What the money is actually buying

Strip away the wonder and the humanoid thesis makes a narrow claim, that a machine shaped like a person, able in principle to do many things, is worth more than a machine built to do one thing perfectly. Versatility is the pitch, and the human silhouette is offered as its proof. This is where the reasoning quietly breaks. A general-purpose humanoid does many tasks poorly at a price premium, while a purpose-built system does one task with a clear return, and in an industrial setting the second machine wins on every line a chief financial officer actually reads. Peter Lasinger, a European venture investor, calls the fixation the anthropomorphic fallacy, the urge to build machines in our own image even when the image adds cost and subtracts nothing. The fallacy is real. The more useful word for a board is premium, because a fallacy is a thinking error, and a premium is a number you overpay every month the machine stays upright.

The equation one layer down is measured in watts

The software side of this market already learned the lesson the hard way, and it is the same lesson. Model capability became cheap and abundant faster than almost anyone expected, and the cost that ended up deciding whether a digital colleague earned its place was never the intelligence, it was the running cost, the price of every inference at scale. In hardware the same equation returns wearing different units. What decides a physical machine is not what it can do in a demo, it is how much energy it burns per task, the ratio of power to payload. A human being walks, lifts, reasons and repairs itself on roughly the power of a light bulb. A humanoid draws many times that, and much of the budget is spent not on the work but on the mere act of staying upright, on solving a balance problem every millisecond that nature only ever solved because it could not grow wheels. Lasinger reads the whole field through intelligence per watt, and the axis he points to is the right one. An anthropomorphic machine spends most of its energy budget on looking like us.

The teleoperation tell

There is a quieter fact under the demos that explains more than any valuation. A large share of the viral humanoid footage is teleoperated, driven in real time by a human in a motion-capture rig or a haptic suit. Tesla’s robots at the October 2024 event were remotely assisted for their crowd interactions, the polished hand demo a month later was teleoperated, and a fall in a late 2025 showcase reopened the same debate in public. This is not a scandal, it is a signal, and it is the physical twin of a pattern anyone who has shipped enterprise artificial intelligence knows by heart. The demo dazzles, the production case stays unpriced. A machine that needs a dedicated operator behind the curtain to cross a dynamic factory floor is not an automation solution, it is an expensive avatar. Real industrial scale runs the ratio the other way, one operator overseeing a fleet of twenty autonomous purpose-built machines, rather than one operator married to a single humanoid for the length of a shift.

If 2026 really is the ChatGPT moment, the thesis holds

The strongest objection deserves the floor. At CES in January, Jensen Huang declared that the ChatGPT moment for physical AI has arrived, and unlike previous years the announcements around him carried shipping numbers rather than concept videos. Boston Dynamics is wiring Google DeepMind’s Gemini models into Atlas with a stated target of thirty thousand units a year by 2028, and the first deployments are committed for 2026. Around the same time, an angel investor who had been shown the next Optimus in Tesla’s lab told his audience that nobody will remember Tesla ever made a car. Musk’s public reply was two words, probably true.

Take the analogy seriously, because it cuts the other way. The ChatGPT moment of software AI did not belong to a machine that resembled a person. It belonged to the least human interface imaginable, a text box, because the revolution was the model and never the body. If physical AI now has its equivalent moment, the same logic applies one level down, the intelligence becomes abundant and portable, and it will flow into whatever body delivers the most work per watt and per dollar in each environment. Nothing about that favors legs. Huang, it is worth remembering, wins either way, since the chips are the same whether they sit in a humanoid or in a wheeled arm. And the Tesla line is not evidence about robots at all, it is evidence about narrative, a company valued in the trillions needs a story larger than cars, and the confirmation from the top was a confirmation of the story’s necessity, not of the machine’s economics.

Where the capital is mispriced

This is the part a board or a fund should sit with, because the error is not only in engineering, it is in how the asset is valued. The market is pricing the humanoid install base as optionality, a versatile platform that will pay off across many future uses, when this cycle the resemblance is closer to a liability than an asset. Every degree of human likeness carries a cost that never appears in the launch video, in maintenance, in the safety margin around real workers, in downtime, in the sheer mechanical fragility of a tall two-legged frame. The wheeled, purpose-built alternative gives up the magic and keeps the margin. The judgment that separates a good underwriter from a late one is exactly this, that versatility is being counted as value when in an industrial setting it is mostly cost, and that the environment can almost always be adapted to fit a simpler machine more cheaply than the machine can be made human enough to fit the environment. Whoever keeps paying for the silhouette is buying a story. Whoever reshapes the shelf, the floor and the bin to suit a wheeled arm is buying a return.

The size of the premium can be read straight off the public numbers. Goldman Sachs projects the entire humanoid robot market at around thirty-eight billion dollars in 2035, which is less than the reported valuation of Figure alone today; other houses reach trillions on a 2050 horizon, but the nearer the date, the smaller the market and the wider the gap to the prices being paid. And the premium has now arrived in Europe. Neura Robotics of Metzingen closed a Series C of up to 1.4 billion dollars in June, led by Tether with Amazon, Nvidia and Qualcomm alongside, the largest robotics financing Europe has ever seen, framed as Physical AI made in Europe. Neura is the instructive case, because the company earns its money today with purpose-built cognitive machines for industry while the humanoid flagship carries the story that raised the round. Europe’s biggest robotics bet confirms both halves of the argument inside a single company, the capital follows the silhouette, the revenue follows the architecture.

And here the debate about steel rejoins the one that runs through all of this work. The decision was never the machine, in software or in hardware. It is the capability of the organization to match the right architecture to the right task, one domain at a time, and to tell the difference between a tool that earns its keep and a tool that merely looks the part. The humanoid is only the most visible case of a mistake that is everywhere in this transition, the mistake of taking the resemblance of intelligence for the result of it.

The way through

None of this means the robots are not coming. They are, and the useful question is not whether but which. Buy the resemblance and you carry the premium for as long as the machine runs. Buy the result and you ask a colder question first, what is the output per watt, and how much human is still left in the loop once the cameras are off. The winning architecture, in the data center and on the factory floor alike, is the one with the best answer to those two questions, and it will almost never be the one that looks back at you.

Whoever keeps paying the anthropomorphic premium will find, a few years from now, that the returns went to the builders who never cared what the robot looked like, and that they never saw it coming.

Gerhard Kürner is CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering, and author of Work After AI. Around 1,000 conversations with boards, owners, and PE funds across DACH and Europe.

Work After AI Weekly, by Gerhard Kürner. Every Friday I read the week’s signals from two sources, the investments of early-stage investors and the findings of research institutions, the same evidence base my annual report on AI and work is built on. Not because the investors themselves are the story, but because their investments are one of the clearest early signals we have for where work, organisations and enterprise software are actually heading.

Over the past three weeks this series watched companies hire an AI workforce, build a management structure over it, and hand it its first job with real professional liability. This week the digital colleague stops being a project and starts appearing in the systems that define what an organisation actually is. It gets an identity in the access directory, the same way a human hire does on day one. It starts changing who owns the business, because a professional whose entire back office is run by software no longer needs an employer. And the capacity it runs on is now being bought the way industrial firms buy factory capacity, years ahead and in billions.

The digital colleague now needs a badge, not just a login

Every organisation has a quiet system that decides who is allowed to do what, and until now it only had to answer for people. That assumption is breaking. Once dozens of AI actors work inside a company, each one needs an identity, a scope of permissions, and someone accountable for what it does with them, and managing that sprawl is becoming its own discipline rather than a checkbox in the IT department.

The market is already pricing this in. Oak, a company building what it calls an AI-native identity operating system, one control plane that manages humans, machines and AI agents as equal citizens of the directory, came out of stealth this week with a 60 million dollar seed round co-led by Accel, CRV and Greylock, unusually large for a seed and closed quietly back in late 2025. The banks are living the same shift in plainer language: Reuters reported this week that BNY now treats its digital employees as team members with their own logins and a human manager, and that other large banks are testing agents for core processes. When a bank gives a piece of software a login, a nickname and a supervisor, the identity system has become the org chart.

The professional keeps the license, the AI runs the office

Vertical AI spent the last two years selling efficiency to large employers. The more interesting move this week points the other way. When an AI operating system can run scheduling, billing, referrals and prior authorisations on its own, the licensed professional no longer needs the institution that used to provide all of that around them. The technology stops making employees more productive and starts making them owners.

Corner Health, which builds a network of primary care practices owned by the nurse practitioners who run them, raised 32.5 million dollars across seed and Series A this week, with Oak HC/FT leading the Series A. The company says ninety percent of its clinics operate without additional staff, a figure from its own announcement and worth reading as such, because the administrative work is carried by Cora, its AI-native practice operating system. The same pattern is arriving at institutional scale: Bunkerhill Health raised a 25 million dollar Series B led by Khosla Ventures this week for a platform that lets hospitals build their own AI actors instead of buying finished ones, with twenty-two agents already productive at one Texas health system.

This is the pattern the coming annual report calls the capability question. Work After AI, the yearly review of artificial intelligence and work that I will publish this autumn, argues that the decisive variable is not which model an organisation licenses but whether it builds the capability to make the technology productive inside its own walls. A one-clinic practice in Arizona and the Mayo Clinic are, on this evidence, now working on exactly that same problem.

Compute is bought like factory capacity now

A digital workforce has a means of production, and it is not the model. It is the inference capacity the model runs on, and the companies planning to field that workforce at scale have stopped treating it as a utility bill and started treating it as strategic procurement, contracted years ahead.

Reflection AI, backed by CRV since its seed round, signed a compute agreement worth more than 1 billion dollars with Nebius this week, running to 2029, stacked on top of an earlier capacity deal with SpaceX’s Colossus 2 worth up to 6.3 billion dollars, all to train open-weight frontier models and run its coding agent. Even the capital itself is retooling: Paradigm, long the technical flagship of crypto investing, closed a 1.2 billion dollar fourth fund the week before this window with a mandate that now spans AI and robotics alongside crypto. When specialist capital that committed redraws its own mandate, that says where the next decade of production capacity is being built.

The deals, at a glance

• Oak (Accel, CRV, Greylock, 60M seed, out of stealth): AI agents become identities to be managed like employees.

• Corner Health (Oak HC/FT, 32.5M seed plus Series A): the AI office turns licensed professionals into owners.

• Reflection AI (CRV, 1B+ compute deal with Nebius to 2029): inference capacity becomes strategic procurement.

• Bunkerhill Health (Khosla Ventures, 25M Series B): hospitals build their own AI actors in-house.

• Chai Discovery (Battery Ventures participating, 400M Series C): vertical AI reaches regulated pharma research.

• Paradigm Fund IV (1.2B new fund, week prior): specialist crypto capital retools toward AI and robotics.

Signals at the edge

Who writes the rules for the digital workforce? Within days of each other, two documents mapped the governance gap at the model layer from opposite directions. Google DeepMind chief Demis Hassabis proposed a FINRA-style, largely industry-funded standards body that would test frontier models before release and could coordinate a slowdown, with compliance eventually required for access to the US market regardless of a model’s origin. Days earlier the Future of Life Institute, an advocacy nonprofit with a declared regulatory agenda, published its summer index, in which an independent expert panel concluded that self-governance is no longer credible without external verification. No company scored above C+, the leaders have walked back their earlier pause commitments, and Europe’s flagship Mistral came last even as the EU leads on regulation, though Mistral objects that the scoring logic does not fit its development approach. Read together, the proposal and the report card make the same point: the rules of the field are being written elsewhere, and safety capability, like productive capability, is built inside the organisation rather than conferred by regulation.

Elsewhere, energy remains the binding constraint. The PJM capacity auction, covering the largest US grid, cleared at a record 16.4 billion dollars this week, with data centres accounting for roughly 6.3 billion of it. In defense, Helsing raised a 1.8 billion dollar Series E at an 18 billion dollar valuation, co-led by Lightspeed and General Catalyst, the largest European venture round on record. And on the labour side, Challenger, Gray and Christmas reported that AI remained the most cited reason for US job cuts in June, even as total announced cuts ran well below last year’s pace.

What this leaves on the table

Most of the firms in this week’s scan wrote no verifiable checks in the window, and that quiet is itself information: the money has moved down the stack, into identity, ownership structures and capacity contracts, the parts of the digital workforce that never appear in a product demo. None of this is a scoreboard of who invested how much. It is evidence for a single claim: the organisations taking this seriously are no longer asking what the technology can do. They are asking what has to be true inside their own walls, in their access systems, their ownership models and their procurement, for it to do that work reliably. Whoever waits for that question to answer itself will find, a few reporting cycles from now, that it was answered long ago, inside someone else’s walls.

Gerhard Kürner is CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering. More than 1,000 conversations over the last three years with boards, owners, and PE funds across DACH and Europe.

Two weeks ago companies started hiring an AI workforce. Last week that workforce got its first management structure, someone to certify it, someone to watch it, someone to keep it affordable. This week answers a harder question. What actually has to happen before a company hands a digital colleague something that matters, a task where a mistake is not an embarrassment but a liability.

Three deals this week trace that path in order. First a place to practice before anything is real. Then an engine big enough to run the practice at scale. And then, for the first time in this series, proof that the approach holds up in a profession where being wrong has always carried a price.

The practice room before the real job

No employer, human or otherwise, hands a new hire the keys on day one. There is a training period, a supervised stretch where mistakes are cheap and reversible, before anyone is trusted with something that cannot be undone. Companies building autonomous colleagues are discovering they need the same thing, and they are building it deliberately instead of hoping the model figures it out live in production.

You can already see this priced into the market. Wing VC led a 40 million dollar Series A into Bespoke Labs, with Mayfield Fund among the backers, for a company that builds the simulated environments and evaluation benchmarks agents are tested against before anyone lets them near a real workflow. The company is behind Terminal-Bench and OpenThoughts, benchmarks used across the industry to check whether an agent actually does what it claims before it gets production access. The pitch is not a smarter agent. It is a rehearsal space, and the fact that investors are funding the rehearsal space separately from the agent itself tells you how seriously the industry now takes the gap between demo and deployment.

