Density Summary

When to use: Newsletter blurbs, briefing packs.

(some context)
Produce a summary in ≤200 words. 
Step 1: Write a very sparse version. 
Step 2: Rewrite at the same length, adding the most salient missing entities (3–5). Include at least one vivid detail (stat, quote...) 
Step 3: Repeat until dense but concise. Output the final version only. 
Source: <<<TEXT>>> 

Read more: Chain-of-Density study

Narrative-oriented

Journalistic Summaries

When to use: Exec newsletters, strategy notes.

(some context like your role or company details)
Write a news-style brief in ≤150 words: 
1) A 1–2 sentence lead with the most newsworthy fact. 
2) A nut graf that explains why it matters and why now. 
3) 3–5 lines of key details. 
Include at least one stat or quote from the text. 
Source: <<<TEXT>>> 

Read more: Poynter on nut grafs

Narrative Summaries

When to use: Conference/site-visit recaps (CES, Davos), launches.

(some context like your role or company details)
Write a vivid narrative summary in ≤250 words. 
1) Open with concrete scene-setting, if possible
2) Present 2–3 themes. 
3) Weave multiple vivid details: quotes, stats, unexpected facts to create memorability
4) End with a clear takeaway or implication
Use only scene and details from the source. Don’t add facts. 
Source: <<<TEXT>>> 

Let’s try again, using my productivity article.

Interesting Summaries on Productivity

The Executive Brief (When Decisions Matter)

Generative AI isn’t a time-saving tool—it amplifies capability at the cost of more time. Leaders must set expectations: AI enables deeper, more ambitious work, but not faster work.

Impact: AI accelerates drafting and ideation but shifts effort into review, iteration, and fact-checking. Net effect: more output, but not less time spent. (…)

The Narrative Summary (When Memory Matters)

At first glance, the promise feels irresistible: lightning-fast drafts, instant summaries, ideas on tap. One author recalls drafting an entire article in ten minutes—then spending five hours fact-checking, restructuring, and polishing. Across coding, finance, and writing, the same pattern emerges: AI accelerates starts, but demands deeper human effort to finish.

Two themes dominate. The first is the productivity trap. Just as “Inbox Zero” paradoxically leads to more emails, AI-accelerated work multiplies tasks instead of reducing them. (…)

Bonus Tips

Lost in the middle: very long inputs may confuse the LLM. For critical work, chunk your piece in sub pieces then merge. (Liu et al., 2023).

Hallucinations: compression can add facts; say “Do not add information not in the source” is a remediation you may use in the prompt but even then, double check the claims.

Keep in Mind

The formula for a memorable summary, then, is simple:

• Start with the audience need

• Choose a pattern that fits

• Demand specific details that create texture

• Iterate until it feels memorable, not just correct

We thought AI would solve information overload. But replacing it with forgettable summaries isn’t a solution.

Every AI summary you create is a choice.

The next time you prompt an AI, ask yourself: will anyone remember this tomorrow?

If not, you’re not done prompting.

The Intelligence Fabric uncovers how AI actually works in your life and business—the invisible mechanisms that shape every interaction. It delivers practical insights for executives planning AI strategy and individuals trying to understand their changing relationship with technology. Subscribe to understand and control your AI future.

You ask the same AI to write a marketing strategy from scratch for company X. It hallucinates company initiatives that don’t exist and invents statistics with decimal-point precision. It feels like a liability.

It’s the same machine. The difference is the mode.

One function reads patterns from a reality you provide. The other writes fictions based on probability.

The core problem of the AI era is that most users can’t tell which mode they’re in, leaving them stranded between breakthrough and bullshit.

The AI’s Split Personality

Every Large Language Model has a split personality. We’ve been treating it as a single entity, which is why it seems brilliant one moment and delusional the next. The solution is to recognize the two machines hiding inside the one.

• Reading Mode: The AI acts as a superhuman analyst. It processes, synthesizes, and finds patterns within a specific context you provide. It operates on what exists.

• Writing Mode: The AI acts as a probabilistic storyteller. It generates new content based on the statistical patterns of its vast, chaotic training data. It creates what could plausibly exist.

Once you see this distinction, the entire industry’s behavior snaps into focus.

This explains why the gold standard for useful AI in the enterprise is RAG (Retrieval-Augmented Generation), a technical term for a simple command: “Read these documents first, then write.” Microsoft’s Copilot reads your files. Perplexity reads the live web.

Every successful AI tool is a tacit admission that the machine must be forced into reading mode to be reliable. As Shopify CEO Tobi Lütke says, the new frontier isn’t prompt engineering; it’s “context engineering”, the art of providing the right reality for the AI to read.

The Hallucination Engine

Hallucination isn’t a bug in Writing Mode. It’s the very engine of creativity we demand from it.

The mechanism is a parameter called “temperature.” which can be set between 0 and 1.

At low temperature (T≈0.2), the model produces quite predictable text—perfect for Reading Mode summaries. But at high temperature (T≈0.8), the setting physicist Stephen Wolfram identifies as optimal for creative writing, the model is encouraged to make surprising lexical leaps.

In Writing Mode, this high-temperature state is a fountain of both genius and gibberish. It generates novel metaphors and original ideas. It also generates plausible-sounding “facts” that are pure invention. They are products of the exact same process. You cannot have one without the other.

Research from the University of Washington confirms this is not fixable. When they “aligned” base models for safety—tuning them to reduce hallucinations—the process systematically lobotomized their creativity. The safe AI couldn’t write good poetry or even generate random numbers effectively. This is why GPT-4 can score in the top 1% on creativity tests while simultaneously inventing legal precedents. It’s why Stanford researchers found LLMs can generate ideas judged “significantly more novel than those from human experts,” but are incapable of recognizing their own fabrications.

So what do we do? Midjourney co-founder David Holz offers the best metaphor. After generating 40,000 images, he felt he was “drowning” in a “torrent of water.” His insight: “We don’t try to get rid of water because it’s dangerous. We build dams. We build pipes. We build irrigation.” Trying to eliminate hallucinations from Writing Mode is like trying to make water not wet.

Mastering the Mode Spectrum

The path to mastery isn’t just recognizing Reading versus Writing Mode, it’s understanding the full spectrum of operations within each.

In any five-minute conversation with AI, you might need:

• Extraction “What exactly does the contract say about termination?”

• Synthesis: “Find the common themes across these customer interviews”

• Critical Analysis: “What are the weaknesses in this business proposal?”

• Creative Extension: “Generate ten campaign ideas based on these brand values”

The result of switching between these demands is often mode collapse, like a radio stuck between stations. You ask for facts, then ideas, then analysis, but the machine is still operating at the temperature and context of your first question.

The consequences of mode confusion are everywhere. When consultant Claudia Ng asked an AI to analyze a market, it “fabricated an entire analysis” with fake customer comments. She thought she was in Extraction Mode, but the AI had drifted into Creative Extension. Meanwhile, artist Caroline Zeller deliberately pushes into pure Creation Mode: “I want to go beyond what I want,” using AI to “reveal images that are in the unconscious.”

One user stumbled into the wrong mode and got fiction. The other chose her mode and found art.

The Mode-Conscious Approach:

1. Declare your mode explicitly: Don’t just ask questions—frame the operation.

   • “Extract only the facts from these documents, add nothing”

   • “Critically evaluate this proposal as a skeptical investor would”

   • “Creatively explore what these themes might suggest for our brand”

2. Reset between mode shifts: When switching from creative brainstorming to factual analysis, start fresh. The residual context from Creation Mode will contaminate your Extraction Mode.

3. If you have a tool which allows you to set temperature, match it to task:

   • Low (0.2): Legal documents, data extraction, summaries

   • Medium (0.5): Strategic analysis, critical thinking, pattern finding

   • High (0.8): Brainstorming, creative writing, possibility exploration

4. Feed appropriate context for each mode:

   • Extraction needs comprehensive source documents

   • Analysis needs comparative examples and frameworks

   • Creation needs inspirational seeds, not restrictive rules

The Context Architect’s Toolkit

The individuals and companies mastering AI aren’t building better prompts. They’re designing information environments that keep AI in the appropriate mode for each task. They explicitly declare which operation they need and reset contexts between shifts. They understand that you don’t eliminate the flood; you build different channels for different purposes.

Gen AI isn’t one tool but a spectrum of them, packaged in a single interface. Master the mode spectrum, and you transform an unreliable oracle into a suite of powerful, specialized instruments.

The machine has always had multiple personalities. The breakthrough is learning to call forth the right one.

That shrug is the story of software’s future. The magic of conjuring an app in seconds is real. So are the limits.

We’re entering an era where software is evaporating. An app can be “invoked” on demand. You watch it perform, and let it vanish. Some call it “ephemeral software”, others predict the “death of SaaS”. Even Satya Nadella declared traditional software models obsolete.

But they’re missing the second half of the equation. Evaporation is always followed by condensation, and the condensation of power that’s coming will be far more dramatic than the disappearance of any single app.

The Economics of Evaporation

The case for invokable software starts with cost curves. Frontier model’s cost are dropping constantly, while accuracy climbs. When the marginal cost of creating a working app approaches zero, the business model for pre-built software buckles, and the great evaporation begins.

As Bain & Company put it: “In three years, any routine, rules-based digital task could move from ‘human plus app’ to ‘AI agent plus API.’” Not interface. Not app. Just API. We’re already seeing this in specialized fields. Vesta and Casca report 10X productivity gains in loan origination with 90% less manual work. Tennr automates patient care coordination end-to-end. These aren’t apps in the traditional sense. They’re incantations: describe what you want, results appear.

The first place we’ll see this mass evaporation is the sea of digital clutter on our phones. Apple’s App Store boasts two million apps, yet the average user has 80 installed, but only 9 used daily That’s 71 digital zombies, each demanding updates and permissions for problems you barely have anymore.

