The underlying assumption here is that progress in robotics is predicated on step-function model breakthroughs. But models and task reliability are already improving consistently and scaling behavior looks real - this isn’t the bottleneck!

The real bottleneck is economics - we need cheap, globally scalable robots that can be put in a warehouse, hospital, or field and drive top-line improvements or efficiency gains. Robotics doesn’t need a GPT-3 moment. It needs a Model T moment.

Cost is the entry point

Cheap robots help make robotic deployments economically feasible and accessible. Robotic deployments create data, data improves autonomy, and autonomy lowers cost and increases value. We think this loop is the most important one in robotics.

This flies in the face of traditional hardware economics, where companies treat cost as something to address only at scale. History teaches us to first build the most capable version, sell it at the top of the market, and use that to subsidize the ramp, driving BOM cost down later with economies of scale.

Unfortunately, that doesn’t work in robotics, because you need deployments to generate the data that improves autonomy, and you need better autonomy to increase value-per-robot and robot-per-teleoperator ratio.

In other words, a robot that starts expensive never gets enough deployments to become cheap. It gets stuck at the top of the loop.

Robo

Today, our portfolio company Robo comes out of stealth. Robo builds affordable, general-purpose robots and teleoperation infrastructure.

Their first robot is Robo-T. Designed and built here in Los Angeles, it holds 10 pounds and runs tasks across warehouses, hospitals, food manufacturing, and logistics environments.

Over the past three months I’ve seen Robo go from robotic-effectors to robot arms to a full mobile embodiment, and relentlessly cut deployment costs by owning the full stack to just $10/hour. They also built Roboport, their own teleoperation platform, rather than renting one. It turns demonstrations, rollouts, and interventions into improving autonomy, helping close the cost-autonomy loop.

Robo’s founders, Don and Kyle, are high-integrity, high-velocity builders with a rare blend of technical depth, product intuition, and customer obsession. They pivoted into robotics midway through our diligence, and seeing their speed and pragmatism helped get us over the line at pre-seed.

Robo is now looking for fleet partners. Similar to operators of Tesla fleets for Uber or Lyft - Robo are looking for people who buy 15+ Robo-Ts, deployed around the clock, and sell the hours. Those fleets ended up providing much of the liquidity on Uber’s supply side, and turned out to be very good businesses for the people running them. Robo handles delivery, setup, and maintenance; what you need is organization and the communication skills to teach operators a task.

If you’re building here, or want to run a fleet, reach out to them directly. And if you’re working on anything at the intersection of cheap embodiment and real deployment, come talk to us at Canonical.

Over the next few months I spent a lot of time with Somi and Sandeep as the vision for Synthefy came together. Later that year, Canonical had written the first check as the only investor in their pre-seed, and we started meeting every week.

The idea they landed on was simple, driven by a core thesis:

Most of the data that runs the economy lives in tables and time series: transactions, prices, sensor readings, inventory, customer histories. Predictions made from that data decide which payments get approved, how products get priced and stocked, which customers leave, and when machines fail.

And the way companies make those predictions has barely changed in a decade. Every workflow is a custom project: clean the data, engineer features, train a model, tune it, deploy it, maintain it forever. The Synthefy team lived this at Uber’s self-driving unit, where dozens of people spent 5-6 months building a single model. A production system can cost $500K-$1.5M to build and up to $750K a year to keep running.

The team believed pretraining would do for numbers what it did for language: one model, trained on huge amounts of structured data, that makes accurate predictions on a table it has never seen. Six months of ML work becomes an API call.

The path from there looked like most good pre-seed paths, which is to say ‘quiet’. First MVP in early 2024, pointed at defense and telecom workloads. Deutsche Telekom named them best AI startup.

Through 2025 Synthefy stayed heads down: deployments at enterprises like GoodRx, Schneider Electric, and a US Army contract, while the rest of the market has been chasing text, images & videos through LLMs.

Then the market turned, fast. In May, SAP agreed to acquire Prior Labs, an 18-month-old German lab that had raised €9M, and committed over €1B to build on it. In June, Nvidia bought Kumo for a reported $400M. Two of the largest companies on earth paid up for the category within 30 days of each other.

Weeks later, Synthefy shipped Nori. It ranks #1 for accuracy on a public benchmark of 96 datasets, ahead of Google’s TabFM and the TabPFN model SAP had just bought and remarkably, it does it with just 6M parameters, a tenth the size of comparable models. It crossed 440K downloads in its first 30 days, and recently surpassed 630K downloads.

In pilots, it beat forecasting systems customers had spent years tuning, with no custom training.

Today Synthefy announces its seed round, led by Wing with Haystack, Canonical, Samsung Next, plus angels including Srinivas Narayanan (ex-CTO at OpenAI), Aparna Chennapragada (CPO at Microsoft), and Manohar Paluri (VP of AI at Meta).

Congrats, Somi and team. The economy’s numbers finally have their foundation model!