The engine that cannot run out

A practice room only matters if the company can afford to run what graduates from it at scale, across every function, every day. That turns out to be a harder problem than picking the right model. It is a hardware problem, and this week’s biggest check went straight at it.

General Atlantic led the first close of a 1 billion dollar Series F into SambaNova Systems at an 11 billion dollar valuation, alongside Seligman Ventures, T. Rowe Price and Capital Group. SambaNova builds dedicated inference hardware, a direct alternative to renting Nvidia capacity, aimed at the fact that a company running a real digital workforce is not making one API call at a time anymore, it is running continuous inference across dozens of agents doing real work all day. The bet is not on which model wins. It is on the plumbing underneath, the same plumbing that decided last week’s story about whether an autonomous colleague is actually cheaper than the human it replaced.

The first regulated profession says yes

Everything above is preparation. This is the test case that matters, because it is the first deal in this series where the job on the other end carries real professional liability if it goes wrong.

Khosla Ventures led a 120 million dollar Series C into Norm AI at a 1.2 billion dollar valuation, with Coatue, Bain Capital Ventures, Blackstone, Craft Ventures and others participating. Norm AI builds AI agents that draft legal documents and handle compliance work under the supervision of human lawyers at its affiliated firm, Norm Law, which already serves clients with a combined 30 trillion dollars under management. Legal work has long been treated as the profession automation could not touch, because a mistake does not just cost time, it creates liability that lands on a licensed human. What changes here is not that the agent replaces the lawyer’s judgment. It is that the firm’s business model is shifting from billing by the hour to pricing by outcome, which only works if the agent doing the underlying drafting is reliable enough that the human supervisor is checking judgment calls, not fixing basic errors. That same structure, agent does the volume, licensed human owns the liability, outcome-based pricing replaces the billable hour, is the template every other regulated professional service is watching right now, audit and compliance work included.

The deals, at a glance

• General Atlantic → SambaNova Systems: 1B (Series F, first close). Inference hardware becomes the enterprise AI bottleneck.

• Khosla Ventures (lead), Coatue → Norm AI: 120M (Series C). A regulated profession goes agentic, with liability still human.

• Wing VC (lead), Mayfield Fund → Bespoke Labs: 40M (Series A). Practice environments before agents get real authority.

Signals at the edge

Three background trends are worth tracking even though they sit outside the work lens this week. Energy remains the structural constraint behind every deal above. A US heatwave this week strained the power grid supporting data centre growth, hyperscaler AI capital spending is on pace for roughly 750 billion dollars in 2026, and the Department of Energy expects data centres to draw as much as twelve percent of US electricity by 2028, up from four percent today, which means the inference capacity SambaNova and others are building is itself rationed by the grid. Defense tech funding has already passed 14.6 billion dollars in 2026, ahead of the entire prior record year, with Anduril alone raising a 5 billion dollar round at a 30.5 billion dollar valuation, but Fortune reported this week that investors in the category are now openly asking whether defense tech has become its own bubble stacked on top of the wider AI one. And the macro mood has not cooled. An internal US Treasury report is reportedly warning that the AI market shares structural risk with the dotcom bubble, a warning echoed this year by the Bank for International Settlements, even as AI companies keep pulling in a growing share of all venture capital.

What this leaves on the table

Put the three deals together and a clearer picture of what it actually takes to trust a digital colleague with something real starts to form. It has to practice somewhere safe first. It has to run on capacity that will not buckle the moment it scales past a pilot. And even then, in the one profession that put this to a real test this week, a licensed human still owns the outcome, just fewer of the hours that lead to it.

That is a more demanding bar than most companies are currently building toward. Plenty of AI rollouts skip the practice room and go straight to production, skip the capacity planning and hope the vendor’s infrastructure holds, and skip the question of who is actually liable when the agent gets something wrong. The capital in this week’s data is not a scoreboard of who wrote which check. It is an early map of what the companies actually getting this right are building first, the parts that do not show up in a product demo. Whoever keeps skipping that groundwork will find, a few reporting cycles from now, that it was there all along, just built by someone else first.

Gerhard Kürner is CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering. More than 1,000 conversations over the last three years with boards, owners, and PE funds across DACH and Europe.

Work After AI Weekly, by Gerhard Kürner. Every Friday I look at where the most sophisticated capital in the world is placing its bets, not because the investors themselves are the story, but because their bets are one of the clearest early signals we have for where work, organisations and enterprise software are actually heading. This week’s signal comes from the deals of the firms ranked eleven to twenty in the 2026 Strebulaev-Jackson Venture Ranking.

Here is where it starts. Companies have already begun hiring an AI workforce, software that finishes a task end to end instead of assisting a human through it. What this week’s signal shows is what happens next, and it has nothing to do with which firm wrote which check. It is a story about what happens inside a company the moment an autonomous colleague becomes a permanent hire. Three questions show up immediately. Can you trust it. Who is watching it. And can you actually afford to run it.

Read together, the deals of the week are not really about new digital colleagues. They are about the layer that makes the colleagues a company already hired trustworthy, supervised, and cheap enough to keep.

From can it do the job to can you trust it

For two years the competition among AI systems has been about capability, what a model can do that the last one could not. That race is quietly ending. Once an AI system runs as a colleague instead of a chat window, capability stops being the thing that decides who wins. Nobody hires a brilliant colleague they cannot trust in front of a client, and a model that occasionally makes something up is not a colleague, it is a liability with a nice interface. Reliability is becoming the actual product.

You can already see this priced into the market. Khosla Ventures backed Scaled Cognition’s new enterprise AI model, purpose-built to avoid hallucination and stay policy compliant while running live customer service work, with a 100 million dollar Series A. The company does not pitch a smarter model. It pitches a model a business can put in front of a customer without a human checking every output first, and that is a different sale entirely.

The digital teammate gets its own supervisor

Once a company runs autonomous colleagues across more than one function, a second problem shows up fast. Somebody has to see what they are doing, which systems they touch, and be able to stop them when something goes wrong. That supervisory function is turning into its own category of enterprise software, not a checkbox feature bolted onto the agent itself, and it is the clearest organisational signal of the week. The org chart question is no longer only who reports to whom among people. It is who inside the company is accountable for what the KI-Kollege did this morning, and what tooling gives them the visibility to answer that honestly.

Two early deals show the shape of that new layer. General Catalyst backed Tsuga, which builds observability for enterprise AI agents, essentially the monitoring layer DevOps built for software, now applied to a workforce that is itself software. Runlayer, with Khosla Ventures among its backers, raised money to give companies a way to track what their AI agents are doing across every system and pull them back when needed.

The economics of the autonomous colleague

None of the above matters if running the digital teammate costs more than the human it replaced. Whether the agentic company is actually cheaper than the human one it is copying depends almost entirely on inference cost, which is why the unit economics of the autonomous colleague are turning into their own investment category, quietly, underneath the more visible agent deals.

Two deals this week point straight at that plumbing. Kleiner Perkins backed Sail Research, which builds infrastructure that runs long-running AI agents more efficiently on the hardware companies already have, aimed at the fact that an autonomous agent can burn fifty to five hundred times the tokens of a normal chat session. General Catalyst also backed Together AI’s push to make open-source models a viable, cheaper alternative to renting every token from a single vendor. Put simply, the capital now flowing into that plumbing is the clearest evidence yet that inference cost, not model quality, is where the real margin fight will happen.

The deals, at a glance

• General Catalyst → Together AI, 800M (Series C): Open-model infrastructure for running agents

• Khosla Ventures → Scaled Cognition, 100M (Series A): Reliability becomes the product

• Kleiner Perkins → Sail Research, 80M (Series A): The cost of running an autonomous colleague

• General Catalyst → Tsuga, 35M (Series A): Observability for the machine workforce

• Khosla Ventures (participant) → Runlayer, 30M (Series A): Governance layer to watch and stop agents

Signals at the edge

Three background trends are worth tracking even though they sit outside the work lens this week. Defense tech funding has already passed 14.6 billion dollars in the first five months of 2026, more than the entire prior record year, and Fortune reported this week that several investors are now openly asking whether the category has become a bubble of its own, layered on top of the wider AI bubble debate. Energy remains the quiet constraint behind every deal above, with hyperscalers on pace to spend roughly 700 billion dollars on AI buildouts this year against a power shortfall Morgan Stanley estimates at close to 49 gigawatts by 2028, meaning the compute that autonomous colleagues need is itself rationed. And the macro mood around AI valuations has not cooled, DeepMind chief Demis Hassabis said publicly this year that early-stage AI startups with little traction are raising at unsustainable prices, a warning that keeps compounding as institutional capital keeps concentrating almost entirely in AI.

What this leaves on the table

Put the three shifts together and the shape of the agentic company keeps sharpening. It is not enough to hire the digital colleague. Someone has to certify it is reliable enough to trust, someone has to supervise what it does all day, and someone has to make sure it is actually cheaper to run than the person it replaced. None of that is a model problem. It is a management problem.

That is the part most companies still get wrong when they talk about their AI roadmap. They treat oversight, trust and cost as implementation details to sort out after the pilot succeeds. The capital in this week’s data treats them as the product itself, and that is the more useful way to read these deals: not as a scoreboard of who invested how much, but as an early map of the management layer every company will need to build for its own AI colleagues. Whoever keeps waiting to build that layer will find, a few reporting cycles from now, that the company sitting next to theirs already had a supervisor in place for its machines, and never noticed it being hired.

Gerhard Kürner is CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering. More than 1,000 conversations over the last three years with boards, owners, and PE funds across DACH and Europe.

Over the last three years I have had more than a thousand conversations with boards, owners and PE funds across DACH and Europe, mostly about the same underlying question. Everyone can see that AI is changing what a company needs to get work done. Almost nobody has a reliable way to see where that change is actually heading next, before it shows up in their own industry.

I started looking for an early signal, and I found one in an unlikely place. Venture capital is one of the few markets where the smartest people in the room have to place a real bet, with real money, months or years before the rest of the market catches on. When the firms at the very top of the field, ranked by the Strebulaev-Jackson Venture Ranking, start moving capital in the same direction at the same time, that is worth paying attention to. Not because the investors are the story. Because their bets are one of the earliest, clearest signals available for where work, organisations and enterprise software are actually heading.

That is what Work After AI Weekly is. Every Friday I go through the latest deals from ten of the top hundred venture firms in the world, rotating through the full list of a hundred over the course of the year, and I read them for one thing only: what they tell us about the future of work. Not which model is smartest. Not which round was the biggest. What it means for how you organise a company when part of the workforce is software.

Each issue distils that into three core trends and a short section on signals at the edge, the energy, defense and macro shifts that sit underneath everything else. No deal roster for its own sake. No hype. The venture data is the method, not the subject. The subject is always the same question. What is the smartest capital in the world telling us about the future of work, and what should you actually do about it before your competitors do.

The first issue goes out right after this note. It looks at what happens the moment an AI colleague becomes a permanent hire, and who has to manage it. It is not a prediction. It is a reading of decisions that have already been made, by people who had to be right or lose their investors’ money.

If you run a company, sit on a board, or simply want to see the shift before it reaches your desk, this is written for you. No jargon, no consultant framing, just the pattern as I see it, every week.

Subscribe here on Substack for the full issue every Friday, and follow me on LinkedIn for the shorter version of the same signal.

Whoever waits to pay attention to this will find, a few reporting cycles from now, that the shift was already visible in the data, and they never saw it coming.

Gerhard Kürner is CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering. More than 1,000 conversations over the last three years with boards, owners, and PE funds across DACH and Europe.

When top-tier AI turns into a commodity, the edge is no longer the model. It is who steers it.

Two dollars. That is what a million tokens of input now cost in Anthropic’s new Claude Sonnet 5, ten dollars for the output, on introductory pricing through the end of August. The flagship, Opus 4.8, costs five and twenty-five. So you get near-Opus capability for roughly forty percent of the price.

The obvious headline is that AI got cheaper again. That reading is correct, and it is the least interesting one available. Because when capability that yesterday lived only in the expensive flagship becomes an affordable commodity overnight, the first thing that changes is not what is possible. It is who holds the advantage. And that advantage moves to exactly the place most companies are not looking.

What actually happened this week

Sonnet 5 is not a benchmark show. On the one coding measure Anthropic reports directly, the model scores 63.2 percent, against 69.2 percent for the larger Opus 4.8. The gap to the top has narrowed, but it has not closed. Anyone waiting for a new record will be disappointed.

The real move sits in the price tag. TechCrunch frames Sonnet 5 as the cheaper way to run agents, VentureBeat reads it as a steep discount on Anthropic’s own flagship, in the middle of a race toward an IPO. On output price, Sonnet 5 lands at a third of OpenAI’s GPT-5.5, which charges thirty dollars per million tokens. That is not a technical detail. It is a declaration that agentic capability is now the baseline expectation at every price tier, and that the competition has shifted to who can deliver it most cheaply and most reliably.

This is the beginning of Work after AI. Not the moment the machine can do everything, but the moment good machines become so cheap that owning one is no longer an edge.

The token price is the wrong number

Here is the part almost no one says out loud. The price per token is no longer a reliable metric. Sonnet 5 uses a new tokenizer that maps the same work onto one to 1.35 times as many tokens. Anthropic set the introductory price, in its own words, to be roughly cost-neutral. The price per token fell, and the tokens per task rose, and the two roughly cancel.

Read that twice, because it breaks the entire way companies have bought AI so far. Two models with an identical token price can cost completely different amounts to finish the same job. A model that answers the same question in more steps and more tokens is the more expensive one, despite the same price list. And the more autonomy you hand a digital colleague, the higher the effort level, the more tokens it burns, not fewer.

So choosing a model from the price list means deciding on the wrong basis. The only figure that matters is cost per outcome. And it appears on no datasheet. It exists only once a concrete task runs through a concrete model at a concrete setting. That is the difference between managing a model and steering intelligence.

The bottleneck moves from the model to the steering

As long as there was one clearly best model, the job was simple. You took the best one. That era ends this week. Between Sonnet 5 and Opus 4.8 you can tune the balance of cost and performance through the effort level. Below them sit Gemini 3.5 Flash and open models like DeepSeek, whose output price runs under a dollar per million tokens, a full order of magnitude beneath Sonnet 5. Above them, the Opus and GPT ceiling at twenty-five and thirty dollars.