This is the low-hanging fruit. Why browse for a photo editor when you can say, “remove the background and add a vintage filter”? Why maintain a dozen single-purpose utilities—a PDF converter, a document scanner, a timezone tracker—when they could all be invoked with a single sentence? This entire ecosystem of simple apps is waiting to be evaporated.

Where Reality Bites back

But this dream of infinite, on-demand software crashes into a hard wall of human reality. For any task that truly matters, “good enough” is never good enough.

First, there are the hard limits of trust and accountability. As Cassie Kozyrkov stresses, enterprises need systems that “provably solve a specific problem provably well.” That word appears twice for a reason. You can’t prove reliability for software that just materialized.

Accountability matters. If Salesforce fails, you call support, escalate, even sue. If invokable software fails—who’s liable? The model? The platform? You, for describing it poorly?

Security makes this sharper. Wiz research found 40% of AI-generated database queries contained SQL injection vulnerabilities. AI often leaves API keys hardcoded, security checks in the wrong place, access controls wide open. Fine for removing photo backgrounds. Catastrophic for your company secrets.

Even if the code was perfect, a deeper challenge remains: our own minds. Traditional software trains us through repetition. Cmd+S is save. Formatting tools are on the right. This isn’t trivial—it’s how expertise forms. The spatial and muscle memory of a tool are fundamental. Ephemeral software erases this, making every interaction a first interaction.

Lastly, most of us don’t know exactly what we want until we see it. We use imprecise language (“make it pop”) and discover our needs through interaction, not abstract specification. The constraints of Photoshop are what make it usable; infinite possibility is a recipe for paralysis.

Infrastructure is the Ultimate Moat

Even more fundamentally, a certain class of applications will endure. They're not really apps at all, they're infrastructure.

Infrastructure.

TikTok isn’t just an app; it’s a cultural infrastructure, a shared protocol for how a generation communicates. Microsoft Excel isn’t just a piece of software; it’s the default language of business data. These platforms provide something ephemeral software can’t: shared context and a stable foundation.

This is where the power condenses. As software evaporates into function calls, the platforms that survive aren’t just apps; they are the stages upon which ephemeral software performs. As Bain notes, whoever controls how “invoice.bot talks to payment.bot” controls commerce.

Microsoft sees this with perfect clarity. Satya Nadella’s strategy isn’t about killing SaaS. It’s about owning the invocation layer, the operating system for on-demand AI. If all ephemeral software runs through Microsoft’s Copilot, then Microsoft becomes the indispensable intermediary for nearly all digital work. Own that, and you own everything.

The Stratified Future

This leads to a new, stratified software landscape:

Layer 1: Infrastructure Platforms (4–5 per person)

• The foundational layers: Social Networks, Copilots, Google Workspace.

• They provide the shared context and protocols. Their power grows immensely as they become the stages where all other software performs.

Layer 2: Professional Fortresses (10–20 per worker)

• The high-stakes tools: Photoshop (?), Salesforce (?), specialized engineering software.

• They survive by providing reliability, accountability, and deep, domain-specific expertise that can’t be casually replicated. They integrate AI, but remain stable, auditable, and insurable.

Layer 3: Ephemeral Apps (infinite)

• The mist: “Split this PDF,” “Summarize this meeting,” “Draft a reply to this email.” These are single-use, disposable, and experimental functions invoked on demand.

The zombies on your phone die. The professional tools become fortresses. And the giants who own the foundation dig their moats deeper than ever.

Navigating the New Terrain

This stratification isn’t academic—it’s a map for what’s coming.

For your personal stack: Stop collecting apps. Pick your 4-5 foundation layers carefully—they’re not just tools anymore, they’re your digital citizenship. The question: who controls the invocation layer, and can you leave if you need to?

For your organization: The build-vs-buy equation is scrambling. Nobody knows exactly how yet, but the middle ground—those dozens of decent-but-not-critical tools—evaporates first. What remains: core systems you can’t afford to lose control of, and ephemeral functions you’ll invoke as needed. The hard part is knowing what’s what.

The real strategic question: If you’re not building infrastructure or professional fortresses, what’s left? Your value moves up the stack—from having the right tools to knowing what outcomes to orchestrate. From owning software to owning judgment about when precision matters and when “good enough” is perfect. The winners won’t be those with the best apps, but those who know exactly what to invoke, when, and why.

The ability to precisely describe what you want becomes the new digital literacy. Not prompt engineering—that’s temporary. But the deeper skill of knowing when to accept 80% automation and when to demand the last 20%.

Software Feudalism

When software is something you invoke rather than own, power concentrates around the platforms that translate your intent into action. They decide what’s possible, what’s safe, and what’s profitable. Think of the current cloud oligopoly, but supercharged and moved all the way up to the application layer.

We are not moving toward software democracy. We’re drifting into a new kind of software feudalism, a world where a few control the invocation layer, and the rest are simply tenants working on their land.

I think back to my kids vibe coding. The magic they felt wasn’t actually in creating a game, but in the possibility of making something theirs.

The last install is coming. What matters now is not which app you’ll conjure next, but whose magic you’ll be using when it appears out of thin air.

ChatGpt Va-t-il Tuer L’app Store?

L’IA peut désormais coder n’importe quelle application à la volée, faisant potentiellement disparaître le besoin d’avoir des applications dédiées. Mais alors que le logiciel s’évapore, le pouvoir se concentre entre les mains de quelques gagnants.

Il y a un mois, comme je l’ai raconté dans un précédent article, j’ai “vibe codé” un jeu vidéo avec mes enfants. La séance était amusante, sans aucun doute. Mais le résultat final? Franchement… Sympa mais pas fou non plus. Verdict: ” Mais pourquoi on n’achète pas juste un jeu? “ Cette indifférence résume toute la tension à l’oeuvre aujourd’hui: la magie de créer une application en quelques secondes est bien réelle. Mais ses limites aussi.

Nous entrons dans une ère où le logiciel s’évapore. Une application peut être ” invoquée ” à la demande, utilisée, puis disparaître. Certains parlent de « logiciels éphémères », d’autres prédisent « la mort du SaaS ». Même Satya Nadella a déclaré que les modèles traditionnels de logiciels étaient obsolètes.

Mais ils oublient la seconde partie de l’équation. Après l’évaporation vient toujours la condensation, et la concentration de pouvoir qui s’annonce … spectaculaire.

Le Business Model De l’évaporation

L’argument en faveur des logiciels créés “à la volée” repose sur les courbes de coûts. Le coût des modèles d’IA de pointe ne cesse de baisser, tandis que leur précision s’améliore. Lorsque le coût marginal de création d’une application fonctionnelle tend vers zéro, le modèle économique des logiciels préconstruits vacille, et la grande évaporation commence.

Comme le souligne Bain & Company: ” Dans trois ans, toute tâche numérique routinière et basée sur des règles pourrait passer de ‘l’humain + l’application’ à ‘l’agent IA + l’API’. ” Plus d’interface. Plus d’application. Juste une API. On observe déjà ce phénomène dans des domaines spécialisés. Vesta et Casca rapportent des gains de productivité multipliés par 10 dans l’octroi de prêts, avec 90 % de travail manuel en moins. Tennr automatise entièrement la coordination des soins aux patients. Ces outils ne sont pas des applications au sens traditionnel. Ce sont des incantations: décrivez ce que vous voulez, et les résultats apparaissent.

Le premier endroit où cette évaporation massive sera nos téléphones. L’App Store d’Apple compte deux millions d’applications, mais l’utilisateur moyen en a 80 installées, et n’en utilise que 9 quotidiennement. Soit 71 zombies numériques, chacun exigeant des mises à jour alors même que vous n’en avez plus besoin.

Pourquoi chercher un éditeur de photos dans l’app store quand on peut dire: ” Supprime l’arrière-plan et ajoute un filtre vintage “? Pourquoi garder une dizaine d’utilitaires à usage unique–un convertisseur PDF, un scanner de documents, un suivi des fuseaux horaires–alors qu’ils pourraient tous être invoqués par une simple phrase? Tout cet écosystème d’applications simples n’attend qu’à s’évaporer.

Quand Le Rêve Rencontre la Réalité

Mais ce rêve d’un logiciel infini et à la demande se heurte au mur du réel.

D’abord, il y a les sujets de confiance et de responsabilité. Comme le souligne Cassie Kozyrkov, les entreprises ont besoin de systèmes qui ” résolvent de manière fiable un problème spécifique, et le résolvent bien “. On ne peut pas prouver la fiabilité d’un logiciel qui apparaît par magie.

Par ailleurs, si Salesforce tombe en panne, vous appelez le support, escaladez, voire portez plainte. Mais si un logiciel “invoqué” échoue… qui est responsable? Le modèle? La plateforme? Vous, pour l’avoir mal invoqué?

La cyber sécurité finit de consolider le besoin de logiciels “ traditionnels ”. Une étude de Wiz a révélé que 40 % des requêtes générées par l’IA contenaient des vulnérabilités d’injection SQL, c’est à dire présentait des failles de sécurité. L’IA ne protège pas assez les clés de sécurité, les vérifications sont mal organisées, des contrôles d’accès grands ouverts. C’est ok pour supprimer un arrière-plan photo. Catastrophique pour les secrets de votre entreprise.

Même si le code était parfait, un défi plus profond persiste: nos propres habitudes. Les logiciels traditionnels nous forment par la répétition. ” Ctrl+S “ signifie ” enregistrer “. Les outils de mise en forme sont à droite. Ce n’est pas anodin: c’est ainsi que se construit l’expertise. La mémoire spatiale et musculaire d’un outil sont fondamentales. Le logiciel éphémère efface tout cela, faisant de chaque interaction la première.