PS: One note on sourcing, because people ask how a team like ours finds Somi and Sandeep. The best pre-seed deals rarely look like “deals.” They look like research: no deck, no go-to-market plan, sometimes no graduation date. Our job is to be in the lab while the conversation is still “could this be a company,” and to be useful.

1. companies building deep infrastructure at the bottom of the stack that is hard to replicate, e.g. NVIDIA, AWS, Cloudflare, Snowflake

2. companies building applications with incredible distribution at the top of the stack, e.g. Facebook, Instagram, OpenAI, Figma

Everything in the middle gets compressed.

I think that model breaks down in robotics - it’s just way too big and too broad for single winners! Instead, we’ll see winning robotics companies in mining, agriculture, logistics, deep sea, defense, manufacturing, healthcare, and dozens of other verticals. These companies will win because they operate in different environments with different safety requirements, regulations, buyers, economics, and failure modes. A winning underwater robot is likely to be irrelevant in a hospital setting. A robot designed to pick and pack fragile inventory stock will be irrelevant when maintaining a solar farm.

With this in mind, I think robotics will produce many large companies, rather than one dominant infrastructure or application layer.

I expect those companies to broadly fall into two categories:

1. vertically integrated companies that own a specific workflow end-to-end, and

2. companies that benefit from the modularization of the robotics stack.

1/ Vertically Integrated Companies

Think of these companies as taking Apple’s path in robotics: increasingly owning the hardware, software, models, deployment, and customer relationship around one valuable physical job. What’s most important for these companies is owning the operating loop.

Take, for example, a robotic mining company - they may start with third-party vehicles, NVIDIA compute, open models, and off-the-shelf sensors. But, so long as they own the operating loop, every hour teaches that company something a generic robotics company cannot know. They learn about how dust affects perception, how different routes change tire wear, which slopes create control problems, and which component failures produce downtime.

That knowledge improves the system. It improves the policies, maintenance schedules, sensor configurations, fleet-management software, and customer workflow. Over time, it also tells the company which hardware should be customized, built, or acquired. The company earns the right to build custom hardware because it owns the job.

I think that same pattern will appear everywhere physical work is valuable and technical. For example: an agricultural robotics company will learn which weeds emerge at each crop stage, how weather and light affect vision, which treatments work, and where its equipment loses performance. A subsea maintenance company learns about marine conditions, asset degradation, battery performance, and the specific equipment customers need inspected or repaired. A logistics company learns how a facility actually operates: where inventory sits, when workers intervene, what causes exceptions, and which tasks create the greatest bottlenecks.

This deployment data makes the system more useful and more economical over time.

This gives those companies a deep competitive advantage, enabling them to sell outcomes, i.e. tonnes moved or packages packed, and not just machines or hours. They may start with third-party components, but the operating loop gives them a growing advantage over generic robot OEMs.

2/ Modular Platform Companies

The winners in this category will be the companies that make it dramatically easier for other people to build, deploy, and operate robots.

Rather than owning a single workflow, such as mining or agriculture, they will provide the shared infrastructure that many robotics companies rely on: fleet management, remote operations, simulation, data infrastructure, safety tooling, hardware interfaces, and marketplaces for task-specific models and components.

Their opportunity comes from the robotics stack becoming more modular. Hardware will get cheaper. Open models will become good enough for more tasks. Components will become more interoperable. And new platforms will let teams deploy robot fleets without rebuilding every layer of the stack from scratch.

A company should increasingly be able to combine a low-cost robot body from one vendor, a specialized gripper from another, an open model, and a shared fleet-management layer, then deploy the system into a real workflow. That is a much more accessible world than one where every robotics company has to build its own hardware, models, tooling, and operating software.

This is what open systems do well. They lower the cost of experimentation, bring in more builders, and speed up the entire market.

The “Android for robotics” opportunity is real.

But open source alone is not a business model. Open models and interoperable hardware will drive adoption, but the best platform companies will own a control point that compounds, e.g. the default fleet-management layer for heterogeneous robotics or managed data and simulation infrastructure. The key question for builders and investors alike is whether every new deployed fleet makes the platform better.

A company managing thousands of robots across different environments will learn which components work together, where deployments fail, and how to reduce the time and cost of getting a robot into production. Those insights improve the platform, helping attract more builders and fleets, helping further improve the platform.

This is a different type of operating loop from the vertically integrated company. The vertical company learns how to perform one specific job better than anyone else. The modular platform company learns how to help many different companies deploy robots more effectively.

Both can create enormous businesses.

At Canonical, we are particularly excited by vertically integrated companies with proprietary access to difficult physical workflows that improve with every deployment. We also want to meet teams building the modular platforms that help the broader robotics ecosystem build and deploy faster.

Zuck made the political case for open-weight AI, an NVIDIA-led coalition made the economic case, and Illia made the systems case. Meanwhile, 1,324 employees of frontier AI companies made the case for restraint.

What divides them is which danger comes first: a few companies owning everything, America falling behind China, AI finding software holes faster than anyone can patch them, or models improving themselves beyond anyone’s ability to follow.

