The AI Workforce Leaves the Desk
Robots on the jobsite, models tuned on your own knowledge, and a permission layer for machine colleagues
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 are the story, but because where they put money is one of the clearest early signals of what work will look like next. This week, almost none of it pointed at the office.
For three years the debate about artificial intelligence and work has been, at its core, a debate about desk work. This week’s funding rounds suggest the buildout has quietly moved to three other places at once: the construction site, the tuning layer between a company’s knowledge and its models, and the permission system of the corporate laptop. Each of these says more about the next phase of working life than any benchmark.
The machine colleague picks up a shovel
The strongest pull for AI right now may not be the surplus of office workers but the shortage of physical ones. Construction, energy infrastructure and factories cannot find the people they need, and the demographic arithmetic behind that shortage gets worse every year. That is the gap into which physical AI is now being financed.
You can see this priced into the market already. Gritt came out of stealth on July 20 with 32.4 million dollars, including a 26 million Series A led by Obvious Ventures, with Union Square Ventures joining and First Round Capital already in from the seed stage. Gritt does not build humanoid robots. It builds AI-driven robotic arms that bolt onto the construction equipment a site already owns, skid steers and forklifts, and puts them to work on utility-scale solar plants first. Union Square Ventures framed the investment as a bet on infrastructure itself, on the fact that the world needs to build faster than its workforce is growing.
There is a supply chain forming underneath this. Sila, the battery materials company, raised 300 million dollars on July 21, led by Atreides Management and Sutter Hill Ventures with 8VC among the participants, and explicitly reframed its factory expansion away from electric vehicles toward the hardware base of AI, robotics and drones.
The detail worth keeping is Gritt’s retrofit logic. The robots attach to machines that already exist, they do not replace the fleet. That is precisely the pattern the organizational side of this story keeps showing: the winners adapt what is already there instead of waiting for a clean-sheet replacement.
Your company’s knowledge becomes the business model
The model market itself is reorganizing around a question of ownership: whose knowledge does the machine run on, and where does that knowledge live afterwards?
Thinking Machines Lab, the company of former OpenAI technology chief Mira Murati, published its mission manifesto on July 10 and released its first model, Inkling, with open weights on July 15. The revealing part is not the model but the business model. According to TechCrunch, the company does not plan to earn its revenue from Inkling itself but from Tinker, its customization platform, with which organizations train the model’s weights on their own knowledge. Bridgewater Associates is confirmed as an early customer. A manifesto is a manufacturer’s document, not evidence, and should be read as positioning. But the positioning is remarkable: a frontier lab is betting its entire economics on the idea that knowledge work does not migrate to the model provider but stays inside the company.
The market is paying for that idea at scale. Fireworks closed a 1.5 billion dollar Series D on July 16 at a 17.5 billion valuation, co-led by Atreides Management, Index Ventures and TCV, for an enterprise platform that tunes and serves specialized and custom models, reportedly at around a billion dollars in annualized revenue. And one layer further down, SkyPilot raised a 20 million seed round led by Lux Capital on July 21 to let companies run AI workloads across any mix of clouds and hardware, which turns compute purchasing into a management discipline of its own, a thread this series has followed for several weeks.
This is where the week connects to a larger argument, and I will come back to it below.
A permission layer for a mixed workforce
Once employees start bringing their own AI agents to work, the operative question shifts from what the machine can do to what it is allowed to do, and on whose device.
Glow came out of stealth on July 22 with a 180 million dollar Series A at a 1.2 billion valuation, led by Sequoia Capital and Cyberstarts, with Greenoaks and Lux Capital among the investors. Glow’s premise is the future-of-work argument in its purest form: employees now install tools and run AI agents on their machines faster than any security team can react, so the endpoint needs its own intelligence, a layer that continuously maps the environment and decides which software and which machine actors may run at all. That is the same institutional wiring this series described in recent weeks, when identity systems began issuing digital colleagues their own credentials. First the badge, now the door policy.
The adjacent signal came one day before the window: Sable raised 45 million dollars, co-led by Sequoia and 8VC, for an AI employee named Aidan that runs live product demos and customer conversations end to end, already in production at Notion and Decagon. The more such colleagues clock in, the more valuable the layer that decides what they may touch.
Signals at the margin
Energy: BloombergNEF projected on July 21 that US data centers will grow from under six percent of national power use today to roughly a fifth by 2035, while PJM, the largest US grid operator, expects a six gigawatt reliability shortfall as early as 2027. Europe is building against the same constraint: Pure DC secured an additional 1.3 billion euros on July 21 for a 110 megawatt AI campus in Seinäjoki, Finland, expandable to 550 megawatts.
Defense: Cathedral, founded by four alumni of the Department of Government Efficiency, raised 160 million dollars at a 1.4 billion valuation for AI-driven military cyber operations, led by Andreessen Horowitz and Sequoia. Defense startups have already raised a record 17.4 billion dollars in 2026, against 11.2 billion in all of 2025.
Macro: Moonshot AI’s open model Kimi K3 rattled the markets in the middle of the month, with the Philadelphia semiconductor index losing around ten percent in a week. Open models near the frontier are now a market force of their own, which also means the open-weights niche Inkling just entered is getting crowded fast.
The week at a glance
Gritt, 32.4M total with a 26M Series A. Obvious Ventures lead, Union Square Ventures and First Round on board. Physical AI retrofitted onto existing construction equipment.
Fireworks, 1.5B Series D. Co-led by Atreides, Index and TCV, with Menlo Ventures participating. Specialized models tuned on company knowledge, at scale.
SkyPilot, 20M Seed. Lux Capital lead, Coatue participating. Compute purchasing becomes a management discipline.
Glow, 180M Series A. Sequoia and Cyberstarts leads, Greenoaks and Lux among the investors. A permission layer deciding which AI actors may run.
Sila, 300M. Atreides and Sutter Hill leads, 8VC participating. The materials supply chain pivots from EV to AI hardware and robotics.
Sable, 45M, announced July 16 just before the window. Co-led by Sequoia and 8VC. An AI employee runs customer demos end to end.
Cheiron, 8M Seed, company announcement. Menlo Ventures lead. An operating system that treats a drug program as one connected system.
The thread through all of this is capability
Robots that retrofit the fleet a company already owns, models that are tuned on the knowledge a company already holds, a permission layer the organization itself has to configure. Every one of this week’s signals ends at the same point: somebody inside the company has to do the building, and nobody can buy the result finished.
That is the diagnosis at the heart of Work After AI, my annual report on artificial intelligence and work, which appears this fall. Its central thesis is that the future of work is decided not by what the models can do but by the capability of the individual organization to make them productive on its own knowledge and its own processes. This week the supply side of the model market, from Thinking Machines’ Tinker to Fireworks’ specialized models, began building its revenue on exactly that capability. When the vendors start pricing a thesis, the thesis has stopped being a prediction.
None of this is a scoreboard of who deployed the most capital. The rounds matter as evidence for a thesis, and the sharpest formulation of that thesis came this week from the investors themselves: Union Square Ventures republished its old maxim as “Obliterate, Don’t Automate,” arguing that the biggest returns come not from automating existing work but from making the old process unnecessary. That is what robots on jobsites, models tuned on house knowledge and permission layers for machine colleagues have in common. They are not features added to work as we knew it. They are the early pieces of work rebuilt. Whoever waits for the finished picture will find that it was already there, and that they did not see 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.