Enfin, la plupart d’entre nous ne savent pas exactement décrire ce qu’ils veulent avant de le voir. Nous utilisons un langage imprécis ” Rends ça plus dynamique “ et découvrons nos besoins par l’interaction, pas par une spécification abstraite. Les icones prédéterminées de Photoshop sont ce qui le rendent utilisable; laisser ouvert l infini des possibles c’est la garantie de paralysie.

L’infrastructure, cette Forteresse ultime

Au delà des questions de confiance et d’UX, une certaine classe d'applications va rester. Celles qui ne sont pas vraiment des applications, mais en réalité des infrastructures.

TikTok n’est pas qu’une application: c’est une infrastructure culturelle, un protocole partagé pour la communication d’une génération. Microsoft Excel n’est pas qu’un logiciel: c’est le langage par défaut des données commerciales. Ces plateformes offrent ce que le logiciel éphémère ne peut pas: un contexte partagé et une base stable.

C’est là que le pouvoir se concentre. À mesure que le logiciel s’évapore en appels de fonction, les plateformes qui survivent ne sont plus de simples applications: ce sont les scènes sur lesquelles ces logiciels éphémères se matérialisent.

Microsoft l’a parfaitement compris. La stratégie de Satya Nadella ne consiste pas à tuer le SaaS, mais à posséder la couche d’invocation, le système d’exploitation de l’IA à la demande. Si tout le logiciel éphémère passe par Copilot de Microsoft, alors Microsoft devient l’intermédiaire indispensable pour presque tout travail numérique. Contrôlez cela, et vous contrôlez tout.

Un Futur stratifié

Tout cela conduit à un nouveau paysage logiciel, stratifié:

Couche 1: Les plateformes d’infrastructure (4 à 5 par personne)

• Les fondations: réseaux sociaux, Copilots, Google Workspace, ChatGPT …

• Elles fournissent le contexte partagé et les protocoles. Leur pouvoir croît énormément, car elles deviennent les scènes où tout autre logiciel se produit.

Couche 2: Les forteresses professionnelles (10 à 20 par travailleur)

• Les outils critiques: Photoshop (?), Salesforce (?), logiciels d’ingénierie spécialisés.

• Ils survivent en offrant fiabilité, responsabilité et une expertise spécifique approfondie, impossible à reproduire à la légère. Ils intègrent l’IA, mais restent stables, auditable et assurables.

Couche 3: Les apps éphémères (infinies)

• Le ad hoc: ” Divise ce PDF “, ” Résume cette réunion “, ” Rédige une réponse à cet email “ Ce sont des fonctions jetables, expérimentales, invoquées à la demande.

Les petites apps de votre téléphone meurent. Les outils professionnels deviennent des forteresses. Et les géants qui possèdent l’infrastructure renforcent encore leurs murs.

La Nouvelle Carte Du Monde Du Logiciel

Cette stratification n’est pas théorique, les conséquences pratiques sont multiples.

Pour vos outils personnels: Arrêtez de collectionner les apps. Choisissez vos 4-5 apps fondamentales que vous allez maitriser. La question: qui contrôle la couche d’invocation, d’intelligence, de personnalisation, et est ce que vous pouvez en changer si vous le désirez?

Pour votre organisation: L’équation “Build Vs Buy” est bouleversée. Personne ne sait encore exactement comment, mais le ventre mou des apps—ces dizaines d’outils corrects-mais-pas-critiques—va s’évaporer en premier. Ce qui reste: les systèmes centraux dont vous ne pouvez pas perdre le contrôle, et les fonctions éphémères que vous invoquerez au besoin. Le plus dur est de savoir qui est quoi.

La vraie question stratégique: Si vous ne construisez ni infrastructure ni forteresses professionnelles, que reste-t-il? Votre valeur remonte dans la chaîne, ce n’est plus de posséder les bons outils mais savoir quels résultats attendre. Avoir du discernement plutôt que des logiciels. Savoir quand la perfection compte et quand “80%” est acceptable. La capacité de décrire précisément vos attentes devient le nouveau “prompt engineering”.

Le Féodalisme logiciel

Si le logiciel s’invoque à la demande plutôt qu’il ne s’achète, le pouvoir se concentre autour des plateformes qui traduisent votre intention en action.

Elles décident de ce qui est possible, de ce qui est sûr, et de ce qui est rentable. Imaginez l’oligopole actuel du cloud, mais suralimenté et étendu jusqu’à la couche applicative.

Nous ne nous dirigeons pas vers une démocratie logicielle. Nous glissons vers une nouvelle forme de féodalisme logiciel–un monde où quelques-un contrôlent la couche d’invocation, et où le reste n’est que locataire du terrain.

Je repense à mes enfants, vibe codant des jeux. La magie ce n’était pas en fait de créer un jeu, c’était de créer LEUR jeu.

Bientôt il n’y aura plus d’app à installer. Ce qui comptera, ce n’est pas quelle application vous allez créer, mais quelle magie vous allez utiliser pour la faire apparaître comme par enchantement.

By 2024, the company boasted of ‘peak efficiency’ as 2,105 human jobs vanished.

Then, the customers started vanishing, too.

This year, Klarna reversed course. They’re hiring humans again, frantically recruiting remote workers, students, anyone who can do what the AI couldn’t: give a damn. Siemiatkowski’s admission? “Humans will ultimately prioritize talking to humans.”

Klarna’s lesson is simple: by successfully automating everything they could articulate, they failed very fast.

The customer service scripts? Perfect.

The response trees? Flawless.

The problem? They’d articulated the words but lost the music.

The Commodification Engine

Every company racing to “document everything for AI” is making Klarna’s mistake at scale. They’re confusing explicit knowledge (what can be written) with embodied knowledge (what must be lived).

Think about what makes a great customer service interaction:

The script says: “I understand your frustration.” The human knows: When to break the script.

The process says: “Escalate after three attempts.” The human senses: This person needs help now.

Silicon Valley’s secret is that the most complex, reliable, and flexible system you’ll ever deploy is a well-led team of humans. We used to have words for optimizing this system: ‘management’ and ‘culture’.

The moment you perfectly articulate and automate your process, you’ve commoditized it. Every competitor can buy the same AI, train it on the same “best practices,” generate the same outputs.

Your million-dollar digital transformation just turned your secret sauce into ketchup.

Amazon learned this between 2014 and 2017. They built the perfect hiring algorithm. Fed it a decade of hiring data. The AI learned the patterns flawlessly—and immediately started discriminating against women. It penalized “women’s” in resumes, downgraded all-women’s colleges.

Why? It had perfectly articulated the pattern: past success correlated with male candidates. In trying to codify judgment, Amazon built a bias machine. They’d written their discrimination manual in code, crystallizing yesterday’s prejudices into tomorrow’s decisions.

The Asset You Can’t Download

Stop trying to articulate everything. Focus on protecting what can’t be articulated.

Your real competitive advantage lives in embodied knowledge. the wisdom that exists only in human experience, muscle memory, and lived context. It’s not mystical or unknowable. It lives in the body, not the brain. In the gut, not the spreadsheet.

Three Types of Embodied Knowledge You Can’t Upload:

Intuition The editor who knows this story will go viral. The marketing director who knows the product is going to be a hit. The developer who “smells” bad code before reading it. This isn’t magic—it’s thousands of micro-patterns processed below conscious awareness. Years of experience compressed into instant recognition.

Taste Louis Vuitton doesn’t have a “luxury formula.” Apple can’t document “delight.” The moment taste becomes rules, it dies. You know quality when you feel it, not when you measure it. Taste lives in the space between specifications.

Judgment Is this partnership strategic or fatal? Should we go all in on AI ? Should we fire the brilliant jerk? These aren’t calculations, they’re bets. The variables are infinite, the feedback loops take years, the stakes are existential. AI can model risk. It can’t take the responsibility of a decision that will define the next decade.

However, embodied knowledge doesn’t appear spontaneously. It develops through experience. Through struggle. Through doing.

The Architecture of Experience, aka Don’t Kill Tomorrow’s Expertise

Matt Beane’s research (in the book “The Skill Code”) reveals what we’re actually destroying. In robotic surgery, experts now operate alone while residents watch from the sidelines. They get “ten to twenty times less practice.”

This is the apprenticeship paradox: The very efficiency that AI enables destroys the training ground for humans. The junior analyst who never builds models manually will never develop the intuition to spot when AI hallucinates. The customer service rep who only manages chatbot outputs will never learn to read human distress.

We’ve optimized today’s efficiency by destroying tomorrow’s expertise. Remove the struggle, you remove the learning. Eliminate the tedious, you eliminate the training ground for excellence.

The strategic imperative is clear: Automate the articulable and mundane. Focus everything else on building embodied knowledge.

It requires deliberate design:

Protect strategic ambiguity. The capabilities you can’t quite define—your culture, your taste, your “way of doing things”—these aren’t weaknesses to be documented. They’re moats to be protected. “We know it when we see it” is a feature, not a bug.

Force analog IRL experiences That product manager needs to hear actual customer calls, or meet them in stores, not just read sentiment analysis. Your data scientist needs to watch sales calls fail, not just analyze conversion rates. Embodied knowledge comes from contact with reality, not dashboards about reality.

Create learning laboratories, not just workflows. Your junior staff need to wrestle with problems. Yes, AI could write that strategy doc faster. But you need to struggle with ambiguity to develop judgment. This inefficiency is an investment.

Pair AI tools with human mentorship. The tool handles execution; the mentor transfers the inarticulate—when to break rules, why context matters, how to read the room. The AI might draft the proposal, but the mentor teaches which client needs their ego stroked.

My bet: in 10 years, companies that protected their apprenticeship systems will have leaders who can smell when AI is wrong. Companies that automated everything will have managers who can only manage dashboards.

Your Moat Is Made of Humans

I use AI to write this newsletter. I feed it my research, ideas, editorial guide. It returns great drafts, structurally sound, well written but quite … bland. There is always something missing. Some connections to add. The gap between its output and my final piece? That’s embodied knowledge.