Zuck’s case is about power: no single superintelligence can represent everyone’s values, so access must be distributed. Concentrated intelligence produces concentrated authority. That makes sense, but it also assumes equal compute, which remains unequal. Whoever can afford more inference, better memory, lower latency and more agents still has the stronger army. Weights can become open while power stays scarce.

Zuck then concedes that biological risks may require coordinated limits on how capable models are deployed. Open weights make those limits harder to enforce. Once the weights are released, anyone can run or modify them, and the original developer cannot take them back. His answer to concentrated power leaves this irreversibility problem unresolved.

NVIDIA’s case is that America will not win by producing a single frontier model. It wins by spreading American models globally before China does. Open weights lower costs, prevent lock-in and let organizations run AI on their own infrastructure.

The underlying incentive makes sense for NVIDIA. It wins when the model layer becomes cheap and compute remains scarce. Open weights push models toward commodity economics, while every fine-tune, evaluation and deployment still burns chips and energy. America gets broader distribution of American AI. NVIDIA gets a market where nobody upstream captures all the margin.

That incentive does not invalidate the economic case. It explains why NVIDIA is leading it.

The 1,324 frontier AI employees worry that models will soon improve AI research itself, creating a feedback loop that moves faster than labs can understand or control. The labs are asking for tools and governance that let society control the pace before that happens.

Their strongest objection to open weights is irreversibility. A hosted model can be monitored, restricted or withdrawn. Released weights cannot.

Anthropic makes this case harder to dismiss because its position is narrower than “open models are dangerous.” Amodei has called lower-capability open models a public good. He objects when downloadable weights become powerful enough to cause catastrophic harm and can no longer be monitored or recalled. This makes sense, but is also self-interested: restrictions on powerful open weights give frontier labs more time to extend their lead.

The open side still has the strongest empirical response. Open-weight models have been around for years, and no catastrophe has arrived. Open models have enabled independent research, local deployment, specialized models and competition. They have given startups an alternative to frontier-model APIs and governments a way to run sovereign AI.

That record matters. Restrictors have to explain what changes at the next capability threshold, where that threshold sits and why the record so far no longer applies. Their answer is simple: yesterday’s models could be safe to release while tomorrow’s are not.

Finally, Illia agrees that more capable models create new risks. He disagrees that restricting access to their weights solves them.

His point is that open vs. closed tells us who can access a model, not whether the system running it is secure. A hosted model can still access sensitive data, call the wrong tools or exploit vulnerable software. Securing AI therefore means securing the whole stack: the model, data, execution environment, hardware and surrounding software.

This is the strongest of the three open arguments. It is also incomplete. Better security can constrain models running inside systems we control. It cannot recall released weights or stop someone from running them elsewhere.

Illia’s position rests on a specific bet: security improves faster than dangerous models spread beyond its reach. If capability wins that race, Anthropic’s irreversibility objection stands. If security keeps pace, the open vs. closed distinction matters much less.

We think all four dangers are real. They just run on different clocks. Concentration is here now. The race with China plays out over years. Recursive self-improvement may arrive at any point, or not at all. The security gap is the one founders can build against today. That makes it the investable theme we’re tracking.

If securing AI is about the whole stack rather than the weights alone, the buildable surface is concrete: sovereign deployment, independent evaluation and red-teaming, agent security, verifiable inference and formal verification.

Some of this already has buyers and budgets. Companies need to know which model ran, what data it accessed and which actions it took. Verifiable inference and formal verification are earlier, more expensive and less proven.

We’re looking to fund companies selling security today with a credible path toward becoming verification infrastructure later. The bet is that security improves fast enough to constrain dangerous models before they spread beyond its reach.

That gap is where an early-stage investor earns the return.

Through this drawdown the speculators walked and the price broke, while the infrastructure kept getting laid and used. Measure the industry against the 2022 bottom instead of against last year, and the picture inverts. Ethereum transaction activity runs roughly thirteen times its 2022 level. Stablecoin supply has roughly doubled to around $300 billion and held there straight through the selloff. Value locked in DeFi is up more than 60%. Tokenized real-world assets sit at a record near $33 billion, led by tokenized Treasuries. Same bear-market price, an industry twice the size. Usage compounded the entire way down. Only the price disagreed.

The clearest tell is where the market already votes with real money. Crypto equities are up about 23% this year while the tokens are down about 36%. That split is not noise. The market is paying for the crypto it can underwrite and declining to pay for the crypto it can only speculate on. Miners sitting on signed power contracts are redirecting that energy into AI compute and getting valued as infrastructure. Lending protocols with real revenue are getting valued as businesses; one of them cleared roughly $900 million in fees over the past year. For the first time the tooling exists to see this plainly, because the fees and the buybacks settle on-chain where anyone can audit them. Cash flow earns a multiple. Narrative earns a chart.