Between that floor and that ceiling lies a factor of thirty in price. Open source sets the floor, the frontier sets the ceiling. The question of which single model is best becomes the wrong question. The right one is which intelligence for which task, at what cost, and at what risk. Run routine work on the expensive flagship and you burn money. Hand the delicate judgment call to the cheapest open model and you burn trust.

The market already feels this without naming it. One survey of enterprise teams puts the share of companies actively managing their AI costs at 98 percent for 2026, up from 63 percent in 2025 and 31 percent in 2024. In the same breath, those teams say they can see their spend rising but not who is driving it or what value it creates. That is the exact picture of a bottleneck that has moved. The model is no longer scarce. What is scarce is the ability to steer a whole portfolio of intelligences by task, cost, and risk, and to make that steering accountable.

What this means for owners and capital

For owners and boards, this is the genuinely uncomfortable point. Access to top-tier AI was a differentiator for a while. This week it stops being one. When near-Opus capability is available to everyone at forty percent of the price, owning the model is as much of an edge as owning a power line. Andreessen Horowitz finds that enterprise CIOs expect their generative-AI budgets to grow by roughly seventy-five percent in the coming year. That capital is flowing into a layer that is turning into a commodity.

So value moves up, into the layer above the model. Into the orchestration that decides which task uses which intelligence. Into the context that makes a model useful in the first place. Into the governance that proves the right decision was made for the right reason. It is the parallel to the cloud, whose compute became cheap and whose real discipline afterward was cost management. The model zoo brings its own discipline. A board that looks for its edge in having licensed the most expensive model is confusing a higher bill with a stronger position. It is the same error as mistaking a leaner balance sheet for a better one.

Anyone valuing a company in this cycle should not ask which AI it uses. They should ask who there decides which intelligence does which task, and whether that company even knows its cost per outcome. The answer separates the firms that own AI from the firms that command it. The distance between the two will not be closed by one more model swap.

The new core competence already has a name

That names the shift. Model management, the picking of a model, was the competence of the last three years. Intelligence management, the steering of a portfolio of intelligences by cost, risk, and task, is the competence of the next. The good news for Europe is that this competence plays to its strengths. Documented processes, cost discipline, and governance were long treated as a brake. In a world where capability becomes a commodity and steering it becomes the edge, they turn into a differentiator. Whoever steers intelligence systematically, and can prove it, builds something a competitor cannot simply buy off the shelf.

From here the models get cheaper and better, week after week. The edge no longer lies in owning the best one, but in steering many of them well. Whoever waits will find, a year or two from now, that the decisive competence was available all along, and that a competitor was practicing it while they were still debating the next model. And they will not have seen it coming.

Gerhard Kürner is CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering.

Seventy-five hundred dollars per employee, every month, is what the most AI-forward companies now spend on artificial intelligence. The first reflex is to file that under outlier, the kind of number that belongs to a handful of labs in San Francisco and has nothing to do with a normal business. The reflex is wrong. According to the Ramp AI Index, the same curve is bending upward for everyone, the top ten percent and the median included, and it has steepened fastest in the past few months. What looks like an outlier is a preview of where the rest of the market is heading.

The number itself is not the interesting part. What matters is the shape of it. For the first time, the cost of getting work done by machines is starting to behave like the cost of getting work done by people. It scales with how much work you push onto it, it lands per employee, and that quietly moves it out of the software budget and into territory that finance and human resources have always owned. Boards are still reading this as a line item in IT. That is the mistake this piece is about.

What the Ramp data actually says

Strip away the headline and the Ramp AI Index is making a narrow, precise claim. Across every percentile of company, monthly AI spend per employee is rising, and the curves are accelerating rather than flattening. The most advanced adopters are approaching seventy-five hundred dollars per employee per month. The top ten percent sit around six hundred and thirty. The median is near twelve dollars and has turned sharply upward in the last stretch. The absolute figures matter less than the fact that all three lines bend the same way. The distance between them is a distance in time, not in kind.

What sits inside that spend is the second thing worth reading carefully. Ramp counts LLM subscriptions, coding agents, API tokens, and GPU cloud. None of that is software in the sense a CFO grew up with. It is the operating cost of output that used to require a person. I have watched this line appear in client after client over the past two years. Two years ago it did not exist. Today it is a budget line that grows with usage rather than with seats, and almost no one has decided who owns it.

The cost scales like payroll, not like software

Here is the uncomfortable equation underneath. Per-seat software has a ceiling built into it. You pay a flat fee for each employee, and your bill stops climbing when your headcount does. Usage-metered intelligence has no such ceiling. The bill climbs with how much work the digital teammate does, and the leaders are deliberately pushing more work onto it every quarter. So the more capable your AI colleague becomes, the more it costs to run, and that cost arrives per employee, in the same shape as a wage.

That is precisely why this turns into a human resources problem rather than a procurement footnote. When the cost of getting work done scales with output and lands per head, it obeys the same budget logic as labor. The machine that absorbs a task does not arrive with a license fee. It arrives with an operating cost that behaves like a salary, and it sits next to the salaries on the same page.

This is the part most boards have not yet put together. The same force that lets a company take cost out of its human workforce creates a new variable cost that grows with use. Human resource cost falls on one side of the ledger while operating expense rises on the other. The net is not an automatic saving. It is a substitution, and whether it improves the margin or quietly erodes it depends entirely on the unit price of the machine work. That single number, the price of the underlying intelligence, decides whether the trade is brilliant or ruinous.

No lock-in is the lever boards are not pulling

The second chart in the Ramp data is the one that should change how every owner thinks about this. Unlike software, artificial intelligence carries no vendor lock-in, and the most advanced adopters know it. The top one percent of companies use a median of eight different AI vendors. The top ten percent use five. The median uses two. The leaders are not married to a single provider. They route each piece of work to whichever model does it best and cheapest, and they switch without the migration pain that a per-seat software contract was designed to inflict.

That is the lever almost no board is pulling. If AI cost is a per-employee operating expense that scales with use, then the unit price of the model is the largest single determinant of whether that line stays sane. And the work itself is portable in a way software licenses never were.

Consider how large the lever actually is. GLM-5.2, the open-weight model from the Chinese lab Z.ai, was trained entirely on Huawei chips under US sanctions and released in mid-June. On coding it matches the closed flagships, scoring 74.4 on FrontierSWE against Claude Opus 4.8 at 75.1, and beating GPT-5.5 on SWE-bench Pro. It does that work at roughly one sixth of the output price, $4.40 against $25.00 per million tokens. The early benchmarks came partly from the vendor and independent verification is still under way, so the exact ranking will move over the coming months. The price gap of roughly six to one will hold. The same coding output, for a fraction of the per-token cost, and it drops into Anthropic’s own Claude Code by changing two environment variables. You keep the interface your engineers already use, and you swap the engine underneath. The cost line that boards treat as fixed is in fact the most negotiable line they have.

The variable nobody put in the model: who controls access

There is a catch that turns this from a procurement question into a risk question. With AI, for the first time, the access to a tool your business depends on can be switched off by someone other than you. In mid-June a US export-control directive barred foreign users from Anthropic’s strongest Fable-class model, and those models went offline. A capability that sits inside your daily workflow can disappear overnight, by directive, with no breach of contract and no clause that procurement could have negotiated away.

So the per-employee AI cost line carries a property no payroll line has ever had. It is a single-supplier dependency on a capability that a vendor or a government can revoke. That reframes the choice of model from cheapest per token to something sharper. Can this capability be taken away from me, and what happens to the work when it is.

This is where open weights and sovereign hosting stop being an ideological preference and become ordinary balance-sheet hygiene. An open-weight model under a permissive license, running on European sovereign infrastructure, is a cost lever and a continuity guarantee at once. Scaleway began hosting GLM-5.2 in Paris in late June as the first sovereign European provider to do so, which means the weights cannot be revoked and no line of code leaves the data center. For an owner or a board, the AI line has become two questions at the same time, a margin question and a dependency question, and pricing either one wrong is pricing the business wrong.

The companies that already see it

Read the Ramp curves again with this in mind and the leaders look different. They are not reckless spenders. They are companies that already treat machine intelligence as a workforce, with the same discipline of unit economics and multi-sourcing that finance has always applied to labor and to suppliers. That is why they run eight vendors and not one. They are managing a cost that scales per head, and they refuse to let any single provider own either their margin or their continuity.

The question has quietly stopped being how much AI costs. It has become who inside the company governs it like the workforce it is turning into. The boards that keep this in IT, treating it as a subscription to renew rather than a labor cost to manage, are not saving themselves the trouble. They are deferring a decision while the line keeps growing.

This is the kind of cost that does its growing while no one is watching. Whoever waits will look up in two years to find that the largest variable cost in the business matured into a payroll line, controlled by a supplier they never chose to depend on, and they never saw it coming.

Gerhard Kürner is CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering.

There is a quiet moment before every market repricing where the data still looks calm and the people inside the companies already know it is not. We are in one of those moments with AI and work, and the calm is being badly misread.

Torsten Slok, chief economist at Apollo, published a chart that captures the calm perfectly. Weekly employment data across the United States, plotted cleanly, showing zero evidence of AI-driven job losses. His reading is almost cheerful: firms are hiring AI implementation experts, the data center buildout is lifting wages, the whole thing is Jevons paradox in real time, cheaper technology creating more demand and more work. At the same time, individual companies have announced tens of thousands of layoffs this year and named artificial intelligence as the reason. More than 142,000 tech workers gone in five months.

Both pictures are accurate. That is the part almost everyone gets wrong. The flat national line and the brutal company headlines are describing the same economy from two different distances, and the gap between them is not a contradiction to be resolved. It is the most expensive thing in business right now, and it is worth understanding exactly why.

Two numbers measuring two different worlds

Slok is looking at the net balance. Every job in the country, added up, week over week. At that altitude the AI signal dissolves into the churn of an economy that creates and destroys millions of positions a month. Through April, employers announced roughly 300,000 cuts in total, down about half from the year before. Healthcare hires, construction hires, transportation hires. The line stays flat because somewhere a job vanishes and somewhere else one appears.

The layoff headlines measure the opposite thing. Gross announcements from individual firms. AI was named as the reason for around 21,000 cuts in April alone, roughly a quarter of that month’s total, the top stated reason for the second month running. That number is real. It is also nearly invisible at the national level, because it sits almost entirely inside one sector.

So nobody in this debate is lying. The economist with the flat line is right about the balance. The journalist with the layoff count is right about the disruption. The flat line simply cannot see who is losing and who is gaining, and that composition is the whole story.

How much of this is even AI

Here the ground softens for the doom side, and it is worth saying plainly. A large share of these AI-attributed layoffs are not actually caused by AI.

Oxford Economics concluded in January that firms are not replacing workers with AI on any meaningful scale, and that some companies are using artificial intelligence as cover for ordinary cost-cutting. Sam Altman, who has every reason to talk up what the technology can do, admitted there is real AI-washing, where companies blame AI for layoffs they would have made anyway, alongside the genuine displacement. Peter Cappelli at Wharton put it most bluntly. Companies announce cuts on the logic that AI will cover the work. They have not done it. They are hoping.

A finance chief who wants to trim payroll in a soft quarter now has the most fashionable justification in a decade. The label does work the technology has not yet done. Even Andy Challenger, whose firm produces the layoff data everyone quotes, is careful: regardless of whether individual jobs are being replaced by AI, the money for those roles is being moved toward it.

The bet hiding inside the layoff

If companies are cutting heads and pouring capital into AI, the productivity gains should be visible by now. They are not, at least not yet, and this is where the analysis gets interesting, because the absence is not random but structural.

Around 80 percent of companies deploying AI have reported workforce reductions. According to Gartner, those cuts have not translated into stronger returns on investment. The chief AI officer at Cognizant, again a person with no reason to undersell the technology, said he does not know whether the cuts connect to real productivity gains, and that it will take another six months to a year before companies see them.

Read the order carefully, because the order is the whole thing. Firms cut the people now. The productivity is supposed to arrive later. The cut is not a response to a realized gain, it is a position taken against a future one. And the most recent reporting tells you what the position actually is: the companies executing the deepest cuts in 2026 are simultaneously posting their strongest-ever results and raising capital expenditure to levels that, in their own words to investors, make human payroll look small. The budget freed by the layoffs flows straight into compute. Cloud contracts, hardware, data centers. Money out of people, money into infrastructure, on the wager that the infrastructure eventually pays back more than the payroll did.

This is the part the headline number cannot show you, and it is the part that matters if you are reading these companies as assets rather than as employers. A firm that has cut its headcount and booked the saving has not become more valuable. It has converted a certain cost into an uncertain bet and recorded the result as efficiency. Those are not the same act, and the accounts do not distinguish them. The saving is real and lands this quarter. The productivity that is supposed to justify it is a promise with a maturity date nobody will name. So the leaner company and the stronger company look identical on the page right now, and they are not the same company. One has cut into genuine slack. The other has cut into its own capacity and is praying the technology backfills it before anyone notices the gap. From the outside, this cycle, you cannot yet tell them apart from the margin line alone. That is the single most useful thing to understand about the present moment, and almost no price reflects it.

The reason the gap is this hard to see from a spreadsheet is that it lives one level below the numbers, in the actual work. After years of building AI systems and watching where they genuinely take load off a team and where they quietly do not, the tell becomes legible: the firms booking a saving have mostly automated the visible, nameable tasks, and left untouched the tacit judgment that was the real reason the role existed. That residue does not appear in a headcount line. It appears eighteen months later, as the thing the AI was supposed to cover and did not.

Watch the split, not the announcements

The clearest signal is not in what any one company says. It is in the fact that the most deliberate players are doing opposite things, and the divergence is information.