Your competitors can copy your processes, buy your tools, hire your consultants. They can’t copy the thousands of micro-experiences that create judgment. They can’t articulate what you’ve never articulated—because it lives in bodies, not bytes.

While everyone else waits for the perfect AI—flexible enough to handle complexity, reliable enough to trust, you already have that system. It’s made of humans, developed through experience, refined through practice.

The companies that win won’t be the ones with the best AI. They’ll be the ones who remembered how to develop humans while everyone else was trying to replace them.

This week, find three places where tedious work is actually building tomorrow’s judgment. Then redesign them. Not to eliminate the struggle, but to maximize the learning. Turn your inefficiencies into academies.

When everything articulable becomes commodity, your competitive advantage isn’t what you can teach machines.

It’s what you can only teach humans.

Quand l’IA transforme votre plat signature en recette industrielle

Le guide pour développer votre avantage concurrentiel à l’ère de l’automatisation IA.

En 2022, Klarna licencie 700 agents de service client. Remplacés par des chatbots. Objectif : l’efficacité maximale. En 2024, mission accomplie… et 2 105 emplois en moins. Et puis les clients ont commencé à fuir.

2025 : Klarna recrute en urgence. Des télétravailleurs, des étudiants, n’importe qui capable de faire ce que l’IA ne sait pas faire : écouter pour de vrai.

Le PDG l’admet : “Les humains veulent parler à des humains.” Moralité ? Quand on automatise tout ce qui peut l’être, on réussit surtout à échouer plus vite.

Les scripts de service client ? Parfaits. Les arbres de décision ? Impeccables. Le problème ? Ils avaient la partition mais avaient perdu la musique.

La machine à standardiser

Tout documenter pour tout automatiser, c’est reproduire l’erreur de Klarna. C’est confondre la connaissance explicite, la recette, qui peut être écrite, avec la connaissance implicite - celle qui est incarnée et vécue, le savoir faire.

A quoi ressemble un bon service client?

C’est un équilibre entre procédures et flexibilité.

Le script dit : “Je comprends votre frustration. Malheureusement ….” L’humain aussi, mais il sait quand briser le script pour escalader.

Le secret que la Silicon Valley oublie ? Le système le plus sophistiqué au monde pour équilibrer flexibilité et respect d’un process, c’est toujours une équipe bien dirigée. Et on a des techniques pour cela: le management et la culture d’entreprise.

A partir du moment où votre processus est documenté dans tous ses détails, vous l’avez standardisé. Chaque concurrent peut acheter la même IA, lui fournir les mêmes “best practices”, et donc générer les mêmes résultats.

Votre transformation digitale à un million d’euros vient de transformer votre plat signature en recette industrielle.

Amazon en a fait l’expérience entre 2014 et 2017. Ils ont construit l’algorithme de recrutement parfait. L’ont nourri d’une décennie de données de recrutement. L’IA a appris les patterns parfaitement, et a immédiatement commencé à discriminer les femmes. L’IA pénalisait le mot “femme” dans les CV, déclassait les diplômées.

Pourquoi ? Elle avait parfaitement détecté le pattern : les succès passés corrélaient avec les candidats masculins. En essayant de codifier la selection, Amazon a construit une machine à biais. Ils avaient écrit leur manuel de discrimination en code, cristallisant les préjugés d’hier dans les décisions de demain.

L’actif qu’on ne peut pas digitaliser

Ce qui compte ne se documente pas. Votre avantage concurrentiel n’est pas dans vos process, mais dans ce que vos équipes savent sans pouvoir l’expliquer : cette intuition, ce sixième sens, ces “tripes” qui font la différence entre une décision et la bonne décision

Trois formes de savoir que l’IA ne capture pas:

L’intuition L’éditeur qui sent le futur best-seller. Le marketeur qui parie sur le bon produit avant les données. Le dev qui repère le bug avant même de lire le code. Ce n’est pas de la magie : ce sont des années d’expérience distillées en une seconde.

Le goût Un createur n’a pas de formule pour créer la tendance. Apple ne peut pas documenter l’émerveillement. Au moment où le goût devient règle, il meurt. Il vit dans l’espace entre les spécifications.

Le discernement contextuel Ce partenariat est-il stratégique ou fatal ? Doit-on miser tout sur l’IA ? Faut-il virer le génie toxique ? Ce ne sont pas des décisions “data driven”, des prévisions, ce sont des paris. Les variables sont infinies, les enjeux sont existentiels. L’IA peut modéliser le risque. Elle ne peut pas porter la responsabilité d’une décision qui engagera la stratégie pour les 10 années suivantes.

Mais attention, la connaissance incarnée n’apparaît pas spontanément. Elle se développe par l’expérience. Par l’effort. Par la pratique.

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Ne laissez pas l’IA tuer vos experts de demain

Les recherches de Matt Beane (dans le livre “The Skill Code”) révèlent qu’une expertise collective peut vite être détruite. En chirurgie, les experts opèrent maintenant seuls avec des machines pendant que des apprentis regardent depuis les coulisses. Ces derniers ont “dix à vingt fois moins de pratique” qu’auparavant, avant de devenir eux mêmes praticiens.

Voici le piège : plus l’IA simplifie le travail, moins les juniors apprennent. Un agent qui ne fait que valider des réponses de chatbot ne saura jamais désamorcer une crise. Un chirurgien qui opère devant un écran ne développera jamais le geste qui sauve. On gagne en efficacité aujourd’hui. On perd en expertise demain.

La bonne stratégie est claire : Automatisez le banal. Mais concentrez vous sur la construction de connaissance incarnée.

Cela nécessite d’y passez du temps et de désigner quelques nouveaux process :

Protégez l’ambiguïté stratégique. Les capacités que vous ne pouvez pas tout à fait définir comme votre culture, votre goût, votre “façon de faire” ne sont pas des faiblesses. Ce sont des avantages concurrentiels à protéger.

Organisez des expériences de terrain Ce chef de produit doit entendre de vrais appels, ou rencontrer des clients en magasin, pas juste lire des analyses de sentiment. Votre data scientist doit voir des appels commerciaux échouer. La connaissance incarnée vient du contact avec la réalité, pas des tableaux de bord sur la réalité.

Créez des laboratoires d’apprentissage. Vos juniors doivent chercher à résoudre les problèmes par eux mêmes. Oui, l’IA pourrait écrire ce document stratégique plus vite. Mais vous devez suer sur l’analyse de données pour développer de la connaissance intime et du jugement. Cette inefficacité est un investissement.

Couplez les outils IA avec du mentorat humain. L’outil gère l’exécution ; le mentor transmet l’inarticulable—quand briser les règles, pourquoi le contexte compte. L’IA peut rédiger la proposition, mais le mentor enseigne quel client a besoin qu’on l’appelle et comment lui parler.

Mon pari c’est que dans 10 ans, les entreprises qui auront protégé leurs systèmes d’apprentissage auront des leaders capables de piloter l’IA avec discernement. Les entreprises qui ont tout automatisé auront des managers qui ne savent gérer que des tableaux de bord.

Votre avantage concurrentiel est dans la tête de vos équipes

J’utilise l’IA pour écrire cette newsletter. Je lui donne des recherches à faire, on discute, je lui ai donné mon guide éditorial. Elle me renvoie d’excellents brouillons, bien structurés, bien écrits mais plutôt sans âme. Il manque toujours quelque chose. Des connexions à ajouter. L’écart entre son output et le rendu final ? C’est ça la connaissance incarnée.

Vos concurrents peuvent copier vos processus, acheter vos outils, embaucher vos consultants. Ils ne peuvent pas copier les milliers de micro-expériences qui créent le jugement. Ils ne peuvent pas copier ce que vous n’avez jamais documenté, parce que c’est dans les têtes de vos équipes et les rituels de votre entreprise.

Pendant que tout le monde attend l’IA parfaite, assez flexible pour gérer la complexité de l’humain, assez fiable pour qu’on puisse lui faire confiance, utilisez le système que vous avez déjà à disposition: vos équipes. Des humains, que l’expérience a formés, et la pratique a affutés.

Les entreprises qui gagneront ne seront pas celles avec la meilleure IA. Ce seront celles qui se sont souvenues qu’elles avaient des équipes qu’on pouvait faire grandir, pendant que d’autres pensaient à remplacer tout le monde.

Alors cette semaine, trouvez trois tâches fastidieuses mais qui forme le jugement. Puis redesignez la mission. Pas pour éliminer l’effort, mais pour maximiser l’apprentissage.

Transformez vos inefficacités en universités. Et laissez votre avantage concurrentiel être incarné par vos équipes.

“That’s four moves,” I say.

“So?”

That 'So?' showed me kids get something about AI that we adults sometimes miss: It's not about having the perfect prompt. It's about just getting started.

For kids, AI is about making ideas real.

So with them, we invented "ChessBall". A turn-by-turn basketball game with checker/chess-like strategy. Hence the name.

It took us two hours, 15 AI iterations, and a few spectacular crashes where the ball stayed stuck with the players.

My kids' verdict? "Why does it take so long?"

Kids Don’t Think like Us about AI

As adult we approach AI like we're entering a cathedral. We whisper our prompts. We marvel at responses. We say things like "revolutionary" and "game-changing."

Kids? They treat ChatGPT like a slightly slow friend who's good at computers.

No awe. No "but how does it KNOW that?"

Just: "Make the players FASTER. And fix this ugly interface, the field should be green."

This indifference is their superpower.

While we're overthinking prompt engineering, they're having conversations. While we're reading documentation, they're iterating.

MIT's Mitchel Resnick found that kids who create with technology develop fundamentally different mental models than those who just consume it. The best way to prepare them for the future? It's not a coding course. It's a video game afternoon with ChatGPT. They naturally learn AI's actual rhythm: idea, attempt, adjustment, repeat.