There is a deeper shift underneath that split. For a decade tokens traded at a premium to the businesses beneath them. Today many trade at a discount to the same cash flows. The market has stopped paying up for the idea of a network and started paying for the economics of one. That is not the category dying. It is the category growing up.

Real institutions are building through the winter rather than waiting for the thaw. Federal stablecoin rules finalize this year and take effect in January. Stripe, Visa, BlackRock, and Coinbase are standing up new stablecoins behind that framework. Schwab and E*Trade are adding retail crypto rails to tens of millions of accounts. None of that is a wager on next quarter’s price. It is capacity being poured while the ground is still cheap, by counterparties who do not build on sentiment.

This is where the early-stage job actually gets done. Bull markets reward trading. Bear markets reward underwriting. The founders laying rails and shipping product right now are doing it into flat charts and thin attention, which is exactly what makes them cheap to back and exactly the kind of people worth backing. Anyone can fund usage after the price has confirmed it. The return comes from funding it while the price still argues.

So the discipline is simple. Ignore the quote and read the usage, because the usage is the business and the quote is only the mood. Sentiment collapses in a quarter and the commitment underneath it does not. When the price finally re-rates, it re-rates up to the usage that was there the whole time. The work is to already own the thing before it does.

In 2026, the largest sponsor of free frontier AI is the Chinese state.

Start with the facts. The open models that keep landing at or near the frontier are increasingly Chinese. DeepSeek, Qwen, and Moonshot’s Kimi now trade blows with the best American models on real benchmarks, ship under permissive licenses, and arrive within months of the closed frontier. The old story was that China was a cheap fast-follower, a year back at a tenth of the price. That story is out of date. The gap is now measured in months, and the strongest Chinese labs are confident enough to charge close to full price for a hosted version while still giving the weights away.

This is deliberate commoditization, sanctioned from the top. If you cannot win the frontier outright, the next best outcome is to make sure the frontier is not worth much. Open weights turn the core asset of OpenAI and Anthropic into a free good. They also set the default. Every developer who builds on a Chinese open model is one more person fluent in China’s tools, standards, and assumptions. The United States understood this logic perfectly when it gave GPS away and let the world build on top of it. Whoever owns the standard owns the leverage that comes with it. China is running that playbook one layer up, at the model.

There is a sharp irony underneath this. Export controls were meant to protect an American compute lead. What they actually did was force Chinese labs to get radically better at doing more with less. Starved of the newest chips, they leaned into efficiency: sparser models that wake only a fraction of their parameters per token, cheaper attention, aggressive compression. The same thing happened in lithography, where a constrained foundry reached advanced nodes on older equipment because it had no other option. Constraint is a forcing function. The controls meant to widen the moat are training the discipline that drains it.

This is the part worth sitting with. Open source was supposed to be insurance against concentrated power. Today the biggest force pushing free frontier intelligence into the world is an authoritarian state. We still believe open beats closed. We wrote that recently and we mean it. What changed is not the direction, it is who benefits.

The United States has no clean answer. Banning Chinese open models mostly binds American firms: weights spread by torrent, so you get costlier domestic AI while the rest of the world standardizes on Chinese models anyway. Winning the open game directly needs a lab both willing to open a frontier model and able to build one, and no American lab is both. Either path cedes the default.

For us, the investing implication follows directly. If the model layer is commoditizing and going open, and it is, then durable value does not sit in the weights. It sits in what open weights cannot commoditize: verifiable inference, private deployment, the sovereign and enterprise builds that cannot run on a foreign API, and the distribution that turns a free model into something people actually pay for. Open winning does not mean nobody captures value. It means value moves, and it moves to exactly the layers we have been backing.

The world’s default intelligence may end up open, cheap, and not American. That is not a prediction we love. It is one we would rather underwrite than ignore.

The reason is a collision. Demand for compute is going vertical at the exact moment the one trick the whole industry relied on has run out of room. Power, not chips, is now the binding constraint on AI: this year alone billions of dollars of datacenter capacity has been delayed or cancelled not for lack of funding or silicon, but because the grid cannot deliver electrons fast enough. And a large share of the energy we do deliver is spent not computing but shuttling data between memory and processor. But progress does not stop. It moves, and right now it is moving in three directions at once: new materials, new geometries, and new architectures.

New materials. There is a class of device that holds memory and does the computation in one place, the way a synapse does, instead of separating the two. The physics has been understood for roughly two decades and the performance ceiling is enormous, but none of these devices are in production. The reason: the materials involved have historically been impossible to build into a normal silicon chip without destroying the transistors underneath.

New geometries. Photonic chips do not need three-nanometer features. They need three-dimensional, multi-material, wavelength-scale structures that the world’s most expensive machines were simply never built to make. The unlock there is not smaller. It is a different shape entirely, and the tools optimized for shrinking transistors cannot produce it.

New architectures. Once you can co-locate memory and compute, you get to attack the von Neumann bottleneck directly rather than papering over it with faster interconnects and more cache. The prize is doing the core operation of a neural network in a single physical step instead of thousands of read-write shuffles. That is where the order-of-magnitude efficiency gains live. And when power is the constraint, efficiency is the only thing that matters.