IBM tripled its entry-level hiring in 2026, on the reasoning that AI handles many junior tasks but still needs a human in the loop. Other firms are cutting exactly that layer as fast as they can. Look closely at what separates the two bets, because it is not optimism versus caution. It is a reading of where the durable value sits. AI replaces routine, not experience. The junior doing routine work is not only a cost, the junior is the mechanism by which a company manufactures its future seniors. Cut that layer and this year’s margin improves while the supply of the one thing AI cannot yet produce, judgment built from years of doing the work, quietly stops being made. The company that cut looks more efficient now and has mortgaged a capability that does not show up as a liability anywhere. The company that kept hiring looks heavier now and owns an asset its competitors are busy destroying.

Neither bet is provably right yet, and that is the point. When the most sophisticated capital in a sector splits this cleanly on the same facts, it means the repricing has not happened. The market is still treating the cutters’ leaner numbers as straightforwardly good. It has not yet started asking the harder question of what was cut, slack or capacity, bet or saving. When it does start asking, and it will, the gap between those two groups is where value moves. Anyone who can read which is which before the question gets asked is reading three years ahead of the print.

The calm is the opening

None of this is fate, and that is the part both the panic and the complacency miss. The flat line is not destiny, it is a snapshot taken before the interesting part. What it cannot see, composition, the missing productivity, the mortgaged pipeline, is exactly what separates the companies that will be worth more from the ones that will be bought. That separation is not yet in any price, which means seeing it clearly is still cheap and acting on it still counts as foresight rather than catch-up. The quiet moment is not a time to wait. It is the short window where clarity is still an advantage instead of a postmortem.

The chart says nothing happened. Read it properly and it says everything is about to. Whoever waits for the headline number to move will find, in a few years, that the repricing was already underway while the line looked flat, and that they never saw it coming.

Gerhard Kürner is an AI Value Creator and CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering. Not a theorist, but the analyst who sees more, from years of shipping AI and tech projects paired with an ongoing eye on the research.

Last week at 506.ai we sat down to plan the next sprint for our platform. The agenda was concrete: a meetings application, an integration with the Austrian RIS (Rechtsinformationssystem, the national legal database used across courts, agencies, and law firms), and a handful of smaller pieces.

We were still in the room negotiating scope and sequence when our product engineering pipeline overtook the conversation. By the time the meeting closed, the pipeline had not only shipped the items on the agenda. It had already pushed the next round of updates on top of them.

The old shape of this meeting is familiar to anyone who has ever run a software company. You translate requirements into tickets, estimate them, cut scope, pick a release date one or two quarters out. With every passing meeting the codebase drifts further behind the conversation that produced it.

That shape is gone. The codebase now outruns the meeting, not as a heroic engineering effort but as the normal output of an agentic engineering pipeline that runs faster than the strategy conversation around it.

This is the moment I understood, inside my own shop, that the dam in enterprise software has already burst. Production now runs ahead of the requirements conversation. The bottleneck was never customer demand or market size, it was engineering throughput colliding with customer specificity, and that bottleneck is gone. The water is at our ankles while most of the industry is still arguing about the architecture of the wall.

Robert Smith is right, and stops one step too early

Robert F. Smith, founder and CEO of Vista Equity Partners, has been more direct on this than almost anyone with a hundred billion in software AUM. In November he told CNBC that “AI will enable enterprise software to eat services.” Earlier in the year, in front of his PE peers, he was sharper: “40% of people here will have AI agents next year. The other 60% will be looking for jobs.”

Smith has put real capital behind the thesis. Vista has built what it calls an “Agentic Factory,” a portfolio-wide infrastructure to retool its software companies for the AI era. 30 Vista companies are already generating revenue from the conversion to agentic AI, with another 30 to 40 in flight. He sees operating margins moving from 25 to 40 percent and beyond for the companies that get this right.

He is correct on every count. He is also one step short of where this actually lands.

Smith is still framing the move from inside the software-PE-owner mental model: better margins, higher growth, sovereignty over data, but the category itself stays intact. Software companies remain software companies, they just become much more profitable software companies.

I would argue the move is bigger. The category is collapsing.

Enterprise software stops being a product and becomes a colleague. You don’t license it, you hire it. You give it a job description, onboard it, assign it a manager, and run quarterly reviews on it. And when it stops performing, you let it go.

This is what we mean when we say Service-as-a-Software. Not a chatbot bolted onto your CRM. Not Agentforce on top of the same CRUD database that Satya Nadella correctly diagnosed last year as the underlying form of every SaaS application. A full unbundling of what software is and how customers buy it.

The fashion house arrived before the AI lab did

A few weeks ago Jack Cantillon at Green Room argued that the future technology founder will look like Jonathan Anderson at Dior. A creative director shipping collection after collection, surrounded by ateliers, supply chain, and one grown-up keeping the operation honest. He is right about the shape of the producer.

He stops short of what happens to the product.

In roughly a thousand conversations with boards, owners, and operators over the last three years, I have watched enterprise customers start to behave like fashion buyers. They no longer ask “fix this bug” or “add this feature.” They ask “what’s next?” They want to be inspired, to know what the next collection looks like, to see a release rhythm closer to a runway show than a SaaS roadmap.

For thirty years, enterprise customers have been trained to wait: new features once a quarter, a redesign every five years, a migration project every decade. That training is dead. The half life of customer patience has collapsed to weeks.

Two consequences follow.

First, the product roadmap as a static document is finished. Roadmaps are now seasons, and each one needs a point of view, not a feature list.

Second, customer acquisition is no longer a marketing problem. It is a curation problem. The vendor that walks into a board meeting with a coherent thesis on what the next 90 days look like wins the contract. The vendor that arrives with a deck of “robust capabilities” gets politely thanked and forgotten.

Shopify is the proof, and the industry copied it

The cleanest market signal of where this lands sits in a Shopify memo from April 2025. CEO Tobi Lütke wrote, and then publicly posted on X to get ahead of leaks, that no team at Shopify can request new headcount or resources before demonstrating that AI cannot do the work. The full sentence: “Teams must demonstrate why they cannot get what they want done using AI. What would this area look like if autonomous AI agents were already part of the team?”

Eight months later the same policy had been adopted in some form by Meta, Microsoft, Google, and Nvidia. The Lütke memo became a category template.

This is not a productivity story. This is a buying-behavior story. Once an enterprise has accepted that every workflow must be defended against an AI alternative, the same logic applies to every vendor in the stack. Salesforce, Workday, ServiceNow, Adobe, and every smaller piece of enterprise software gets asked the same question. Can an internal or external agentic system do this work for less, faster, and with better data ownership?

If you are a PE-owned software company and your top customers have a credible internal AI engineering capability, you have a much shorter runway than your last board pack assumed. The replacement risk is no longer a competitor with a better product. It is your own customer with sixteen engineers on Cursor and a CEO mandate to defend every headcount and every license against an AI alternative.

Bret Taylor saw this two years early

Bret Taylor is the cleanest operator-thinker on what this looks like at scale. Ex-Salesforce co-CEO, chair of OpenAI, founder of Sierra. And Sierra does not sell customer service software. Sierra sells customer service, with outcome-based pricing. You pay per resolved ticket, not per seat.

This is the dream of every CFO I have spoken to in the last year. A variable cost line that scales with revenue, not with headcount or contract length. It is the nightmare of every classical SaaS CEO. The seat-based moat dissolves into a service that anyone with the right model access can replicate or undercut.

Marc Benioff is at the back of the same race. Agentforce is the architectural equivalent of stapling a colleague onto a filing cabinet and asking the customer to pay for both the colleague and the cabinet, by the seat. That math will not survive the next downcycle.

The double transition

Two transitions have to happen at the same time for this trade to actually book. Most investor commentary covers the first one and skips the second.

The first is inside the vendor. The fashion analogy goes deeper than the product side. Dior does not produce 20 collections a year because Jonathan Anderson is talented. It does so because LVMH built a machine around him: ateliers that prototype in days, supply chains that turn samples into stocked garments, retail that puts them on shelves in 80 cities, and a communications operation that builds a story around each drop. The creative director sits at the center of that infrastructure.

Software companies today do not have that machine. They have engineering organizations built for waterfall releases, product teams built for quarterly cycles, customer success teams designed to defend SaaS renewal. Shipping a new “collection” each quarter is not a product roadmap problem, it is an operating model rebuild that touches engineering, product, sales, finance, and HR at the same time, harder than the on-premise to cloud move. Most of the C-suites I sit with are still pattern-matching to that cloud transition, but this one is different.

The second is outside the vendor. This is the harder problem.

Enterprise buyers have spent twenty years building procurement, IT security, vendor management, and training capabilities for one shape of software: per-seat SaaS that you license, integrate, train on, and renew. Their organization is calibrated for that shape. RFP templates, ISO 27001 vendor reviews, change management methodologies, and budget categories all assume software is a tool you install.

Service-as-a-Software does not fit that shape. It looks like a vendor on paper, behaves like an employee in operation, and charges like a service provider. Procurement does not know how to onboard it, IT security has no template for it, and the line manager does not know whether to treat it as a tool or a hire. This friction is invisible in a pitch deck and lethal in deployment.

This is why market entry and product entry decide everything. Walking in with a strategic vision sale is a way to get strung along for nine months. Walking in with a narrow, ROI-obvious use case wins three things at once: a fast first transaction, a deployment story the customer’s organization can metabolize, and the right to expand from there. The right entry points are boring and unglamorous: inbound ticket triage, first-line queries, reporting drudgery, internal IT help desks. Land where the customer can count the savings in week six.

The investor commentary skips this entirely. It is the part that decides which software companies actually book the margin expansion Smith is forecasting.

What this means for owners and PE

Here is the operative summary I have been walking owners and funds through.

One. Software portfolio companies have somewhere between 24 and 36 months to flip the entire model, not just the pricing but the whole shape. The vendor that used to sell payroll software starts running payroll itself, agent-based, billed per processed payslip. The vendor that used to license a CRM seat starts operating customer relationships on behalf of its customer, billed per qualified opportunity or per resolved ticket. The vendor that used to ship an HR suite starts onboarding new hires as a service. Pricing follows the service flip: per-seat dies, outcome and consumption based pricing wins. The vendors that flip first reset their growth curves and capture the service margin. The vendors that wait get repriced by customers anyway, in the wrong direction, and lose the service layer entirely to someone else.

Two. The most interesting arbitrage is no longer inside the software category but adjacent to it. Service businesses with strong customer ownership and deep workflow data are about to become software businesses overnight: mid-market accounting firms, staffing agencies, BPO operations, boutique consultancies. If they own the workflow and the data, an agentic layer turns them into outcome-based vendors with SaaS-like margins. This is what Vista is hunting at scale, and where lower-mid-market PE and family offices will see their cleanest entries over the next 24 months.

Three. The equity story of a software company is no longer ARR plus net retention. It is share of customer workflow, and the rate at which that share is growing. Any board still reporting only on logo retention and seats is flying on instruments from 2015.

Four. HR cost in software companies is going to fall hard. OPEX in compute and model usage is going to rise to meet it. The shape of the income statement will be unrecognizable in three years. If your portfolio company’s CFO has not built a P&L scenario for this, that is the first board meeting to schedule next month.

The trade

A planning meeting last week, a concrete agenda, and a pipeline that ran ahead of the conversation. Software that had already moved past what we were debating before we left the room.

This is where software ends up: not as a thing you license, but as work that gets done.

Smith is right that software will eat services. The part he undersells is what happens to software itself. Software stops being a category and starts being a workforce. The PE funds and owners who internalize this first will price software acquisitions like service companies and run them like software companies. That is the trade for the next cycle.

The dam isn’t bursting, it already burst. Some of us are working in the river while most of the industry is still arguing about the wall.

Gerhard Kürner is an AI Value Creator and CEO of 506.ai, the European platform for Service-as-a-Software and agentic engineering. Over 1,000 conversations across the last three years with boards, owners, and PE funds in DACH and Europe.

This is not just another round of “cloud investment.”
This is the industrialization of intelligence and it marks a qualitative leap.

The Numbers Are Mind-Blowing

For 2026, analysts expect the following capital expenditure (capex) from the big players:

• Amazon, Google, Meta, Microsoft: combined $650–670 billion

• Including Oracle: approaching $700–800 billion in a single year

• A 60–70% increase from 2025

That’s more than the GDP of many countries, spent almost entirely on AI infrastructure.

At the same time, bond issuances are exploding. Meta alone raised $30 billion, Alphabet issued global multi-currency bonds, and more record deals are coming.

Why Debt Now? The Qualitative Leap

For years, these companies were the ultimate “cash kings.” They funded every single expansion purely from their enormous free cash flow. That era is now over.

The speed and sheer scale of the AI buildout have become so extreme that even their record-breaking cash flows are no longer sufficient. By turning to massive debt financing, the hyperscalers are moving to the next level. Leverage is no longer a last resort. It has become a strategic necessity to stay in the race.

This shift from 100% internal funding to large-scale borrowing represents a new quality in Big Tech’s behavior and signals absolute conviction: they believe the long-term returns from owning superior AI infrastructure will far outweigh the cost of capital.

What Are They Actually Building?

Three core pillars define the new AI infrastructure:

1. Hyperscale Data Centers Facilities no longer measured in square feet but in gigawatts.

2. The Full Supply Chain Latest GPUs, advanced cooling, transformers, and fiber optics, all scaled at unprecedented speed.

3. Energy Infrastructure: The Growing Bottleneck

This is where the real story lies. One modern AI data center can consume more electricity than a major city. Some planned campuses will need their own dedicated power plants.

Energy has become the single biggest bottleneck of the entire AI race. Hyperscalers are now directly negotiating with utilities, investing in Small Modular Reactors (SMRs), gas peaker plants, and massive renewable-plus-storage projects.

Europe’s Challenge and Opportunity

While the U.S. hyperscalers push forward at full throttle, Europe is struggling to keep pace. The combination of regulatory hurdles, slower permitting processes, and limited access to cheap, reliable power makes it extremely difficult for the EU to compete on equal terms.

But that doesn’t mean we should give up.

On the contrary: Europe must use every single resource it has (land, existing grid capacity, nuclear know-how, renewable potential, and skilled talent) to secure at least a relevant slice of the future AI infrastructure.

Companies like TechVera are already showing the way. They are actively building a European service that focuses on exactly this challenge: delivering high-performance AI infrastructure within the EU by intelligently utilizing local resources and navigating the regulatory landscape.