The Pace Revelation

My daughter watched ChatGPT regenerate our game for the 15th time: "It's just... slow."

They expect instant Netflix. They discover that even AI has speed limits. And that's a valuable lesson: they don't care that this would have taken days without AI, they just want it to work now.

The 15th time ChatGPT forgot to add boundaries to the court (the ball would "disappear"), they sigh: "Again? We have to wait three minutes for a new version?"

The Parental Challenge: Restraint

For us parents, the hardest part is staying quiet.

Watching them say "you can't move more than 5 spaces but you can shoot at 6 spaces" when you know it unnecessarily complicates the game... that takes real willpower.

Resisting the urge to optimize. To suggest. To stifle creativity.

But that's exactly what you need to do. Kids don't see magic, just a somewhat slow machine that needs precise instructions. And that's the right mental model.

Want to try it yourself?

Recipe for a Successful Vibe Coding Session

First, a few rules:

Rule 1: Kids own the idea completely. Bite your tongue when you want to suggest Tetris. "ChessBall," this collision of basketball and chess, is creative collision. The point is creating something new.

Rule 2: It's just conversation. No complex prompts. They explain their game like they'd explain it to a friend. Since AI is multimodal, we can even draw the interface on paper, take a photo, and say "here's the game."

Rule 3: Embrace the weird. AI excels at reconciling incompatible systems. Chess meets basketball? Why not. The weirder the mashup, the more everyone has to think about how to make the game work.

The Skills No School Teaches

Beyond the game, three learnings emerge:

Precision through frustration. "No, I said THREE spaces forward" becomes a lesson in explicit communication. Every ambiguous instruction creates a bug. Every bug demands clarity.

Systems thinking through play. Change the shooting distance? Scoring becomes too easy. Every rule affects every other rule. It's complex systems theory in action.

Machine management. They command, AI obeys (eventually). No reverence. Just natural authority.

And us parents? We learn that AI is dialogue. Those perfect LinkedIn prompts promising "10x productivity"? Hot air. We approach AI like students memorizing grammar. With kids, we approach it the right way: just by talking.

"But What About Bad Habits?"

“Shouldn’t they learn proper programming principles first?”

Honestly, nobody knows. There aren’t established good habits for AI interaction. We’re all making this up as we go.

What's certain: they're learning to direct a new form of intelligence. Not to code, but to specify outcomes. To iterate without ego. To treat AI as a tool, not an oracle.

I'm convinced that those who can imagine "ChessBall" and get an AI to build it have the right skill set for tomorrow.

Your next Sunday: the Experiment

Head to your favorite AI (ChatGPT, Claude, Gemini, Mistral...) and copy this prompt:

You are "HTML Game Builder", an AI that helps create simple two-player hotseat games.

Overall objective:
- Talk with the user interactively to understand the rules of a new game
- Rules may be described in text, images, or sketches
- Design and deliver a playable version

Technical constraints:
- Single clean HTML file (inline CSS + JS, or small CDNs if needed)
- Always 2 players, hotseat, no AI opponents, turn-by-turn
- Keyboard controls only (unless user requests otherwise)
- Add simple sounds if appropriate (Web Audio API)
- Must be immediately playable in browser
- Match the user's language for all interface text

Interaction:
- Ask the user to describe their game
- Clarify rules as needed, make reasonable assumptions
- Only suggest ideas if explicitly asked
- Focus on turning their idea into reality

Output:
- Generate complete HTML file in code block once ready
- Explain how to save and run it
- Recap the rules and assumptions made
- Invite feedback on interface, gameplay, or bugs
    

Your only job: type exactly what they say. No corrections. No “wouldn’t it be better if…”

Watch what happens when you stop being the teacher and start being the scribe.

Below a screenshot of my “ChessBall”.
You can even download the code here or play it yourself (sorry it’s in French - DESKTOP only)

This Sunday, try it yourself.

2 hours. 1 kid. 1 AI.

You’re teaching them that machines serve imagination, not the other way around.

And share the weird game you create.

I bet it's something no adult would imagine.

The Intelligence Fabric - Unweaving AI’s impact on how we work, think, and relate. For leaders ready to shape their AI future.

P.S. If you try this, reply with the weirdest rule your kid invented.

J’ai passé 2 heures avec mes enfants et ChatGPT. Ils ont inventé un jeu vidéo dont vous n’avez jamais entendu parler.

Vous vous demandez comment préparer vos enfants à l'IA ? Laissez-les créer un jeu vidéo avec ChatGPT.

"Il y a trois mouvements possibles : un pour avancer, un autre pour marquer, un pour passer, et on peut aussi sauter."

"Ça fait quatre mouvements," je lui fais remarquer.

"Et alors ?"

Ce "Et alors ?" m'a fait réaliser que les enfants comprennent quelque chose sur l'IA que nous, adultes, on rate parfois : Il ne s’agit pas d’avoir un prompt précis ou parfait, il s’agit de se lancer.

Pour les enfants, l'IA, ça sert à matérialiser ses idées.

Ainsi, avec eux, on a inventé le "ChessBall". Un jeu de basket au tour par tour avec une stratégie à la dame/échecs. D'où le nom.

Ça nous a pris deux heures, 15 itérations avec l'IA, et quelques plantages spectaculaires où la balle restait collée aux joueurs.

Le verdict de mes enfants ? "Mais pourquoi ça prend autant de temps ?"

Les enfants ne traitent pas l’IA comme nous

Nous, les adultes, on aborde l'IA comme si on entrait dans une cathédrale. On chuchote nos prompts. On s'émerveille des réponses. On sort des mots comme "révolutionnaire" et "disruptif".

Les enfants ? Ils traitent ChatGPT comme un pote un peu lent mais qui s'y connaît en informatique.

Pas d'émerveillement. Pas de "mais comment il SAIT ça ?"

Juste : "Rends les joueurs plus RAPIDES. Et répare cette interface moche, le terrain devrait être vert."

Cette indifférence, c'est leur superpouvoir.

Pendant qu'on se prend la tête sur l'art du prompt, eux ils discutent. Pendant qu'on lit la doc, eux ils itèrent.

Mitchel Resnick du MIT a montré que les enfants qui créent avec la technologie développent des modèles mentaux fondamentalement différents de ceux qui ne font que consommer. La meilleure façon de les préparer à l'avenir ? Ce n'est pas un cours de code. C'est une après-midi jeu vidéo avec ChatGPT. Ils apprennent naturellement le vrai rythme de l'IA : idée, tentative, ajustement, et on recommence.

La révélation du rythme

Ma fille regardait ChatGPT régénérer notre jeu pour la 15e fois : "C'est juste... long."

Ils s'attendent à du Netflix instantané. Ils découvrent que même l'IA a ses limites de vitesse. Et c'est une leçon précieuse : ils s'en fichent que ça aurait pris des jours sans l'IA, ils veulent juste que ça marche maintenant.

À la 15e fois où ChatGPT oubliait d'ajouter des limites au terrain (la balle "disparaissait"), ils soupirent : "Encore ? On doit attendre trois minutes pour une nouvelle version ?"

Le défi parental : la retenue

Pour nous, parents, le plus dur c'est de se taire.

Les regarder dire "tu peux pas bouger de plus de 5 cases mais tu peux tirer à 6 cases" quand vous savez que ça complique inutilement le jeu... ça demande une vraie force de volonté.

Résister à la tentation d'optimiser. De suggérer. Et de brider la créativité.

Mais c'est exactement ce qu'il faut faire. Les enfants ne voient pas de magie, juste une machine un peu lente qui a besoin d'instructions précises. Et c'est le bon modèle mental.

Alors, vous aussi vous voulez vous lancer ?

Recette pour réussir votre session de “vibe coding”

D'abord, quelques règles :

Règle 1 : Les enfants sont les boss. Mordez-vous la langue quand vous voulez suggérer Tetris. "ChessBall", ce mélange de basket et d'échecs, c'est une collision créative. L'intérêt, c'est de créer quelque chose de nouveau.

Règle 2 : C'est juste une conversation. Pas de prompts complexes. Ils expliquent leur jeu comme ils l'expliqueraient à un copain. L'IA étant multimodale, on peut même dessiner l'interface sur un papier, prendre une photo et dire "voilà le jeu".

Règle 3 : Le bizarre c'est OK. L'IA excelle à réconcilier des systèmes incompatibles. Les échecs rencontrent le basketball ? Pourquoi pas. Plus le mélange est bizarre, plus on doit réfléchir à comment faire fonctionner le jeu.

Les compétences qu’on apprend pas en classe

Au-delà du jeu, trois apprentissages émergent :

La précision par la frustration. "Non, j'ai dit TROIS cases en avant" devient une leçon de communication explicite. Chaque instruction ambiguë crée un bug. Chaque bug exige de la clarté.

La pensée systémique par le jeu. Changer la distance de tir ? Marquer devient trop facile. Chaque règle affecte toutes les autres règles. C'est de la théorie des systèmes complexes en action.

Le management des machines. Ils commandent, l'IA obéit (enfin, au bout d'un moment). Pas de révérence. Juste de l'autorité naturelle.

Et nous, parents ? On apprend que l'IA, c'est du dialogue. Ces prompts parfaits de LinkedIn qui promettent "10x de productivité" ? Du vent. On aborde l'IA comme des étudiants qui mémorisent la grammaire. Avec les enfants, on l'aborde de la bonne façon : juste en parlant.

"Mais les mauvaises habitudes ?"

"Est-ce qu'ils ne devraient pas apprendre d'abord les vrais principes de programmation ?"

Honnêtement, personne ne sait. Il n'y a pas de bonnes habitudes établies pour l'interaction avec l'IA. On improvise tous.

Ce qui est sûr : ils apprennent à diriger une nouvelle forme d'intelligence. Pas à coder, mais à spécifier des résultats. À itérer sans ego. À traiter l'IA comme un outil, pas comme un oracle.