In every one of these cases, the breakthrough is not the invention. It is the integration. The exotic material, the photonic structure, and the analog crossbar have existed in labs for years. What’s been missing is a way to manufacture them at scale without throwing away the trillion-dollar silicon supply chain. The companies we think win this decade are those who figure out how to build these devices on machines the fabs already own, inside an existing process. Complement the incumbent, do not try to replace it. The moat moved from what you can invent to what you can integrate, and that is a much less crowded, much more defensible place to stand.

This reframes what we look for. In diligence, we care less about a benchmark from a clean-room prototype than whether the process survives contact with a real foundry, which is where almost everyone stalls. It also reopens the map. The leading-edge logic race is lost outside Taiwan and a single town in the Netherlands, but the new wedges are early, unoptimized, and open to new entrants in a way the transistor race has not been for thirty years. That is why the best teams we see are not trying to out-TSMC TSMC. They are building what TSMC was never designed to build.

Why now? AI created both halves of the opportunity. It created the demand by making energy efficiency existential. And it supplied the tools to meet it: cheap GPU compute, better lasers, a consolidated supply chain. These approaches turned viable in the last twenty-four months, not the last twenty years. The constraint and the cure arrived together.

Arena announced a $100M annual revenue run rate, eight months after launching its evaluation product. It started as a research project at UC Berkeley, with one mission: to measure AI progress through real-world use. The company is barely a year into commercialization, but that research began back in 2023. By the time the revenue showed up, the hardest part was already done: earning the whole field’s trust as the neutral scoreboard for AI.

That gap is what we keep coming back to. Arena’s team figured out how to measure model utility before it was even recognized as a central problem by industry. The research was the moat, and the revenue is the market catching up to a bet placed long ago in a lab. We would rather get to know these people while they are still publishing than compete for them once every fund has the same warm intro.

This is why our focus on academics is so deliberate. At the frontier, in AI, robotics, energy, and cryptography, the durable edge tends to be technical depth and being early to a paradigm. Academics live there by definition. They often see the shape of the next S-curve before it has a name, and they carry conviction that is hard to reverse-engineer from a competitor’s launch.

It also shapes where we look. We think the best technical founders are spread far wider than the target-school shortlist suggests, so we put as much energy into the research groups at UIUC, UT Austin, and Georgia Tech as we do at Stanford or MIT. The work coming out of the less-trafficked programs is world-class, and the founders behind it are often the least contested. Getting to know them early is one of the most underrated edges we have found in venture.

So we try to treat the lab as the top of the funnel. We map research groups, we read the papers, and we build relationships years before anyone incorporates. Arena is a good reminder of why that patience compounds. The company was there for anyone paying attention, long before it printed a nine-figure run rate. The best companies are often visible on paper first. Our job is simply to be reading.

Last week a couple of days after my panel at SuperAI in Singapore on “Compute, Chips and the Cost of Intelligence“, I stood on a cleared lot in Johor, Malaysia, watching a future AI data center get staked out. Everyone on that panel was talking about GPUs. But what decides whether that lot in Johor becomes a working cluster is not just chips. It is whether you can get a gigawatt of power to the dirt, cool it, and find people who know how to run it. That gap is what this post is about.

It is a companion to our map at canonical.cc/labs/data-centers, which tracks 31 players across 7 layers. It sits next to our Semiconductor Stack and Decentralized AI maps. Body, brain, nervous system of the buildout.

The strange loop

Our silicon primer was predicated on this fact:

the technology breaking the chip industry is the same technology most likely to fix it.

AI demand is breaking the grid. A single campus now draws a gigawatt, a small city’s worth of power for one building. And AI is turning out to be the best tool we have for designing those power systems, tuning the cooling, and squeezing more compute from concrete we already poured.

A building, on a napkin

A data center is a building full of computers. Power comes in, heat comes out, compute happens in between. That is the whole thing. We went from server rooms, to the cloud, to the AI explosion, and the physics never changed. Only the scale did.

The number everyone quotes is PUE, Power Usage Effectiveness: total power divided by the power that reaches the chips. A PUE of 1.2 means you burn 0.2 watts on cooling for every watt of compute. The old server room ran near 2.0. Liquid-cooled AI halls are pushing toward 1.1. At gigawatt scale, that gap is a power plant’s worth of waste.

Now stack it up. From the rental product at the top to the grid at the bottom, every layer is its own market.

1. Neoclouds. The product most AI companies buy: GPUs by the hour, no enterprise baggage. CoreWeave, Crusoe, Lambda, Nebius. CoreWeave proved the category reaches public scale. It also exposed the category’s central risk, below.

2. Operations. Who runs the cluster once it is powered on. The quietest layer on the map, and we think the most mispriced. More below.

3. Compute silicon. The GPUs and accelerators. NVIDIA, AMD, Google TPU, Cerebras. Everyone fights here. We cover it in the silicon map, so we will skip it.