Final Thought

We are witnessing the largest and fastest re-industrialization of the digital world in human history.

This is no longer about better chatbots or image generators.
It’s about building the physical rails on which the era of superintelligence will run.

The winners of the next decade won’t necessarily be the companies with the best AI models, but those who own and operate the best AI infrastructure.

Big Tech has just placed the biggest corporate bet in history by moving from pure cash-flow investing to large-scale debt financing. Energy is now the decisive factor, and for Europe, the time to act with every available resource is now.

What’s your take?
How fast do you expect Enterprise AI to transform your industry?
And how can European companies best position themselves to benefit from this infrastructure wave?

Drop your thoughts in the comments below.

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In April 2026, a team from Imperial College London, Internet Archive, and Stanford published a paper that has so far gained surprisingly little traction in the broader business discussion. The paper is titled “The Impact of AI-Generated Text on the Internet” and delivers the first robust empirical measurement of how large the share of AI-generated content on the open internet has actually become.

The headline number: 35 percent.

35 percent of all newly published websites in mid-2025 were either fully AI-generated or AI-assisted. In November 2022, before the launch of ChatGPT, that share was zero. In three years, roughly one third of the publicly accessible internet has flipped, measured against a representative sample rather than extrapolated from industry surveys.

The number itself is not the truly remarkable finding of the study. What is remarkable is the speed at which this shift has happened, and the fact that it has remained practically invisible to most companies.

What the study now confirms was already discernible twelve months earlier

In the summer of 2025, I conducted strategic analyses for several industries, all converging on the same trajectory. Anyone who at that point combined the adoption curves of mainstream AI tools, the publication frequency of new coding agents, and the rapidly falling unit costs of synthetic content could compute the next twelve months cleanly. The conclusion at that point was not speculative, it was arithmetic.

What the Stanford and Imperial paper now delivers is therefore not new information. It is the retrospective validation of what was already visible, provided one had connected the right indicators. This is precisely where the actual strategic problem begins, because the majority of executive teams do not perform this connection.

The scope is the variable, not the direction

In more than 1,000 conversations on AI and software integration with executive boards, supervisory boards, investors, and management teams, I have observed a consistent pattern over the past twelve months. The direction of change is broadly accepted, no one seriously disputes that AI is changing business. The scope of this change, however, is being systematically underestimated, not by ten or twenty percent but by entire orders of magnitude.

This is the observation that the paper indirectly confirms. Anyone who cannot measure the speed of an ongoing restructuring is also not in a position to address it. And most organizations simply do not measure.

Where the scope gets lost

In the summer of 2025, I had a conversation with a long-standing business partner and friend. His business consists of e-commerce and website creation for SMEs, a solid model with a good customer base and a classic mid-market profile.

My analysis at the time was simple in substance. Website creation will, within a window of twelve to eighteen months, no longer be a meaningful cost or time factor, but will become effectively free and accessible to anyone. The economic value therefore shifts from building to customer access. Whoever holds the customer can offer them adjacent services that take work, time, and cost off their plate. Whoever loses the customer no longer has a bridge to the next layer of value creation.

The strategic recommendation was correspondingly clear. Build a second line, immediately, with fully automated website creation positioned as customer access rather than as product, and from there derive the next service layer. The window was twelve months.

My friend believed me in principle. We have known each other for a long time, the trust was there. But the scope I described, he did not see.

He and his partner subsequently automated, did clean work, and built their own solution. However, they set up the project as an optimization of the existing model rather than as the construction of a new value creation architecture. The urgency was not translated into architecture, which in economic terms is the same effect as no movement at all.

Eight months later the market is a different one. Coding agents and agentic systems today form a complete stack that did not exist in this form in the summer of 2025, and what was then considered demanding automation is now entry level. The next wave is already clearly visible in the indicators. It will no longer be about websites, it will be fully agentic, and it will arrive within a horizon of six to twelve months.

Three waves arriving in short succession. Whoever misses one catches up with significant effort. Whoever misses two has a different business. Whoever misses three is no longer part of the market.

The European defensive line is built in the wrong place

The typical reaction in European boardrooms follows a stable pattern that condenses into a few sentences. “That is the US. Here it takes longer.” This position is not analytically tenable.

The paper analyzed exclusively English-language websites, and no comparable study yet exists for the German-speaking market. The assumption that adoption here proceeds substantially more slowly is therefore exactly that, an assumption, and not a measurement. What is genuinely slower in Europe is decision-making within organizations, an endogenous variable rather than a market characteristic. The markets themselves do not adhere to the schedules of board meetings.

Whoever confuses speed with geographic distance defends along a line where the threat does not actually originate.

The starting advantage that is currently expiring

This is where the strategic point sits, the one missing from most discussions. Established companies with an existing customer base, accumulated domain knowledge, and a functioning sales structure hold a historic starting advantage in the current transition over AI-native startups, larger than in any previous technology wave. The reason is arithmetic: building a solution has become drastically cheaper, building a customer base has not.

This inverts the classic startup logic. For decades, the rule was that better technology beats established distribution over time. In the current phase, established distribution beats any technology that can be replicated overnight, but only on the condition that it moves.

This advantage has an expiration date. A realistic estimate sets it at twelve months. Anyone who does not begin within this window to extend the business model in an AI-native direction loses the advantage gradually. The competitor who takes it typically does not come from the same industry but from an adjacent domain, with a fundamentally different value creation architecture.

What every leadership team must decide in the next 90 days

Three inventories form the minimum frame for the strategic response.

The first inventory concerns the business model itself. Which service is being sold today that will be free or close to free in twelve months? This position cannot be secured by optimization, it must be redefined, including the fundamental question of what the customer will still pay for in the changed market picture.

The second inventory concerns customer access. Which adjacent service takes time, money, and work off the customer’s plate? This is where the value creation of the next three years sits, not in today’s product. Whoever does not define this transition cleanly loses the bridge between current business and future value creation, and with it access to their own market.

The third inventory concerns the company’s own speed. How many weeks lie between an idea and a productive pilot? If the answer is more than twelve weeks, speed itself has become the strategic bottleneck. This inventory is, in my experience, the most uncomfortable, because it directly addresses the organizational structure and the established decision pathways.

The position in which it is now decided

Three years, from zero to 35 percent, a study that measures what was visible twelve months earlier, and a scope that continues to be underestimated. There has not been a better time in a generation to play radically inside such a transformation. Anyone with an existing customer base and accumulated domain knowledge is in the better position. Anyone who waits transfers this advantage into the equity story of a later transaction, instead of into their own current value creation.

Begin the inventory this week. Build the pilot within the next 90 days. Make the architecture decision before the next fiscal year. The scope has been measured. The time to ignore it has run out.

Gerhard Kürner is an operator, investor, and specialist in the AI-driven transformation of business processes and enterprise software. CEO of 506.ai (Service as a Software and Agentic Engineering made in Europe), Inventor of Kollega, Your AI Colleague (mykollega.ai), and Co-Founder of Choose European.

Link to the paper: https://ai-on-the-internet.github.io/ai-on-the-internet.pdf

Goldman Sachs called it something else. Goldman Sachs called it overdue.

What the report actually says

Gabriela Borges from Goldman, Rick Sherlund, and Sanjay Poonen, CEO of Cohesity and former President of SAP, agree on more than you would expect. AI is not eating software. AI IS software. The market does not shrink, it expands. The total addressable market grows, not falls.

And yet the panic is not an overshoot. It is the market pricing in a new reality.

Three points all three experts make directly or by implication:

1. Price per user is collapsing. If one AI colleague does the work of five analysts, nobody buys five licenses.

2. Legacy systems do not need optimization. They need to be rebuilt. Bolt-on AI is marketing, not product.

3. Incumbent moats buy time, not immortality. Sherlund puts it plainly: “AI will very likely erode them over time.”

John Zito, Co-President at Apollo, said the uncomfortable part even more bluntly on CNBC in February: “The marginal cost of producing software is moving toward zero.” That is not a forecast. That is a financial statement from one of the largest private equity houses in the world.

The real message sits one layer deeper

Software will be used many times more in five years than it is today. At the same time, it will be many times cheaper to produce. That is the uncomfortable equation:

More software. Much more. And every single euro of it will be harder earned.

This equation breaks the classic SaaS logic: high margins, sticky customers, recurring revenue, per-seat pricing. Fortune lists the three forces dismantling the model: switching costs as artificial anchors, collapsing barriers to entry through AI coding agents, and the redefinition of workflows through autonomous systems. None of these forces is speculative. All three are already in the market.

Anyone who still thinks this is a product management topic at the software vendor has not understood where the wind is blowing from. This is a topic for every CFO, every CIO, and every supervisory board in every company that uses software. Which is every company.

Why legacy organizations will not keep up

This is where it gets genuinely uncomfortable for established European enterprises.

The instinct of many boards: “We have legacy code. We will modernize it with AI.” McKinsey promises 40 to 50 percent faster modernization. HFS Research points to 30 percent cost reduction. Sounds great.

It is also not the problem.

The problem is not the legacy code. The problem is the legacy organization wrapped around it. Poonen says it almost in passing in the Goldman interview, and it is the most important sentence in the entire report: “The bigger the install base, the tougher it is to pivot.” Or even more directly: “God created the world in seven days because he didn’t have an install base.”

Install base here does not only mean software. It means processes, roles, budgets, works council agreements, supplier contracts, job descriptions, bonuses. It means the entire organization that was built around the old software.

AI modernization of code is technically solvable. Breaking the organizational structures built around it is not.

This is the point where consulting, support, and offerings hit their limit. You cannot teach an organization to outpace itself while its incentives are wired to keep the old operation stable. Sometimes the organization has to break before it can be rebuilt. That is uncomfortable. It is also not negotiable.

The PE dimension: European investors are getting nervous

This is the part most European boards have not yet put on their radar.

Private equity has deployed roughly a quarter of its total volume into software over the past five years. In the 2021 and 2022 vintages, software assets were acquired at 15 to 20 times revenue, with leverage ratios of 6 to 7 times EBITDA. The assumption: stable retention, predictable growth, premium multiples at exit.

That assumption is dead.

Bloomberg reported in late February: secondary market buyers are demanding up to 20 percent discounts on PE software portfolios. A few weeks earlier, it was 5 percent. S&P Global counts 130 billion US dollars in software acquisition loans trading below 90 cents. That is the zone where equity is already impaired. PitchBook openly calls it a “Software Reckoning” in its current Analyst Note.

For European corporates that are majority PE portfolio companies or have PE investors on the cap table, this translates concretely:

LP patience is gone. The Distribution Drought model from Allianz forecasts a range from minus 3 to plus 8 percentage points in distribution rates for 2026. The difference between the top and bottom of that range: whether the software assets in the portfolio become AI capable, or have to be written down.

What this means: if your company has a PE investor, this conversation is coming to the table in the next 12 months. Not as a strategic option. As pressure. LPs want DPI, not IRR. Exits want multiples, not stories. And multiples only go to companies that can tell a credible AI value creation story. PwC puts it coolly: “The ability to articulate a credible AI value creation story is no longer optional. It is a prerequisite for liquidity.”

For European companies this is both an opportunity and a threat. Opportunity, because capital on reasonable terms will flow into AI native transformation. Threat, because companies that do not move will either be written down or sold. At discounts, to buyers who mean it more seriously.

What to do now

Offerings and support are the easy part. Every consulting firm, every software vendor, every system integrator now has an AI offering. That is not the bottleneck.

The bottleneck is the willingness to attack your own organization before someone else does.

Three moves every leadership team should initiate in the next 90 days:

First, an honest inventory of all software contracts. Which tools are still paid per seat for work that could already be handled by AI colleagues? This is not a procurement exercise. This is strategic repricing.

Second, a brutal inventory of the processes that exist not because of software but because of organizational history. Who protects which process because their job depends on it? That is the real technical debt.

Third, a decision: bolt-on or rebuild. Poonen calls it “living in the old house while you build the new one next door.” Sounds comfortable. It is not. Because the old house must come down once the new one is ready. Most organizations fall too much in love with the old address.

Closing the bracket

Goldman Sachs writes that the numbers must contradict the story before markets can stabilize. That is correct. But it also applies to the other side.

The story is not that AI is eating software. The story is that AI is eating the existing business model of software while the amount of software itself is exploding.

Read from the investor perspective, this is an 800 billion dollar loss. Read from the perspective of a European CEO, this is an invitation. A brutal, short invitation to turn your own organization into a company where humans and AI colleagues work side by side. Not in three years. Now.

In five years, every enterprise that still exists will run hybrid teams. The question is not whether. The question is who builds those teams: the company itself, or a buyer at a discount.

Gerhard Kürner is CEO of 506.ai and Co-Founder of Choose European. 506.ai builds digital teammates for European enterprises and the public sector with Kollega, hosted in the EU, GDPR compliant, designed for hybrid teams of humans and AI. Everyone taking enterprise AI transformation seriously finds the entry point at mykollega.ai.

The ranking was identical across every reviewer model: Codex with GPT-5.4 came first, Codex with GPT-5.3-Codex second, Claude Code with Opus 4.6 third, and the human researchers finished last. The order held across every judge and across every task variant, which rules out the easy explanation that one particular model was flattering itself or punishing the humans.

This is not a benchmark and it is not a toy task. It is a serious head-to-head comparison of agentic AI systems and human economists doing real empirical work, and it quietly closes the chapter on the idea that AI is merely a helper for coding and drafting.

What the study actually measured

The research question was genuine applied microeconomics, namely estimating the causal effect of DACA eligibility on the probability of working full-time among Hispanic-Mexican, Mexican-born individuals in the United States, using American Community Survey data from 2006 to 2016. Grundl ran the comparison across three difficulty levels that progressively constrained the researcher, starting with full freedom and moving to a prescribed research design and then to a pre-cleaned dataset.

The human side of the comparison was serious. 146 research teams were recruited, 87 percent held PhDs, 67 percent were faculty, and each team was paid 2,000 dollars to complete all three tasks. This was not undergraduates doing a homework set, it was practicing applied economists producing the kind of work they would submit to a journal.