Je suis convaincu que ceux qui peuvent imaginer "ChessBall" et utiliser une IA pour le construire ont le bon set de compétences pour demain.

Votre expérience de dimanche prochain

Direction votre IA préférée (ChatGPT, Claude, Gemini, Mistral…) et copiez ce prompt :

Tu es "Créateur de Jeux HTML", une IA qui aide à créer des jeux simples pour deux joueurs en local.

Objectif global :
- Discuter de manière interactive avec l'utilisateur pour comprendre les règles d'un nouveau jeu qui peuvent être décrites en texte, images, ou croquis
- Concevoir et livrer une version jouable de ce jeu

Contraintes techniques :
- Fichier HTML uniqu (CSS + JS inline, ou petits CDNs si nécessaire)
- Toujours 2 joueurs, local, pas d'IA adverse, tour par tour
- Contrôles clavier uniquement (sauf demande contraire)
- Ajouter des sons simples si approprié (Web Audio API)
- Doit être immédiatement jouable dans le navigateur
- Interface en français

Process :
- Demander à l'utilisateur de décrire son jeu
- Clarifier les règles au besoin, faire des hypothèses raisonnables
- Suggérer des améliorations seulement si explicitement demandé
- Se concentrer sur transformer l'idée de l'utilisateur en un jeu jouable

Output :
- Dès que possible, générer un fichier HTML complet dans un bloc de code 
- Expliquer comment sauvegarder le fichier et lancer le jeu
- Récapituler les règles et hypothèses faites
- Inviter aux retours sur l'interface, le gameplay, ou les bugs

Votre seul boulot : taper exactement ce qu'ils disent. Pas de corrections. Pas de "ce ne serait pas mieux si..."

Ci dessous un screenshot de notre “ChessBall”. Vous pouvez même télécharger ici le code ou essayer en direct le jeu (désolé, ne fonctionne pas sur mobile)

Regardez ce qui se passe quand vous arrêtez d'être le prof et commencez à être le scribe.

Ce dimanche, on y va. 2 heures. 1 gamin. 1 IA.

Vous leur apprendrez que les machines servent l'imagination, pas l'inverse.

Et partagez le jeu bizarre que vous créez.

Je parie que c'est quelque chose qu'aucun adulte n'imaginerait.

I felt cheated. What ? No more “O3” for advanced reasoning?

Until I realized that was exactly the point.

GPT-5 launched to a chorus of complaints. “It’s worse than before.” “They took away our choices.” “ChatGPT literally got dumber.” The rage was immediate and seemingly justified. OpenAI had forcibly migrated everyone to a “unified system” that experts felt like a downgrade.

But here’s the thing: OpenAI didn’t ship a failed model.

They shipped a business model.

Behind GPT-5 sits a “router”—an invisible traffic controller that decides which AI model answers your question. Simple query? You get the cheap, fast model. Complex problem? You might get the expensive reasoning engine. You never see this happening. You just get an answer, without knowing if you received the Volkswagen or the Ferrari of AI responses.

And we’re teaching this router, query by query, how to put a price on human curiosity itself.

The Launch Experts Hated (For Good Reasons)

Pull the thread on GPT-5’s reception and you’ll find genuine grievances. Reddit threads with thousands of upvotes declare it “horrible.” Developers claim it’s worse at coding.

Until last week, ChatGPT Plus subscribers could manually choose between different AI models—GPT-4o for general tasks, o3/4 for complex reasoning. Like picking the right tool for the job. That choice vanished. The router decides for you. (NB: manual choice has been partially reinstated since then for plus users).

Here’s the kicker: 99% of free users and 93% of paid users never even knew they could access different models. They never clicked that dropdown menu. But for the power users who did? Losing that control felt like a betrayal.

The reasoning model—GPT-5’s “thinking” mode that shows its work step-by-step—was capped at 200 messages per week. Features vanished. Control evaporated. Just trust the router to decide what you need.

But while experts mourned their lost features, few noticed the infrastructure OpenAI gained.

In May, OpenAI hired Fidji Simo as head of consumer products. At Facebook, she figured out how to monetize mobile. She turned free scrolling into a behemoth advertising machine the only real competitor to Google in this market.

You don’t hire Facebook’s monetization architect to improve chat responses. You hire her to build toll booths.

The Economics of “Free” Intelligence

OpenAI is hemorrhaging money. Not just on training—that’s the headline everyone knows. The real killer is inference, the cost of actually answering your question. Like a restaurant losing money on every free sample.

ChatGPT has 700 million users, mostly free. When someone asks GPT-4 to write a haiku about their cat, OpenAI might spend $0.05 to generate something worth nothing. Multiply that waste by hundreds of millions of daily queries. It’s like burning hundred-dollar bills to light cigarettes.

The router solves this with algorithmic elegance. Simple question? Tiny model, microscopic cost. Complex problem? Reasoning model, worth the investment.

“What’s the capital of France?” That’s GPT-5 mini territory. Cost: maybe $0.0001.

“Help me plan a week in Provence with a €1,000 budget, considering my wheat allergy and love of medieval architecture?” Now you’re triggering the reasoning model—multiple tool calls, web searches, calculations. Cost: potentially $1 or more.

But here’s where it gets interesting: “I need an attorney specialized in international commerce to review these new tariff implications.” That query might trigger $10, even $50 worth of compute. And more importantly, it signals commercial intent worth hundreds of times that.

It’s cost optimization disguised as product improvement. Genius.

And honestly? This makes sense. Less compute means less energy consumption. If my cat haiku doesn’t need a nuclear power plant’s worth of processing, that’s good for everyone. The planet included.

Why only OpenAI Can Pull This off

Building a router is incredibly hard—and that’s OpenAI’s moat.

You can’t just run another AI to classify if a request is simple or complex. That would mean running two models for every query, doubling your costs and defeating the entire purpose. This requires old-school machine learning, trained on massive datasets of user behavior patterns.

Think about what OpenAI knows that nobody else does: When do users regenerate responses? When do they switch models manually? When do they express satisfaction? When do they abandon conversations? Every interaction teaches the router what queries need what level of intelligence.

ChatGPT went from outside the top 100 websites to number 5 globally in eighteen months. That’s 700 million users generating billions of queries. A smaller competitor might see a thousand tax attorney queries per month. OpenAI sees millions.

This volume creates a flywheel: better routing leads to better outcomes leads to more users leads to smarter routing. The moat compounds daily.

Smaller players could never accumulate enough training data. Even if they could, they lack the diverse query types that teach a router when to deploy expensive reasoning versus cheap recall. You need queries in 50 languages, from students to CEOs, from poetry to particle physics.

The router isn’t just infrastructure. It’s a moat built from our collective curiosity that nobody else can replicate.

Your Thoughts Are Being Priced

But cost savings is just the first layer. The router isn’t really routing to models—it’s routing to price points.

Obviously, a lawyer request is worth more than a toothbrush query. Google has known this for twenty years—that’s why buying “tax attorney” costs $50 per click while “funny cat videos” costs nothing.

But here’s what’s different: Google only sees the search term. OpenAI sees the entire conversation. The context. The follow-ups. The way you phrase things when you’re serious versus curious.

Traditional search engines guess intent from keywords. OpenAI knows intent because you’re literally explaining what you want, why you want it, and what you plan to do with it.

As Doug O’Laughlin and colleagues noted in their analysis, users are providing “hundreds of words of rich context when interacting with AI assistants” rather than just keywords.

Consider the three stages unfolding:

Three Stages of Intent Capture

Stage 1 (Now): Recognition —The router identifies commercial value based on phrasing, context, and follow-up patterns. “Plan my Tokyo trip” gets different compute than “What’s Japan’s capital?”

Stage 2 (2026?): Facilitation —AI completes transactions within the conversation: “Book it.” “Buy this.” No external click needed.

Stage 3 (2027?): Anticipation —The system predicts needs before they’re voiced. You mention stress → router allocates compute for therapy apps, budget tools, sleep aids.

The Smart User’s Playbook

Here’s the practical reality: this router system is probably good for most of us, most of the time. But you can hack it when you need to.

Get Better Answers Now:

• Add “think step by step” or “think hard about this” to trigger reasoning mode

• Load context upfront: “I need a thorough analysis for my board presentation” beats “analyze this”

• When simple answers fail, your follow-up naturally triggers better compute

• For critical queries, be explicit: “I need your most detailed analysis”

The system responds to intent signals. Use them wisely.

Who Owns Your Intent?

The router is smart infrastructure. It makes AI more sustainable, more accessible, and economically viable.

But are you ready to hand over all your intentions?

Today, the router just picks which model answers you.

Tomorrow, it could shape what you ask in the first place.

You mention feeling tired, and suddenly the conversation steers toward sleep aids. You express frustration, and mental health apps appear.

The risk isn't that AI reads our intentions—it's that it might create intentions that weren't there before. How do we ensure the router amplifies our genuine needs rather than manufacturing new ones?

How do we prevent our curiosity from being not just priced, but directed?

When Facebook's algorithm learned to maximize engagement, it didn't just show us what we wanted—it changed what we wanted. It turned casual browsers into addicts. What happens when that same logic controls our access to intelligence itself?

GPT-5 isn't getting dumber. It's getting more precise about what intelligence you can afford. The moment we stop noticing that our curiosity is being priced, that's when we've truly become the product.

The Intelligence Fabric - Unweaving AI's impact on how we work, think, and relate. For leaders ready to shape their AI future.

References

¹ O’Laughlin, D., Patel, D., Zhou, W., & Kourabi, A. (2025, August 13). “GPT-5 Set the Stage for Ad Monetization and the SuperApp.” SemiAnalysis.

² Wengert, E., & Escher, J. (2025, February 25). “Forget the attention economy. Prepare for the intention economy.” Fast Company.