4. Networking. Past 100,000 GPUs, the bottleneck stops being compute and becomes moving data between chips. Arista, Broadcom, optical-I/O upstarts like Ayar Labs.

5. Cooling. Old racks drew 5 to 20 kilowatts; air handled it. AI racks draw 40 to 80 today, and NVIDIA is targeting a megawatt per rack on Rubin Ultra. Air cannot touch that. Liquid goes straight to the chip. Motivair, CoolIT, JetCool.

6. Power. The binding constraint. Constellation restarting Three Mile Island, Oklo and small reactors, plus a fast-growing layer of on-site generation and batteries that smooth a training run’s spiky draw.

7. Facilities. The buildings and land, mostly held in REITs. Equinix, Digital Realty, Vantage, QTS.

So where does it break?

Which layer can actually stop the buildout?

In silicon, the answer was two companies: ASML and TSMC. Here it is not a company. It is power.

About 2,300 gigawatts of generation sit stuck in US interconnection queues, more than the country’s entire installed capacity.

The average project waits five years to connect. Some data centers are quoted twelve.

US data-center demand is headed for roughly 76 gigawatts in 2026, up from 50 in 2024.

In Texas, large-load requests to one utility jumped sevenfold in a year.

Bessemer counts 190 gigawatts of hyperscale capacity already announced against 5-7 year queues.

“We need more compute” is something we are all hinding behind. The world is not just short on chips this quarter. It is short on energized, cooled, staffed megawatts. You can panic-order GPUs. You cannot panic-build a substation, and you cannot panic-train the people who run the room.

Where AI causes the squeeze

Power takes the direct hit. Cooling takes the next one, as rack density climbs an order of magnitude and liquid goes from exotic to mandatory.

Then the strangest one: utilization. The most expensive hardware ever built mostly sits idle. xAI’s fleet reportedly runs near 11%. Hyperscalers run 5-10%. We spent 25 years learning to share CPUs with VMs, containers, and schedulers. Almost none of that exists for GPUs yet. So we scream about a shortage while most of our GPUs sit parked.

Under all of it, people. Maybe a few hundred humans on earth have run a multi-thousand-GPU cluster end to end. The demand is the entire buildout. Construction is no better: the trade is short hundreds of thousands of workers, the average data-center worker is 53, and most operators cannot fill ops roles. The pain lands where the talent is thinnest. Not a coincidence.

Where AI relieves it

The same intelligence stressing the grid is being pointed back at the stack. AI permitting software is compressing the slowest, most bureaucratic step in the build (Paces, Lorica). AI runs cooling and power inside live halls (Phaidra, Corintis). A new layer makes GPU work power-flexible, so a cluster leans into cheap power and backs off when the grid is tight (Emerald AI, Verse). Construction is getting robots (Gecko Robotics, DroneDeploy).

The highest-leverage bet sounds the dullest. Build “VMware for GPUs,” real multi-tenancy and scheduling, and a 10 percent utilization problem becomes a 10x supply unlock. That is more new compute than most fabs add in years, and it ships in software.

Will any of this beat the power constraint this decade? No. Electrons obey physics and permitting, not roadmaps. But the software-shaped layers (permitting, orchestration, utilization, operations) compound in quarters, not half-decades. That is where venture lives.

Sovereign AI: the repatriation trend worth watching

For two decades the logic was centralize: ship compute to whoever had the cheapest power. That is reversing, fast. Countries now treat AI compute as critical infrastructure, like a grid or a port, and they want it on home soil under home law. Some call it geopatriation. I call it the most important capital-flows story in infrastructure right now.

The EU has put 20 billion euros behind AI Gigafactories. France is building a 1-gigawatt campus with the UAE worth tens of billions, while backing Mistral as its champion. Saudi Arabia stood up HUMAIN under its sovereign fund to build the whole stack. The UAE is breaking ground on a multi-gigawatt cluster in Abu Dhabi. Japan and Singapore are moving the same way. We see it at seed stage too, with founders pitching energy-first inference backbones built for Europe.

Repatriation does not shrink the stack, it widens it. Every sovereign build needs its own power, its own cooling, and its own operators, in a country that has never run a frontier cluster. The talent gap that is acute in Virginia becomes a wall in Riyadh or Johor. Many national buildouts means the power and operations layers get demanded everywhere at once. That is the picks-and-shovels case, multiplied by the flags on the map.

Why this could fail

I would be selling you a dream if I skipped the bear case. It is real and specific.

The sharpest risk is financial. Neoclouds buy GPUs with GPU-backed debt. If utilization or pricing softens, the collateral can depreciate faster than the loans amortize, and one demand wobble cascades through the most leveraged layer. CoreWeave’s bull case and bear case are the same fact. Add regulatory backlash (moratoriums, water fights, grid-reliability fights are already spreading) and the chance that hyperscalers re-bundle and fix the allocation problems that created the neocloud layer in the first place. And the deepest question stays open: does inference get effectively free as efficiency compounds, or does demand outrun supply forever, keeping power the permanent constraint? The thesis hinges on which way that breaks.