Grundl then ran 100 independent instances of each AI system on each task, for a total of 900 AI replications. Every run produced actual code, actual data cleaning choices, actual regressions, and an actual written report in the style of an academic submission. Three reviewer models, Gemini 3.1 Pro, Opus 4.6, and GPT-5.4, then read 300 bundles of four submissions each and wrote comparison reports ranking them on identification, robustness, and substantive quality.

The detail that should end the debate

The more interesting finding is not that AI won the ranking. It is how it won, because the victory looks different depending on which part of the distribution you inspect.

If you just compare median estimates, the humans and the AI systems land in roughly the same place, which suggests on first glance that the two groups produce similar work on average. The distributions, however, tell a very different story. Human estimates have substantially wider tails, with larger standard deviations and more extreme outliers, while the AI estimates concentrate more tightly in the middle even though individual runs can occasionally flip sign. More importantly, the written submissions themselves, when evaluated on substance rather than on a point estimate, were consistently ranked higher for the AI systems than for the humans.

The cleanest evidence against reviewer bias sits inside the paper itself. Claude Code with Opus 4.6 was one of the reviewers, and it consistently ranked the Codex submissions above its own, which is the opposite of what you would expect if the AI judges were simply flattering their own kind. If there is a bias in this tournament, it is a bias against the reviewing model’s own work, and the humans still finished behind.

What just changed

For the past three years, the dominant narrative about AI in knowledge work has been a story of assistance, a faster intern who helps you write code, draft emails, and summarize documents. Grundl’s paper ends that narrative, because empirical research is the hardest, most credentialed, and most carefully reviewed work the knowledge economy produces, and agentic AI has now been shown to do it at or above human quality in a fair fight.

The paper’s closing observation is worth reading in its original form, where Grundl writes that “agentic AI systems will allow us to scale empirical research in economics.” The operative word is scale, not assist, not accelerate at the margin, but change the order of magnitude of analytical output that a single researcher or a single institution can produce.

What this is not

It would be easy to read Grundl’s ranking as a verdict against human researchers, and that reading would miss the actual point entirely. The 146 human research teams in this study are not mediocre, they are among the strongest applied economists in the profession, with 87 percent holding PhDs, 67 percent serving as tenure-track or tenured faculty, and roughly 40 percent working directly in labor or immigration economics. These are the people who review the papers, teach the methods, and produce the empirical evidence that shapes policy, and they are not being left behind because they are bad at their jobs.

The ranking is better understood as a preview of what happens when that same level of human expertise builds on top of agentic systems rather than working alongside them. A PhD economist who runs a research question through 100 independent AI replications, inspects the forks in the analysis path, selects the robust specifications, and writes the interpretation is not being replaced by AI, she is doing research that no single researcher on the planet could have done alone three years ago, and she is doing it with a depth of robustness checking that used to be reserved for referee-response rounds.

The real implication of Grundl’s paper is not that human researchers are obsolete but that the gap between augmented and unaugmented researchers is about to become the dominant variable in knowledge work, and the humans who pull ahead in the next decade will not be the ones who defend their turf against AI, they will be the ones who figured out how to build on it first and with the most judgment. Every research organization, every consultancy, every analytics team now needs to decide which side of that gap it wants to be on, because the technology no longer tolerates fence-sitting.

What this means for the next twelve months

Research becomes a volume problem rather than a talent problem, because a single researcher with access to agentic systems can now run hundreds of independent analyses in the time it previously took one PhD team to run one, which means robustness checks and multi-specification sensitivity analysis stop being a chore reserved for referee responses and become the default mode of working.

Peer review is likely to be rebuilt around the same tools, because the Grundl paper also demonstrates that AI reviewers rank submissions consistently across different models, which is a better inter-rater reliability than most human review panels achieve, and the journals that ignore this will be outrun by workflows that embrace it.

The bottleneck moves decisively to judgment, because when analysis itself is cheap, the scarce resource becomes the question you choose to ask, the framing you bring to it, and your ability to recognize which answers would actually change a decision. That part of the work is still human, and in the new workflow it is the only part that is reliably human.

Smaller organizations finally catch up to larger ones on analytical throughput, because a three-person team equipped with agentic tools now has the research capacity of a twenty-person department from three years ago, and the competitive gap in knowledge-intensive industries is collapsing from the bottom rather than widening from the top.

The European angle no one wants to say out loud

Europe has a demographic problem that will not solve itself, with a structural shortage of millions of skilled workers projected across the next fifteen years, and the productivity tooling we build right now is not a nice-to-have but the bridge across a labor gap that no amount of hiring or immigration policy will close in time.

Agentic research of the kind Grundl documents is one of those bridges, because a system that can take a research question, translate it into a design, write the code, run the analysis, inspect its own results, debug its own mistakes, and produce a written report that peer-ranks above human work is not really a tool in the traditional sense, it is analytical capacity that can be provisioned the way we used to provision cloud computing. Organizations that treat this as capacity rather than as a novelty will pull ahead quickly, and the ones still debating whether to pilot a chatbot will find themselves in two years explaining to their boards how they missed the moment their competitors doubled analytical throughput without hiring a single new person.

Back to the paper

Grundl is careful in the way Federal Reserve economists tend to be careful, and he is explicit that AI systems make mistakes, that different runs of the same model can produce estimates with opposing signs, and that none of this is magic. He also notes, correctly, that the same problems apply to human research teams, where inter-analyst variation in empirical economics has been documented for years and is the whole reason the Huntington-Klein et al. many-analysts study exists in the first place.

The insight that matters more than the caveats is what becomes possible when you can run an analysis 100 times for the cost of one human team-month, because in that regime you stop needing any single run to be perfect and you start aggregating, inspecting dispersion, and surfacing the forks in the analysis path that produce different conclusions, which gives you better empirical work rather than worse because variance becomes observable instead of hidden.

That is the new standard for serious empirical work, not AI replacing humans, but a workflow in which human judgment selects the question and reads the results while hundreds of parallel analyses fill in the space between. The humans finished last in Grundl’s ranking, but the humans also designed the experiment, chose the task that made the whole comparison visible, and wrote the paper that forced the rest of us to notice, which remains the part of the job that is still worth doing.

Gerhard Kürner is CEO of 506.ai. He writes about enterprise AI, the European productivity question, and what actually changes when agentic systems cross quality thresholds.

Reference: Grundl, S. (2026). A Comparison of Agentic AI Systems and Human Economists. Federal Reserve Board of Governors. claude-code-economist.com

Seit über einem Jahr zeige ich in Keynotes eine einzige Folie, die fast jedes Publikum zum Nachdenken bringt.

Aber viel wichtiger: Dieselbe Folie öffnet bei 506.ai jedes Kundenprojekt. Bevor wir über Anwendungsfälle, Modelle oder Integrationen sprechen, legen wir diese eine Grafik auf den Tisch.

Eine J-förmige Kurve. Vier Phasen: Initial-Investition, Strategie und Kompetenzaufbau, Integration und Skalierung, exponentieller Erfolg. Die Botschaft dahinter ist immer dieselbe. Unternehmen, die KI einführen, werden zunächst langsamer, nicht schneller. Sie verlieren Geld, bevor sie welches verdienen. Und die meisten steigen genau im Tal der Kurve aus, kurz bevor die Ernte beginnt.

Keynotes erreichen Aufmerksamkeit. Kundenprojekte erreichen Wirkung. Und die schwierigste Botschaft kommt immer zuerst. Wer das Tal nicht akzeptiert, bevor das Projekt beginnt, wird es im Projekt nicht überleben. Das ist keine Rhetorik. Das ist operative Risikosteuerung.

Im April 2026 hat das Stanford Digital Economy Lab sein Enterprise AI Playbook veröffentlicht. 51 erfolgreiche KI-Implementierungen in 41 Organisationen aus sieben Ländern. Über eine Million Beschäftigte abgedeckt. Die Studie bestätigt die These empirisch.

Kurz darauf greift Prof. Holger Schmidt die Produktivitäts-J-Kurve im F.A.Z. Digitalwirtschaft-Briefing auf. Titel seines Beitrags: “Die Schere zwischen KI-Vorreitern und dem Rest der Wirtschaft öffnet sich rapide.”

Damit ist die Debatte durch. Praxis, Wissenschaft und Leitmedium sind sich einig. Was bleibt, ist die operative Frage: Auf welcher Seite der Kurve steht Ihr Unternehmen in 24 Monaten?

Die Schere ist bereits sichtbar

Auf der einen Seite: Spitzenprojekte steigern ihre Produktivität um 71 Prozent. Auf der anderen Seite: Der MIT NANDA-Report zeigt, dass 95 Prozent aller generativen KI-Pilotprojekte keinen messbaren finanziellen Beitrag liefern.

Das sind keine widersprüchlichen Datenpunkte. Das ist dieselbe Kurve aus zwei Perspektiven. Die 5 Prozent in der Ernte-Phase. Die 95 Prozent im Investitionstal.

Erik Brynjolfsson, Direktor des Stanford Digital Economy Lab, hat die Produktivitäts-J-Kurve bereits 2021 formalisiert. Jede Allzwecktechnologie erzeugt denselben Verlauf. Dampfmaschine. Elektrifizierung. Personal Computer. Internet. Und jetzt KI. Erst sinkt die gemessene Produktivität. Dann steigt sie steil an.

Der Grund: Vor der Ernte kommt die Investition. Und zwar nicht in die Technologie selbst.

Warum 95 Prozent scheitern: Die Friction-Falle

Wenn Stanford beschreibt, was Erfolgsprojekte richtig machen, dann erklärt eine ergänzende Studie von MIT und J. Snyder (2025), warum die 95 Prozent scheitern. Der Titel ist der Schlüssel: Organizational Friction as a Barrier to GenAI Scalability.

Der Hauptgrund ist erstaunlich banal. Unternehmensführungen vermeiden bewusst Friction. Sie implementieren KI dort, wo sie bestehende Workflows am wenigsten stört. Snyder nennt das Path-of-Least-Resistance-Problem.

Diese Strategie ist aus klassischer Change-Management-Perspektive verständlich. Akzeptanz entsteht. Niemand muss seine Arbeitsweise verändern. Politische Konflikte werden vermieden. Und genau deswegen bleibt der Produktivitätssprung aus.

KI entfaltet ihre Wirkung nicht neben bestehenden Prozessen. Sie entfaltet sie, indem sie Prozesse neu definiert. Wer KI als Add-on zu alten Strukturen einsetzt, erzeugt Akzeptanz. Aber keine Transformation. Und keinen Wettbewerbsvorteil.

Das ist die kausale Brücke zur Stanford-Studie. Die 77 Prozent invisible costs, die Stanford identifiziert, sind genau das, was die 95 Prozent gescheiterten Projekte zu vermeiden versuchen. Change Management. Prozessumbau. Neudefinition von Jobprofilen. Die Friction ist nicht der Preis der Transformation. Sie ist das Produkt.

Echte Transformation findet dort statt, wo Reibungspunkte in der Organisation identifiziert und aktiv gemanagt werden, anstatt sie zu umgehen.

Phase 1: Initial-Investition. Wo 77 Prozent der Kosten unsichtbar bleiben.

Das ist die Zahl, die auf kaum einer Bühne genannt wird.

77 Prozent der schwierigsten Herausforderungen bei KI-Einführungen liegen nicht in der Technologie. Sie liegen in Change Management, Datenqualität und Prozess-Redesign. Nur 23 Prozent entfallen auf das, was die meisten CIOs in ihren Präsentationen zeigen: Modellauswahl, Integration, API-Landschaft.

Die Technologie war in fast jedem von Stanford untersuchten Fall der einfachste Teil. Das sagen nicht Theoretiker. Das sagen Praktiker, die es gebaut haben.

Für jeden Dollar greifbarer Technikinvestition investieren Unternehmen bis zu zehn Dollar in immaterielle Vermögenswerte. Prozessredesign. Schulung. Organisatorische Transformation. In keiner Bilanz tauchen diese Kosten als Asset auf. Aber sie entscheiden alles.

Wer glaubt, KI sei ein IT-Projekt, hat bereits in Phase 1 verloren.

Phase 2: Strategie und Kompetenzaufbau. Wo 61 Prozent der Erfolgsprojekte zuerst scheitern.

Eine der wichtigsten Erkenntnisse der Studie wird in der Öffentlichkeit kaum diskutiert.

61 Prozent der erfolgreichen Projekte hatten mindestens einen gescheiterten Vorgänger. Das ist keine Schwäche. Das ist ein Muster.

Erfolgreiche Unternehmen scheitern iterativ. Sie bauen systematische Ansätze, um aus Rückschlägen zu lernen. In keinem einzigen der 51 von Stanford untersuchten Fälle wurde jemand für eine fehlgeschlagene KI-Initiative bestraft. Das ist keine Toleranz. Das ist Strategie.

73 Prozent der erfolgreichen Projekte starteten bewusst klein. 63 Prozent formulierten ihre Pilots explizit als Experimente. 100 Prozent nutzten einen iterativen Ansatz. Kein einziges erfolgreiches Projekt folgte dem klassischen Wasserfall-Muster.

Wer KI einführen will, braucht eine Unternehmenskultur, die Experimente erlaubt. Wer jeden Fehlversuch zur Karrierefalle macht, bekommt am Ende keine Experimente mehr. Und auch keine Durchbrüche.

Phase 3: Integration und Skalierung. Wo sich 71 Prozent von 30 Prozent trennen.

Hier wird die Spreizung zwischen Vorreitern und Rest zum ersten Mal messbar sichtbar.

Die Stanford-Studie unterscheidet drei Implementierungsmodelle entlang der Dimension menschlicher Aufsicht.

Collaboration. Mensch und KI arbeiten kontinuierlich gemeinsam an jeder Aufgabe. Typisch für Software-Engineering mit Copilot-Modellen. Produktivitätsgewinn: rund 54 Prozent.

Approval. Die KI macht die Arbeit, ein Mensch prüft und genehmigt jeden Output. Typisch für regulierte Bereiche wie klinische Dokumentation. Produktivitätsgewinn: median rund 30 Prozent.