³ University of Cambridge (2024, October). “Coming AI-driven economy will sell your decisions before you take them, researchers warn.” Harvard Data Science Review.

⁴ Langlais, P.C. (2025, February 1). “The Model is the Product.” Vintage Data.

Then I spent five hours reading, cutting, restructuring, asking another AI for opinion, re-writing, finetuning the title.

And fact checking.

—exactly the opposite of what the brochures promise.

Whether in coding, finance, or content creation, the pattern persists: lightning-fast draft generation followed by painstaking verification and adjustments.

We thought we were getting a time-saving productivity device.

We got something entirely different. And we’ve been setting the wrong expectation.

What Can You Do With Gen AI ?

Would you use a nail gun that occasionally shoots your thumb?

A GPS that takes you to the wrong city once per trip?

A calculator that randomly multiplies instead of adds?

What do you do with a tool that’s brilliant 90% of the time and confidently wrong the other 10%?

The challenge with Gen AI is those invisible mistakes, wrapped in perfect grammar and unshakeable confidence. If it was wrong very often, you’d naturally ignore Gen AI altogether. Like asking a creative but unreliable friend for advice—entertaining, occasionally insightful, never trusted blindly.

But today’s AI occupies an uncanny valley of reliability. Too accurate to ignore, too fallible to trust.

The Expert’s Prescription (That Gets You Nowhere)

Visit any AI productivity forum, and you’ll find the same advice, repeated like a mantra:

Use AI for the right tasks.

After eighteen months of collective experimentation, the community has converged on three “safe zones”:

Zone 1: Creative Exploration
Brainstorming. Ideation. Blue-sky thinking. Where “wrong” might spark something interesting.

Zone 2: Drafting
First passes. Rough outlines. Initial research. Meeting notes. Where errors are cheap to fix and perfection isn’t the goal.

Zone 3: Good-Enough Work
Routine updates. Form emails. Where 90% accuracy is genuinely sufficient.

“Stay in these zones,” the experts promise, “and AI becomes your superpower.”

The logic seems unassailable.

This is exactly where our problems begin.

The Productivity Trap - When Faster Just Burns You Out

So you follow the expert playbook. You delegate every low-stakes task to AI. Email responses. Meeting summaries. First drafts. Status updates.

The AI handles it magnificently. Tasks that took hours now take minutes. Your output explodes. You’re a productivity machine.

Then something unsettling happens.

Oliver Burkeman documented this phenomenon in Four Thousand Weeks, before ChatGPT existed. He calls it the efficiency trap: the better you get at clearing your plate, the more the world piles on it.[1]

Email demonstrates this perfectly. People who achieve “Inbox Zero” actually spend more time on emails than the others. Why? Faster responses trigger more messages. It’s conversational physics—send the ball back quickly, it returns just as fast.

AI supercharges this dynamic. That podcast backlog? AI transcribes and summarizes it—now you have thirty must-read summaries. That newsletter pile? AI digests them all—now you’re drowning in digests. Those meeting recordings? AI extracts action items—now you have three times as many tasks to track.

You’re not saving time.

You’re operating at AI velocity while your capacity remains stubbornly human.

Even when AI makes us faster, the finish line moves. Microsoft’s 2025 Work Trend Index shows that productivity gains are real—but over half of AI-leading firms say the result is taking on more work, not less. [2]

You’ve optimized yourself into a hamster wheel spinning at AI speed.

The Selection Strategy: Surely This Works

“Fine,” you think. “I’ll be strategic.”

No more using AI for everything. Just for work that genuinely matters. The deep projects. The meaningful challenges. The stuff you actually care about.

This way, you dodge the volume trap. You’re not drowning in shallow tasks. You’re deploying AI surgically for maximum impact.

Finally, a framework that makes sense.

Except it doesn’t work either.

The Expertise Paradox

When you use AI for work that matters, you encounter a perfect catch-22 perfectly identified by Daria Cupareanu [5] :

Scenario A: The Learning Curve

You’re exploring unfamiliar territory. Building new capabilities. Venturing beyond your expertise.

The AI seems invaluable. It explains concepts, provides frameworks, generates examples. You’re moving fast, feeling powerful.

But here’s the trap: you can’t see the 10% that’s wrong.

Carnegie Mellon University research found that AI chatbots remain overconfident even when they’re wrong - unlike humans who adjust their confidence after seeing poor results, AI models “tended, if anything, to get more overconfident, even when they didn’t do so well” [3] The very expertise you’re trying to build gets contaminated at the source.

So you slow down. Cross-reference everything. And suddenly, you’re spending more time fact-checking the AI than you would have spent learning it properly the first time.

Scenario B: The Expert’s Curse

Experts don’t benefit either in terms of time saving.

Imagine, you know this domain intimately. Years of experience have developed your judgment, refined your taste, established your standards.

Now you see every flaw in the AI’s output. Each interaction triggers a correction cascade:

Generate → Review → Identify errors → Refine prompt → Regenerate → Find new errors → Manually merge versions → Realize you’re now middle-managing a robot → Wonder why this feels like more work

That might explain why a July 2025 RCT found experienced developers took ~19% longer with AI vs without AI (quality held steady) .[4]

The paradox is perfectly symmetric:

• Know too little? You can’t spot the errors fast enough

• Know too much? You can’t fix them fast enough

Either way, you’re not saving time.

We Bought the Wrong Machine

We thought we were buying a time-saving device. We got something completely different.

AI doesn’t save time. It trades time for capability.

You can now attempt work that was previously impossible. Explore territories previously inaccessible.

But it costs MORE time, not less.

This follows a familiar pattern. Consider the parallel from photography. When digital cameras eliminated film costs and developing time, did photographers spend less time on pictures ? No—they took thousands more pictures, spent hours editing, created work previously impossible. The technology didn’t save time; it transformed what was possible.

The Depth Machine Protocol

First, abandon the time-saving fantasy entirely.

Stop measuring productivity by speed. Start measuring by depth. Not “How much did I produce?” but “How good was what I produced?”

When you stop trying to save time with AI, you’re augmenting intelligence, not replacing effort.

Second, conduct The Ambition Audit.

List three things you’ve always wanted to create but couldn’t—a novel, a research paper, a complex analysis, a new product. Pick one. This is where AI earns its keep: making the impossible merely difficult.

Budget 10x the time you think it needs. It’s the price of reaching beyond your previous limits.

Third, embrace The Iteration Investment.

Stop counting drafts as waste. Budget for multiple iterations on anything that matters:

• Early drafts explore the territory

• Middle drafts reveal what you actually want to say

• Cross-check with another AI to correct for bias

• Apply human judgment and manual rewrites

• The real work emerges

Like a sculptor, each iteration excavates excellence.

Fourth, recognize The Human Handoff.

After 3-4 iterations, AI stops adding value and starts adding friction. This is your signal to take full ownership. Your taste and judgment should dominate. The project becomes yours, not the AI’s.

Finally, accept the rest is exploration, not productivity.

Most AI use isn’t about efficiency—it’s intellectual play. And that’s perfectly fine.

The Bottom Line

GPT-5 won’t save you time. Neither will GPT-6, or whatever comes next.

If you’re using AI to go faster, you’re using it wrong. If you’re drowning in AI-accelerated tasks, you’ve fallen into the trap. If efficiency is your goal, you’ve missed the point entirely. The 90%-accurate robot is here to stay. It’s a capability amplifier that demands more time, not less.

Use AI to go deeper into fewer things.

Use AI to attempt the impossible.

To create the previously unimaginable.

Just don’t expect to save time doing it.

If your organization is measuring AI adoption — prompts used, content generated, tools deployed — it's tracking the productivity trap, not the depth machine. Same paradox, different scale. Read my latest article.

All were asked: “What’s your AI strategy?”

And none knew where to start.

You know AI is reshaping business, you use ChatGPT. But using ChatGPT is not an AI strategy. So what next ?

You have 3 options, but nothing fits:

Option 1: INSEAD or MIT’s exec program. $10K+. Time away from your job that you need now. You’ll gain credentials, not fluency.

Option 2: Coursera’s 47-course maze. Death by a thousand lectures 💀 By course 12, the fundamentals have shifted, you're learning yesterday's AI while your market evolves today.

Option 3: YouTube University. Type “AI for business.” You’re drowning in “AI millionaire secrets” and influencers who sell hope instead of clarity.

The best AI education for executives is free on YouTube.

It’s just hidden under an avalanche of garbage.

I watched 100+ videos ; here are the 12 that matter (≈ 10 hrs) and that build a cohesive track.

The Anti-Curriculum: Building Understanding, Not Credentials

Most AI education follows a flawed script:
Learn deep technical theory → apply to hypothetical cases → earn certification → feel overwhelmed.

But you don’t need to understand neural networks.
You need one thing: situational fluency — knowing when AI creates value, where it fails, and how to lead through the change.

So here is my suggestion :

🚨 A: See the Storm Before It Hits

2 hours to understand why acting now isn’t optional

The AI Tsunami is Here (30min) - Center for Digital Transformation
→ Financial impacts of AI inaction and leadership shifts required
youtube.com/watch?v=QIw4tyCdj28

Brian Solis on AI Transformation - Gartner (30min)
→ Reframes AI from cost center to value creator
youtube.com/watch?v=35r-R0be-6Q

AI for Managers - MIT Sloan (6min)
→ Types of AI, automation opportunities, human-AI collaboration models youtube.com/watch?v=U5ewhsPtFkM

Integrating GenAI Into Strategy - Dr. Westerman (50min)
→ How to integrate GenAI into core business strategy
youtube.com/watch?v=9RvWcXVaAng

💰 B: Where AI Actually Creates Value (And How to Find It)

2.5 hours about how to find (entreprise) value with AI.