Our read: the bull case is already priced into the obvious layers, silicon and the public neoclouds. We would rather underwrite where the centralized option is structurally weak and demand is non-negotiable. Power. Liquid cooling. And the operators who turn a half-billion-dollar building full of idle silicon into a working cluster.

The exciting part

Today, standing up AI compute takes a hyperscaler’s balance sheet and a team that barely exists. Productize the operations and power layers and that capability gets unbundled: handed to the sovereign, the enterprise, the neocloud in a country you would not have guessed. Standing up a cluster starts to look less like building a refinery and more like provisioning a service.

Somewhere on that lot in Johor, rebar is going in for a building that will think. Whether it ever does comes down to the least glamorous layers on the map: the wire coming in, the heat going out, and the few people who can keep it alive at 3am. That is the layer we are building toward.

The flashy layers (silicon, the public neoclouds) are mostly priced. The middle (power, cooling, operations) is where we think the next infrastructure companies get built, and where AI is the relief, not the strain. We track all of it, layer by layer, with sources and caveats, on the full map.

Explore the map: Data Centers & Neoclouds at Canonical Labs →

Educational, not investment advice. Figures are point-in-time as of June 2026.

When the input commoditizes, value moves to whoever orchestrates it. That is happening in two directions at once, above the model and below it.

Above the model, the aggregator is becoming the intelligence layer. OpenRouter spent two years routing each request to the single best model. This month it launched Fusion, which fans a prompt out to a panel of models, has a judge reconcile their answers, and returns one synthesized result. The panel beats any single model: 69% on the DRACO research benchmark against 65% for the best solo model, and ahead of both GPT-5.5 and Opus 4.8. The routing layer stopped picking the winner and started manufacturing one.

Below the model, serving is becoming its own discipline. A cheap open model is only cheap if you serve it efficiently, and the optimization surface (batching, KV cache, speculative decoding) moves faster than any one team can track. vLLM is the floor, not the ceiling, and the frontier shifts week to week. The edge is no longer which model you run. It is how few GPUs you need to run it.

We think this is where the margin goes. Not to whoever trains the best model, but to whoever orchestrates models best on top and serves them cheapest underneath. We’ve said before that open weights commoditize the base layer and the moat moves up-stack. The model was the product for three years. Now it is the raw material, and the companies that matter are the ones that turn it into something cheaper, faster, or smarter than any single model could be alone.

We started paying attention anyway because of the talent density. Elite researchers and infrastructure veterans who could have shipped at any centralized lab were choosing to build here instead. They saw decentralization as a force multiplier, not a constraint.

At our AGM last year, we told our LPs that AI was concentrating power in a handful of labs, and that the answer was open, verifiable intelligence built on crypto rails. “Not your weights, not your brain.”

In the time since, four things changed. Governments started requiring AI to run inside their own borders, on infrastructure they control. The GPU shortage priced most startups out of compute. People grew wary of handing every prompt and document to a few opaque providers. And as AI agents began managing real money, they needed a way to prove the right model ran on the right data. Each of those is a reason to build outside the centralized stack.

And the results are no longer hypothetical. A network of 70-plus strangers trained Bittensor’s Covenant-72B on commodity hardware and beat LLaMA-2-70B on MMLU. x402, the HTTP-native agent payment rail, has settled over 161 million machine payments across roughly 69,000 active agents. Aethir booked more than $128 million in real revenue last year from 150-plus enterprise clients. Venice runs private inference for over 2 million users with zero data retention.

What makes decentralized AI different is structural. Three things stand out.

Openness is built in, not bolted on. Pluralis splits a model across many machines so no single party ever holds the full weights. You can use the model, but you can’t take it private and walk away with it.

Cryptography does the work that trust used to. You don’t have to take these systems at their word. EigenCloud ran the same inference 10,000 times and got an identical result every time, which is what lets a smart contract actually rely on the output. Venice encrypts your prompt on your own device, so neither Venice nor the GPU provider ever sees it.

And the people building this are not who you’d expect. NEAR’s founder co-authored the Transformer paper. Sentient’s co-founder invented the core technology behind 4G. This is serious technical talent choosing the harder path.

The obvious objection: OpenAI and Anthropic are running away with this, so why does any of it matter? Because the question was never who builds the smartest model. It is whether AI infrastructure ends up winner-take-all or multi-vendor. The centralized labs win on raw capability, and they will keep that lead. But capability is not the whole market. Governments that won’t run on American clouds, enterprises that can’t expose their data, and agents that need provable execution: none of that is a capability problem, and none of it is something a closed API solves well. Centralized AI compounds through data moats and switching costs. Decentralized AI compounds through networks and adoption. The first wins the short term. We are betting the second matters more over time, and regulation, GPU scarcity, and sovereign mandates are all pushing that way.

The bear case is real:

1. The centralized labs are extremely well capitalized, backed by the best investors and run by exceptional operators. That is an enormous head start.