Escalation. Die KI erledigt 80 Prozent oder mehr autonom, Menschen prüfen nur Ausnahmen. Typisch für IT-Operations, Customer Support, Schadenbearbeitung. Produktivitätsgewinn: 71 Prozent im Median.

Wer KI als Werkzeug denkt, erreicht 30 Prozent. Wer KI als autonomen Akteur in klar definierten Grenzen denkt, erreicht 71 Prozent. Derselbe Use Case. Dasselbe Modell. Völlig unterschiedliche Ergebnisse.

Agentische Systeme sind in der Studie noch selten. Nur 20 Prozent der untersuchten Fälle nutzen sie produktiv. Aber ihre Produktivitätsgewinne liegen ebenfalls bei 71 Prozent Median. Gegenüber 40 Prozent bei hoch-automatisierten, aber nicht-agentischen Systemen. Diese 20 Prozent sind die Speerspitze der Speerspitze. Sie zieht täglich weiter davon.

Phase 4: Exponentieller Erfolg. Wo der Vorsprung uneinholbar wird.

Das ist der Kern der Dynamik, die viele Unternehmen unterschätzen.

Eine Fintech hat in der Stanford-Studie Millionen Zeilen alten ETL-Codes in wenigen Wochen mit einem KI-Agenten migriert. Eine vergleichbare Bank rechnet für dieselbe Aufgabe in Jahren. Gleiche Technologie. Gleiche Use Cases. Zwei Geschwindigkeiten, die sich niemals wieder annähern werden.

Der Grund: Wer einmal in der Ernte-Phase angekommen ist, investiert die Gewinne sofort in die nächste Welle. Die zurückgebliebenen Unternehmen müssen noch die Investitionskosten der ersten Welle stemmen. Die Produktivitätsdifferenz wirkt nicht additiv. Sie wirkt exponentiell.

Brynjolfsson beschrieb Anfang 2026 eine kleine Kohorte von Power-Usern, die ganze Arbeitsabläufe mit KI-Agenten automatisieren. Aufgaben in Stunden statt in Wochen. Das sind die Unternehmen, die das nächste Jahrzehnt definieren werden. Alle anderen werden darauf reagieren müssen.

Die US-Produktivität ist 2025 um 2,7 Prozent gestiegen. Das Doppelte des Zehnjahresdurchschnitts. Bei gleichzeitig revidiert nach unten angepasstem Beschäftigungswachstum. Das ist das makroökonomische Signal, dass die Ernte-Phase beginnt. Für die Vorreiter.

Die europäische Frage, die Holger Schmidt seit Jahren stellt

Was bedeutet das für Europa?

Es bedeutet: Wer jetzt zögert, verliert nicht ein Jahr. Er verliert eine Dekade.

Holger Schmidt hat den Zusammenhang im F.A.Z. Digitalwirtschaft-Briefing zugespitzt. Europas KI-Lücke wird zum Produktivitätsproblem. Das europäische Produktivitätswachstum liegt bereits bei null. Der demografische Wandel wird in den nächsten Jahren Millionen Arbeitskräfte aus dem Arbeitsmarkt nehmen, die nicht ersetzt werden. KI ist aus dieser Perspektive keine Option. Sie ist die einzige verfügbare Antwort.

Aber sie kommt mit einer Bedingung. Europa muss entscheiden, ob es die nächste Welle der Wertschöpfung selbst gestaltet oder nur nutzt. Entweder wir akzeptieren, dass Infrastruktur, Modelle und Arbeitsprozesse mehrheitlich aus den USA und China kommen. Dann werden unsere Unternehmen zu Nutzern, nicht zu Gestaltern. Oder wir bauen eigene Wertschöpfungsketten. Mit eigener Infrastruktur. Mit eigenen Modellen. Mit eigener Governance.

Digitale Souveränität ist keine Compliance-Frage. Sie ist die Frage, auf welcher Seite der KI-Schere europäische Unternehmen in fünf Jahren stehen werden.

Was wir bei 506.ai vor jedem Projekt klären

Bei 506.ai ist die J-Kurve nicht nur eine Keynote-Folie. Sie ist die erste Folie in jedem Projektkickoff. Bevor wir über Anwendungsfälle, Modelle oder Architektur sprechen, stellen wir drei Fragen an das Führungsteam.

Verstehen Sie, dass Ihr Unternehmen zunächst langsamer werden wird? Sind Sie bereit, durch das Tal zu gehen, bevor Sie ernten? Haben Sie die Bereitschaft, Friction zu managen, statt sie zu umgehen?

Wer diese drei Fragen nicht überzeugt mit Ja beantwortet, bekommt von uns keinen Projektstart. Das klingt hart. Es ist die wichtigste Entscheidung, die wir für den Projekterfolg treffen können.

Denn Projekte, die ohne diese Klarheit starten, erreichen Phase 4 nicht. Sie erreichen auch Phase 2 nicht. Sie brechen irgendwo im Tal ab, und die Organisation schreibt KI als enttäuschende Technologie ab. Projekte, die mit dieser Klarheit starten, erreichen die Ernte. Nicht immer sofort. Aber verlässlich.

Was jetzt zu tun ist

Drei Prinzipien zeigen sich quer durch alle erfolgreichen Implementierungen.

Erstens: Führung von oben. In den sieben Fällen, die unternehmensweite Transformation ausgelöst haben, war KI-Adoption ein Corporate OKR. Mit Boni verknüpft. Nicht delegiert. Nicht optional.

Zweitens: Friction managen, nicht umgehen. Wer KI dort einsetzt, wo sie bestehende Workflows am wenigsten stört, bekommt Akzeptanz ohne Wirkung. Die höchste Produktivitätsrendite entsteht dort, wo KI Prozesse neu definiert, nicht wo sie alte Prozesse beschleunigt. Reibung ist nicht der Preis der Transformation. Sie ist der Indikator, dass Transformation überhaupt stattfindet.

Drittens: Agentisch denken, nicht als Werkzeug. Die höchsten Produktivitätsgewinne entstehen dort, wo KI nicht als Copilot verstanden wird, sondern als autonomer Akteur mit klarer Verantwortung und klaren Grenzen. Als virtueller Kollege, der onboarded wird, eine Führungskraft hat und Ergebnisse liefert.

Diese Denkweise ist der Grund, warum wir bei 506.ai unsere KI-Kollegen nicht als Software verkaufen. Sie werden eingestellt. Sie werden eingearbeitet. Sie haben einen menschlichen Vorgesetzten. Und sie arbeiten 24 Stunden am Tag auf europäischer Infrastruktur, DSGVO-konform und in klar definierten agentischen Strukturen.

Die Kurve schließt sich nicht von selbst

Die J-Kurve ist kein Naturgesetz. Sie ist eine Beschreibung dessen, was passiert, wenn eine Allzwecktechnologie wie KI auf eine bestimmte Art organisatorisch verarbeitet wird.

Ich zeige diese Kurve seit einem Jahr. Auf Bühnen. Und wichtiger: In jedem einzelnen Kundenprojekt, bevor wir die erste technische Entscheidung treffen. Weil mir klar war: Die Mehrheit der Entscheider sieht nur das Tal. Die Spitze sieht niemand, weil sie noch nicht erreicht ist. Und genau deswegen steigen so viele aus, bevor sie dort ankommen.

Stanford hat jetzt empirisch gezeigt, dass die Kurve nicht nur ein Denkmodell ist. Sie ist bereits gemessene Realität. MIT und Snyder haben gezeigt, warum die 95 Prozent im Tal hängen bleiben. Holger Schmidt hat gezeigt, dass das deutsche Leitmedium angekommen ist. Die Vorreiter sind in Phase 4. Die Mehrheit steckt in Phase 1. Dazwischen wachsen die Distanzen täglich.

Unternehmen, die jetzt die organisatorische Hausaufgabe machen, landen in der Ernte-Phase. Unternehmen, die jetzt warten, bleiben im Investitionstal stecken und stehen in 24 Monaten vor einer Konkurrenz, die doppelt so produktiv arbeitet. Bei gleicher Belegschaft. Bei gleichen Kosten. Bei gleicher Technologie.

Die Schere öffnet sich nicht wegen der Technologie. Sie öffnet sich wegen der Bereitschaft, Friction auszuhalten und Arbeit neu zu denken.

Wer diese Bereitschaft jetzt nicht aufbringt, wird die Lücke nicht mehr schließen.

Quellen: Pereira, E., Graylin, A. W., Brynjolfsson, E. (2026): The Enterprise AI Playbook. Lessons from 51 Successful Deployments. Stanford Digital Economy Lab, April 2026. / Snyder, J. (2025): Organizational Friction as a Barrier to GenAI Scalability. MIT Study. / Schmidt, H. (2026): Die Schere zwischen KI-Vorreitern und dem Rest der Wirtschaft öffnet sich rapide. F.A.Z. Digitalwirtschaft-Briefing.

This morning, Andrej Karpathy published the most precise AI jobs analysis anyone has produced.

By tonight, it was gone.

Here is what happened. And why the deletion tells us more than the data ever could.

The Analysis

Karpathy scored every US occupation on AI exposure. 342 jobs. Each rated 0 to 10 based on how easily large language models could take over the work. The data came from the Bureau of Labor Statistics. The scoring was done by AI, which adds a certain irony to the exercise.

The results were not surprising if you have been watching this space. They were, however, stark when laid out all at once.

42% of all US jobs scored 7 or higher. That is 59.9 million people. $3.7 trillion in annual wages sitting in direct exposure range.

Software developers landed at 8 to 9. Financial analysts, accountants, management consultants, legal assistants: similar. Medical transcriptionists scored a perfect 10. Pure digital input, pure digital output, zero physical component. AI does not need to be better than a human transcriptionist. It just needs to be cheaper and faster. It already is.

The safe jobs? Roofers. Plumbers. Electricians. Nurses. Construction workers. Jobs that require a body in a specific place doing something that cannot be done from behind a screen. These scored 0 to 3.

The weighted average across all 143 million US jobs: 4.9 out of 10.

The entire economy. Halfway exposed.

Then Elon Musk Replied

The tweet spread quickly. It was exactly the kind of data that travels fast: specific, visual, and threatening to enough people that sharing it feels like a warning or a provocation depending on who you are.

Elon Musk saw it and responded publicly: “All jobs will be optional.”

At that point, a technical research project became a political statement. A data visualization became a manifesto. Karpathy’s careful scoring rubric got attached to a worldview he almost certainly did not intend.

Within hours, the GitHub repository was gone. The original tweet deleted.

What the Deletion Actually Means

I want to be careful here. I do not know Karpathy’s reasons. He has not explained them publicly.

But the sequence is telling.

One of the most credible voices in AI research built something honest and methodical. It got picked up by the loudest amplifier in the conversation. And he pulled it back.

That is not a story about data. That is a story about readiness.

We are not ready for this conversation. Not politically. Not socially. Not inside most boardrooms or government offices.

The question of what AI will do to jobs has been theorized, debated, and worried over for years. But when someone actually scores the jobs, one by one, and publishes it as an interactive treemap you can explore with your own name in mind, something shifts. It stops being abstract.

And that is uncomfortable.

The European Context

Here is what I keep coming back to.

In Austria and across Europe, we face a structural labor shortage that is not going away. Demographics are working against us. There are not enough people to fill the roles that need filling, and that gap will widen over the next decade regardless of what AI does.

So the framing of “AI will replace jobs” is, at least in our context, incomplete.

The more useful question is: what happens when AI can do the work that nobody is available to do?

That is not a threat. That is an answer.

Digital team members that onboard like employees, work within defined boundaries, operate under GDPR-compliant infrastructure, and scale without the friction of hiring. Not tools you buy and configure. Colleagues you bring in.

The labor shortage and the AI capability curve are on a collision course. The organizations that recognize this now and build accordingly will not be the ones who were least afraid. They will be the ones who asked the right question earliest.

The Territory Is Still There

Karpathy drew the map. Then rolled it up.

The territory does not care.

The 59.9 million jobs in the exposure range are still there. The accountants and analysts and consultants whose output is text and spreadsheets are still there. The European companies struggling to find qualified candidates for knowledge work roles are still there.

The conversation Karpathy started and then deleted will happen anyway. In boardrooms. In policy discussions. In hiring decisions made or not made.

The only question is whether you are in the room when it does.

---

Gerhard Kurner is CEO of 506.ai and Co-Founder of Kollega, a platform for AI-powered digital team members built for European enterprises and public sector organizations.

When the internet was invented, it was clear who it was for: humans. Every website, every app, every online store was designed for human comprehension. Colors, fonts, menus, and the ever-present “Accept Cookies” button. For thirty years, the internet was a world in which only one species navigated: us.

That is changing now. Quietly, but fundamentally. Because the internet has new inhabitants. And they see the world through entirely different eyes.

The End of Software That We Operate Ourselves

Let me start with an observation that explains a great deal. For decades, companies have been buying software so that humans can do the work. ERP systems, CRM platforms, project management tools. All of these are instruments that we have to operate. The software does nothing on its own. It waits for someone to click, type, enter. Without a human, nothing happens.

That is now being turned on its head. Completely.

What is emerging is not better software that we operate more efficiently. It is AI agents that do the work themselves. We no longer use a tool to accomplish a task. The AI takes on the task. Independently. From start to finish. Nobody buys software for the sake of having software. Companies want someone who does the work. And that is exactly what is happening right now: for the first time, the software does the work.

Tools Are Becoming Colleagues. And It Starts with the Juniors.

The difference from what most people know is fundamental. ChatGPT, Copilot, a clever text field you ask questions. That is already the past. It was the first step. But it was still software that we operated. We asked the questions. We processed the answers. We did the work. Just a little faster.

The next generation is different. AI agents no longer answer. They act. They execute processes, take over tasks, communicate externally. Like a colleague, not like a tool.

And as with every great transformation, it starts at the beginning: with the junior hires. Think of a smart, tireless employee fresh out of university. They research quickly and thoroughly, compile information from hundreds of sources, prepare decision briefs, answer emails, coordinate appointments, create presentations from templates, maintain data. They even make phone calls. All of it around the clock, without breaks, without sick days.