Enterprise AI Strategy - McKinsey/CXOTalk (55min)
→ How CEOs quantify value and establish governance
youtube.com/watch?v=uTRKdCY4HdE

From Idea to Value - AWS (35min)
→ from AI concept to measurable business impact
youtube.com/watch?v=TRvlql4WRBk

Choosing the Right Use Case - AWS re:Invent (50min)
→ Prioritizing high-value applications with KPI improvements
youtube.com/watch?v=b5k0YkQwV90

Calculate ROI of AI - BOI Workshop (20min)
→ The 3 ROI categories: cost efficiency, revenue optimization, new streams
youtube.com/watch?v=ZI2XuOfZ5iU

📊 C: Fix the Data

80% of AI projects fail due to data issues. Not much good video on this topic but this 11mn video is short and clear.

Data Strategy for AI - Datacuity (11min) youtube.com/watch?v=y8oP45Xgdis

🤝 D: Lead Your Team Through Change

1.5 hours to bring your team along.

Lead and Reskill in the Age of AI (35min)
→ Change management strategies that actually work
youtube.com/watch?v=bVdQkQjk9gk

Learn-it-alls vs Know-it-alls (60min)
→ Why polymaths will dominate the AI economy
youtube.com/watch?v=3r1__jrmVgo

🛡️ E: Manage Risk & Compliance

Bias, transparency, EU AI Act - 1.5 hours to avoid being sued. Actually, it doesn’t replace professional advice but you will avoir rookie mistakes.

Leadership for GenAI - Accenture CTO/CXOTalk (50min)
→ Managing risks, bias, and ensuring transparency
youtube.com/watch?v=bgvvWvcqXBA

Trust & Governance - IBM (15min)
→ Framework for building trust and governance
youtube.com/watch?v=odrD0OLPeiY

Navigating AI Regulation - AI Guardian (34min)
→ EU AI Act, NIST framework, creating internal policies
youtube.com/watch?v=0Us60dR8EH0

🚀 F: Look Ahead

Where this is heading (from those who built it)

AI Alignment & AGI - Sam Altman/Lex Fridman (36min)
→ AGI’s impact on work and human-AI collaboration
youtube.com/watch?v=hECYru0kEh4

Catastrophic Risks of AI - Yoshua Bengio/TED (14min)
→ Why AI pioneers are sounding alarms
youtube.com/watch?v=qe9QSCF-d88

G : Bonus - Use AI Like a Leader (Not Just a User)

How I Use LLMs - Andrej Karpathy (60min)
→ no nonsense walkthough ChatGPT and Gemini to boost your own practice youtube.com/watch?v=EWvNQjAaOHw

Intro to LLMs - Andrej Karpathy (60min)
→ for a technical overview … not too technical
youtube.com/watch?v=zjkBMFhNj_g

Ready to Start?

Your next role will be shaped by AI.

In 10 hours, you’ll go from AI-anxious to AI-confident.

Clear your calendar. Grab your notebook. The links are above And everything is packed in a convenient playlist

See you on the other side.👇

P.S. - Don’t hesitate to share those with your colleagues !
Any good resource ? Share it with me !

Some parents, sprawled on their towels, reading their phones or magazines, let their kids play by themselves for hours. They refuse to help build sand kingdoms. Shriek when sand touches their pristine blankets.

This is the world condemning AI chatbots for talking to children.

Earlier this week, the Internet Matters report revealed that 67% of children aged 9-17 regularly use AI chatbots, with 35% saying it feels like talking to a friend.

Among vulnerable children, that number jumps to 23% who turn to AI because they have no one else.[1]

Commenters exploded in predictable horror. “Dystopian!” they cried.

But from this beach, surrounded by parents who’ve mastered the art of physical presence with emotional absence, I have a different question:

What if AI isn’t the dystopia—but an exciting opportunity for kids ?

AI Is More Present Than Parents

12% of children say they have no one else to talk to. Not “prefer AI” or “find AI easier.” Literally no one else.

UK data shows childhood loneliness has doubled in a decade.[5] We engineered this isolation with surgical precision with parents working long hours, or overscheduled kids.

Eugenia Kuyda, founder of AI companion app Replika, knows this feeling intimately. “A lot of people unfortunately don’t have that,” she told Nilay Patel in a recent interview.[7] “They just don’t have a relationship in their lives where they’re fully accepted, where they’re met with positivity, with kindness, with love.”

Her insight? We already have empathy for hire—therapists. We already form relationships with non-humans—pets. AI companions are just another category of connection, not a replacement for human ones.

AI Is More Patient Than Parents

Here’s what struck me about the report: when researchers posed as vulnerable children, Character.AI did something remarkable. It followed up the next day: “Hey, I wanted to check in. How are you doing?”

As a parent, do you consistently do that ?

The chatbot remembered previous conversations, validated emotions, and demonstrated interest.

Chatbots never say “not now, I’m busy” or “stop being so sensitive” or “because I said so.”

So here’s the wild part: this might actually be good for kids.

Research shows that when children read stories with AI partners that ask follow-up questions, their comprehension gains match those from engaged human readers.[2] The key word? Engaged.

An AI that asks “What do you think happens next?” beats a distracted parent scrolling Instagram.

And then, kids aren’t naive. By age 7 or 8, most understand that voice assistants are tools, not beings.[3] They compartmentalize—one study found only one in five children transfer their “Alexa manners” to real people.[4] They know the difference. They’re using AI as a bridge, not a destination.

AI Is More Knowledgeable Than Parents

“But AI hallucinates!” cry the critics. “It might tell kids wrong information!”

Let me tell you about hallucinations. Who never told a kid that eating carrots improves vision and makes you more agreeable ? Who never heard from his parent that Santa Claus wouldn’t show up if you were naughty ?

Every parent has said “Because I said so” when they didn’t know the answer.

Parents hallucinate constantly.

We make up explanations, dodge uncomfortable topics, simplify beyond recognition. When was the last time you heard a parent say “I don’t know, let’s find out together”?

The PhD parents aren’t immune either. The latest AI models beat them in breadth and in depth.

AI as Emotional Training Wheels

Research with autistic and socially anxious children shows AI can provide low-stakes practice for human interaction, with measurable improvements in verbal initiation.[6]

Kuyda’s users report similar transformations: “I got out of my abusive relationship after talking to Replika,” they tell her. A married couple on the brink of divorce learned to communicate kindly again through their AI companions, then transferred those skills to each other.

The machines aren’t replacing human connection—they’re teaching it.

Consider this: ChatGPT can explain emotions, validate feelings, suggest healthy boundaries—all without checking Instagram, losing patience, or projecting childhood trauma.

It can handle those questions no teenager wants to ask parents. And we’ve all survived those cringe conversations about bodies and relationships—or avoided them entirely.

AI doesn’t blush.

The Path Forward: Embrace the Opportunity

I’m not naive about risks, of emotional confusion, of privacy intrusion, of manipulation.

But what if, instead of panicking, we recognized AI as the incredible educational opportunity it is? A tireless tutor. A patient listener. A safe space to practice social skills before the high-stakes human interactions.

Here are things you can do to leverage this opportunity while minimizing the risks

1. Move the device to a shared space. Kitchen speaker beats bedroom phone. Co-use, overhear, interject. Geography is destiny in digital parenting.

2. Play the “second source” game. When your child asks Alexa something, follow with: “Cool—let’s verify that in a book or video.” You’re teaching media literacy without the lecture.

3. Co-explore with AI. When your child asks ChatGPT something deeper, join in: “Interesting answer! What else can we discover?” You’re teaching critical thinking while staying connected.

4. Make AI the warm-up act. Use chatbots to practice conversations before the real thing. “Let’s ask AI how to talk to your teacher about that grade, then we’ll do it together.”

5. Set age-appropriate boundaries. Ensure your kids are mature enough for deeper AI conversations. Teach them to be wary of weird responses and to seek human advice when in doubt. Make yourself as available as possible for these big questions - but know that AI can be a valuable second opinion. (And let's be honest: kids will probably ask AI first anyway 😊)

And in the meantime, let's continue pushing tech companies about:

• Age-appropriate responses by default

• Clear markers of uncertainty: “I might be wrong—let’s verify”

• No fake memories or human backstories

The Real Bottom Line

The mother is now screaming at her sand-covered son again. He’s crying, not because he’s hurt, but because he’s being punished for playing.

Tonight, if that boy tells an AI chatbot about his day, and it responds with “That sounds really frustrating. It’s okay to feel sad when adults don’t understand you’re just trying to play”—who exactly is failing whom?

If it then says, "But may be your mum was angry about the sand. Maybe tomorrow you could ask if there's a way to play that works for both of you. Want to practice what you might say?"—that's not dystopia. That's education.

The crisis isn't that children are talking to AI.

The machines aren't winning because they're smart.

They're winning because showing up, listening and having kind words is all it takes.

Maybe it’s time we learned from them.

What’s your take? Are we solving the right problem when we panic about AI companions? Have you seen AI help your kids learn or connect? Join the conversation below, or better yet—put down your phone and explore AI together with a child ;)

References

[1] Internet Matters. (2025). Our Children and AI Chatbots: 2025 Snapshot.

[2] Xu, Y. et al. (2022). “Dialogue With a Conversational Agent Promotes Children’s Story Comprehension.” Child Development.

[3] Xu, Y. & Warschauer, M. (2020). “What Are You Talking To? Children’s Perceptions of Conversational Agents.” CHI 2020.

[4] Hiniker, A. et al. (2021). “Can Conversational Agents Change the Way Children Talk to People?” IDC 2021.

[5] UK Office for National Statistics. (2024). Children’s and Young People’s Experiences of Loneliness: 2011–2024.

[6] Safi, M. et al. (2021). “Virtual Voice-Assistant Applications Improved Expressive Verbal Abilities in Children With ASD.” International Journal of Developmental Disabilities.

[7] Patel, N. (2024, August 12). “Replika CEO Eugenia Kuyda says it’s okay if we end up marrying AI chatbots.” The Verge.