The deep product knowledge of an engineer with twenty years of experience? The intuition of a sales director who has known their clients for a decade? The gut instinct of a project manager who senses when a timeline is about to slip? That is coming next, the pace of development is breathtaking. But it starts where the need is greatest and the entry point most natural: gathering, structuring, preparing, and passing on information. The assistance work, the groundwork, the grunt work that devours endless hours in every company and for which there are barely any people left on the job market.

The comparison that captures it best: a chatbot is a calculator. An AI agent that works like a colleague is a talented junior who clears the path for your best people. And like every good junior, they will learn fast.

At 506.ai, we call these evolved AI agents “Kollega.” Because they are not treated as software but as what they actually are: digital team members. With a manager who defines tasks. With an onboarding process that works like it does for any new hire. With measurable results. You don’t buy a Kollega. You hire one.

But These New Colleagues Need a Different World

Now it gets interesting. What does a digital junior actually do all day? They research. They compare. They gather information from a wide range of sources, summarize it, and pass it on: to sales, to procurement, to customer service. They are constantly moving through the internet, through internal systems, through databases.

And this is where we hit a problem that most leaders don’t yet have on their radar: these digital sales assistants, project coordinators, and clerks need an entirely different infrastructure than we humans do. Our systems and the internet were built for human eyes and human hands. For an AI agent, a beautifully designed website is about as useful as a painting to someone who needs the phone number written inside it.

Imagine sending a new employee out to compare supplier prices. A human opens ten browser tabs, scrolls through product pages, skims PDFs, calls the sales rep. An AI agent can do all that in principle. But it has to fight through cookie banners, interpret JavaScript animations, and decipher dropdown menus that were never designed for it. It’s like sending your best employee into a city where every sign is written in a language they can’t read.

A Second Layer Is Emerging. Quietly.

What is happening right now is one of the most fascinating developments in digital history. And most people are completely overlooking it. Something like a second layer of the internet is forming. Not planned by any single company, but as a natural consequence of what happens when billions of AI agents start using the web.

The Future Today Strategy Group, one of the world’s most respected foresight consultancies, calls it the “Model Web.” In their article “The Next Internet Isn’t For Us,” they describe a world in which AI agents no longer consume the internet as outsiders, scraping websites built for humans, but as equal participants for whom dedicated access points and formats are being created.

In the 1990s, the internet was built for reading. In the 2000s, it was rebuilt for participation: comments, likes, social media. Now an internet for working is emerging. For digital workers who don’t click, but act.

And this doesn’t only affect the public internet. The internal software landscape of companies is facing the same upheaval. When AI agents are meant to maintain customer data as digital sales assistants, process cases as clerks, or monitor deadlines as project coordinators, they need access to CRM, ERP, and project management systems that are built for them. Not for human hands clicking through interfaces.

The world’s largest technology companies (Google, Microsoft, Amazon, OpenAI, Anthropic) are jointly creating the standards for this. The fact that these competitors are aligning on shared infrastructure almost never happens in this industry. It’s as if every car manufacturer in the world had simultaneously agreed on a single charging plug. A sign of how fundamental this shift truly is.

When the World Opens Up for the Newcomers

Now put these two developments together. On one hand: AI that no longer waits to be operated, but does the work itself. On the other: an internet and a software ecosystem that are growing in ways that allow these digital colleagues to navigate them the way humans navigate a well-signposted city.

What happens when both converge? Your digital assistant becomes a field agent. They compare suppliers on their websites, because those websites offer machine-readable access points. They book appointments with business partners, because their systems can speak to each other. They research market trends and put a summary on your sales team’s desk every morning that used to cost half a day of human effort.

And here is the crucial part: they do all of this without you having to learn a new tool, operate a new interface, or introduce a new process. You define the task. They deliver the result. That is the paradigm shift: you don’t work with software. Software works for you.

The Invisible Company

This is where it becomes truly relevant for leaders. Because the Model Web won’t just change how AI agents work. It will change who gets found.

Remember the companies that had no website in the early 2000s? The ones that said: “Our customers know us, they don’t need the internet.” Those sentences sound absurd today. Within a few years, it will sound equally absurd when a company says: “We don’t need an AI-friendly presence.”

Because when a potential customer’s AI agent searches for the best supplier for a specific component, it won’t find the company with the most beautiful website. It will find the company that delivers relevant information in a format the agent understands. Those who don’t offer that will simply be invisible to the digital assistants of the world. And therefore invisible to a growing share of the business world.

Picture it concretely: a procurement director tasks their AI agent with finding and comparing five possible suppliers for a specialized part. Three suppliers have structured their product data and terms so the agent can evaluate them directly. Two only have a classic glossy website. Who makes the shortlist? The answer will be as obvious in three years as the question of whether a company needs a Google listing is today.

The Real Transformation Is a Human One

The biggest barrier in all of this is not technology. The technology is here. The biggest barrier is our thinking. We need to let go of two beliefs that have been deeply embedded in our understanding of work for decades: first, that software is a tool we operate. And second, that only humans can do work.

Neither is true anymore.

The skilled worker shortage, particularly acute in European SMEs, is no longer a forecast. It is daily reality. Positions remain unfilled, teams operate at their limits. Your best people, the engineer with deep product knowledge, the sales director with the client relationships, the project manager with a sense for timelines, spend far too much of their time on preparatory work that a good junior could have taken over long ago. Except those juniors barely exist on the job market anymore.

This is exactly where digital colleagues come in. Not as a replacement for the experience and expertise of your specialists. But as the reinforcement that the labor market can no longer deliver. So that your best people can finally do what they are actually paid to do.

Those who close this gap now will gain an advantage that is hard to catch up with. Not because the technology is so complicated. But because the shift in perspective takes time. Away from “I buy software and do the work myself.” Toward “I hire someone who does the work.” And time is the one thing that is running out in this transformation.

Depending on industry and market, mixed teams of humans and digital colleagues will be standard within the next three years. The internet and the software landscape will be built for both. The question is not whether this happens. The question is whether you will have a well-practiced team by then. Or whether you will just be starting to write the job posting.

-- Gerhard Kürner, CEO 506.ai

Learn more about AI agents that work like colleagues: mykollega.ai

The ChatGPT moment in 2022 was: AI can talk.
The agent moment in 2026 is: AI can work.

I’ve been watching a shift over the past few weeks that feels bigger than anything since November 2022.

The chat interface was a stepping stone. It forced us to become prompt engineers – breaking down every complex task into individual lines of text. Analyze a spreadsheet? One prompt. Write a project plan? Another prompt. Send an email? Yet another prompt. And in between: manual copy-paste, lost context, frustration.

It was like having a brilliant assistant who can only hear one sentence at a time – and forgets everything after they reply.

Now the paradigm is flipping.

The new generation of AI tools doesn’t need a chat window anymore. You connect them to your cloud services .- Drive, CRM, project management - and say: “Create an expense report from the receipts in the Q1 folder.” Or: “Summarize last week’s meeting notes into a report.”
And then they work. Autonomously. With context. Across multiple steps. Directly in your existing tools.

The numbers: → 85% of developers already use AI coding tools → 41% of all code is now AI-generated → Autonomous agents can now work 30+ hours without performance degradation

Developers have already experienced this shift. For everyone else, it’s starting now.
The mental shift:
Old: “I’m using a tool”
New: “I’m managing a team”

Your role becomes strategic oversight - defining goals, setting guardrails, providing high-level feedback.The chat window isn’t disappearing. But it’s becoming the exception rather than the rule.

We’re at the beginning of the agent era. And it’s coming faster than most people think.

What do you think - are you ready to shift from “prompting” to “delegating”?

OpenAI has officially entered the medical arena with ChatGPT Health. It promises a personalized AI coach that connects directly to your medical records and wellness apps.

But there’s a massive asterisk: The EU, Switzerland, and the UK are strictly excluded from the launch.

While some see this as Europe "falling behind," a closer look at recent events suggests this regulatory barrier might be the most meaningful protection we have.

The Regulatory Blocker: GDPR & EU AI Act
The reason for the exclusion is a complex mix of the GDPR and the EU AI Act.

The Reality: Yes, both frameworks are often criticized for being "innovation killers" that desperately need reform to become clearer and more applicable.

The Silver Lining: In this specific case, they act as a vital safety net. They ensure that highly sensitive biological and medical data isn't just another commodity to be processed in a legal vacuum. We can, and should, strive for a reformed version of these laws that enables innovation without sacrificing this core protection.

A Reality Check: The US Court Ruling
The skepticism toward US-based consumer platforms isn't just theoretical. This week, a US federal court confirmed a ruling forcing OpenAI to disclose 20 million ChatGPT records (anonymized) in an ongoing copyright lawsuit.

This sets a chilling precedent for the future of "ChatGPT Health".

Legal Precedence:
If a court can demand 20 million logs today for a copyright case, what stops a subpoena for sensitive health data tomorrow?

The Myth of Anonymization:
In the age of Big Data, "anonymized" logs are increasingly easy to re-identify.

Jurisdiction Risks:
No matter what a company’s privacy policy says, they are ultimately bound by the laws of the land where they reside.

The Trust Gap
AI is undeniably the future of healthcare, it will save lives through early detection and personalized care. But the question remains: Is a US consumer tech giant the right custodian for our most intimate data?

The EU’s current "barrier" might be clunky, but it forces a necessary conversation about Data Sovereignty. Perhaps the goal shouldn't be to lower the bar to match the US, but to build a reformed European framework that allows for "Safe Innovation" under our own rules.

#ChatGPTHealth: https://lnkd.in/dza-bda9
#DataPrivacy: https://lnkd.in/dZ5djVtg

Looking back at 2025, I aim to cut through the noise and summarise the most significant expert insights that defined this year’s ‘signal’, offering a glimpse into the ‘parallel reality’ that awaits us in 2026.

2025: Three Signals That Changed Everything

1. The shift to ‘living intelligence’ (Amy Webb)

While the hype focused on better chatbots, Amy Webb identified the convergence of AI, biotechnology, and advanced sensing as the real signal.

2025 was the year AI became physical. We entered the ‘Technology Super Cycle’, where biology became programmable. Tools like AlphaFold 3 meant that we stopped ‘discovering’ medicines and materials and started designing them. AI is no longer confined to our screens; it has become the operating system for the physical world.

2. The ‘Lego-isation’ of software (Ivan Zhao)

The noise said: ‘AI will replace apps.’ Ivan Zhao (Notion) responded: ‘AI will make software modular.’

By 2025, we had moved away from rigid, one-size-fits-all SaaS. We entered an era of ‘handcrafted’ digital environments, in which users can build their own tools in real time. AI agents have evolved from simple assistants into autonomous architects that structure data and workflows behind the scenes. Software has finally become as flexible as Lego.

3. The Invisible Acceleration (Jack Clark)

Jack Clark of Anthropic highlighted a signal that many had overlooked: AI-to-AI communication.

While humans are still perfecting their prompts, a significant proportion of digital intelligence has transitioned to ‘Droid Speak’ — machine communication that occurs 100 times faster than human language. This has created a hidden layer of hyper-acceleration in research and optimisation that is largely invisible to the naked eye, yet its effects are felt in every industry.

Outlook for 2026: Living in a Parallel Reality

The groundwork laid in 2025 will lead us towards a transformative 2026. Jack Clark made a profound prediction: by summer 2026, we will witness a ‘cognitive gap’:

Those who natively master frontier AI systems will essentially be living in a ‘parallel reality’. Their productivity, problem-solving speed and access to insights will far exceed traditional structures, making communication between the two worlds increasingly difficult. We are transitioning from viewing AI as a tool to viewing it as an invisible, global infrastructure.

The implications for 2026 are so vast, ranging from this ‘parallel reality’ to the new AI-driven economy, that I will be sharing my detailed analysis of 2026 in a separate post very soon.

#SignalVsNois

e

A deep dive into the new “2025 National Security Strategy” is a wake-up call for anyone who believes in a strong, united Europe. The message is brutal: The era of partnership is over. We are expected to pay up, buy American, and step back politically into a fractured landscape of individual states.

Here are my key personal takeaways from the document:

📉 Europe as a Cash Cow & Consumer Base The strategy explicitly prioritizes “opening European markets to U.S. goods and services”. It’s not about mutual cooperation anymore; it’s about unilateral access. They dismiss our regulatory frameworks as “regulatory suffocation” that must be abandoned. They explicitly reject “Net Zero” ideologies, claiming they have “greatly harmed Europe”.

🇪🇺 The Goal: Dismantling the Political Union The U.S. no longer views the EU as a key ally, but as a problem. The strategy labels the “activities of the European Union” as something that “undermine[s] political liberty and sovereignty”. The explicit goal is to have Europe operate not as a Union, but merely as a “group of aligned sovereign nations”. The strategy opposes the “sovereignty-sapping incursions” of transnational organizations.

🗳️ Open Political Interference In an unprecedented move, the document announces that the U.S. aims at “cultivating resistance to Europe’s current trajectory within European nations”. They are openly picking winners in our domestic politics, stating they will encourage their “political allies in Europe” to promote a revival of national spirit.

💸 The Price Tag: 5% GDP While they aim to weaken our political unity, they demand we shoulder a massive financial burden. The “Hague Commitment” demands NATO allies spend 5% of their GDP on defense. We are expected to foot the bill while being stripped of our strategic autonomy.

The Bottom Line: Stop Complaining, Start Acting.

“America First” translates to “Europe Last.” We are positioned as paying customers and politically fragmented vassal states.

But getting angry and wondering how it came to this is no longer enough. We cannot just accept our role as passive cash cows.

We must act, and we can start immediately.

Every single one of us, citizens, businesses, and governments, has power right now. The answer is simple but crucial: Choose European.

We need to aggressively support and prioritize procuring European sovereign technology. Stop feeding the very machine that seeks to dismantle us economically and politically.

Buying European sovereign tech isn’t just an economic preference anymore; it’s an act of geopolitical self-defense that strengthens our resilience immediately.

👉 Join the initiative and take action here: https://www.choose-european.eu/

👇 Read the full strategy as evidence (Page 29): https://www.whitehouse.gov/wp-content/uploads/2025/12/2025-National-Security-Strategy.pdf#page29