2. Most decentralized AI projects are yet to find real organic demand. Many are propped up by token emissions rather than customers.

3. “Decentralized” often isn’t. Look closely, and many of the nodes for these networks are fairly centralized.

4. Several layers depend on trusted execution environments, which have been broken before.

5. A lot of this rests on research breakthroughs that have not landed yet, like privacy-preserving computation. By the time they arrive, the window of opportunity may have closed.

We don’t wave any of that away. Our job is to separate the projects solving a real bottleneck, where the centralized option genuinely can’t go, from the ones solving a problem that centralized AI already handles fine.

So we built the map we wanted to read. 35 projects across 9 layers, with the team, the traction, and our view on each. It is a companion to our Semiconductor Stack and Physical AI maps, and it is fully interactive: filter by layer, search by project, click into any one.

We said it a year ago and we will say it again. Still early, but not too early.

Explore the Decentralized AI map →

We think that bundling is the wrong abstraction. A person does not relearn the world every time they reason through a problem. Memory and reasoning are distinct faculties, and the most interesting work in AI is starting to separate them.

Two public results point the same way. DeepMind’s Mixture of a Million Experts treats knowledge as a vast store of tiny experts, retrieving only the handful a query needs and decoupling what a model knows from what it costs to run. From the other direction, Samsung’s Tiny Recursive Model treats reasoning as a small recursive loop: 7 million parameters scoring 45% on ARC-AGI-1, beating Gemini 2.5 Pro, DeepSeek R1, and o3-mini with less than 0.01% of their parameters. One unbundles memory. The other unbundles reasoning. Neither needed scale to win.

The metric that falls out of this is not parameter count but intelligence per FLOP, capability for every unit of compute and energy spent. The author of the TRM paper calls the belief that hard problems require billion-dollar foundation models a trap, and the results are starting to agree.

We think the next frontier is architectural. The teams that separate memory from reasoning will deliver the same intelligence at a fraction of the cost, and that efficiency is exactly what physical AI and on-device inference have been waiting for. Scale was never the point. It was a proxy for the things we had not yet learned to build directly.

27 years ago at Purdue, in ECE 362, I programmed my first chip: a Motorola 68HC12 with a few KBs of memory, hand-written assembly, bolted onto a racecar so we could control its speed remotely. I still remember the rush. Today the chips driving the AI boom cram 200+ billion transistors into one package, and half the world’s governments are fighting over them. Same starting material: sand. And that distance is what this post is about.

The strange loop at the center of this

Marinate on this for a second:

the technology currently breaking the chip industry is the same technology most likely to fix it.

AI eats chips faster than the world can make them. A single frontier model trains on tens of thousands of leading-edge GPUs. And AI is also the best tool we’ve ever had for designing chips, checking them, and squeezing more of them out of the factories we already have. That tension runs through everything below.

To see why, you have to know how a chip gets made. Most people don’t, and that’s fine; the industry hides behind acronyms. The structure underneath fits on a napkin. So that’s what we drew.

(This post is a companion to our full interactive map at canonical.cc/labs/semiconductor-silicon-stack, which tracks 20 incumbents and 40+ challengers across every layer)

The stack, on a napkin

A chip starts as quartz sand and ends as a die with billions of transistors. In between: six physical steps, plus one software pipeline running alongside. Each step is dominated by one or two companies you’ve probably never heard of.

Follow the sand.

1. Raw materials. First you purify sand into polysilicon that is 99.9999999% pure. Nine nines. Take a billion atoms; only one is allowed to be the wrong kind. Two companies, Wacker and Hemlock, dominate, not because the chemistry is secret but because nobody else wants to run a billion-dollar chemical plant that needs a decade of contracts to pay for itself.

2. Wafers. The polysilicon gets grown into a single perfect crystal, then sliced into mirror-polished discs. Two Japanese companies, Shin-Etsu and SUMCO, make most of the world’s supply.

3. Lithography. The famous one. You print the circuit pattern onto the wafer with light, and the features are so small that ordinary light is too fat to draw them. So the machine makes its own: it fires a laser at droplets of molten tin, 50,000x/second, and collects the extreme ultraviolet flash with mirrors so smooth that if you stretched one to the size of a country, the tallest bump would be about a millimeter. One company on Earth makes these machines: ASML, in the Netherlands. The newest model runs $380 million. There is no second source. None.

4. Deposition and etch. Now you build the chip up like a layer cake, hundreds of layers, depositing material and etching it away. Applied Materials, Lam Research, and Tokyo Electron split this market. A healthy three-horse race, though each horse specializes in different tools.

5. Inspection and test. After every step, you hunt for defects a few nanometers wide. Miss one and the chip is garbage. KLA owns this.

6. The fab. TSMC in Taiwan puts the whole symphony together at scale. Samsung is a credible second on some nodes. Intel is rebuilding. At the true leading edge, though? TSMC or nothing.

And running alongside all of it: design software, called EDA. Before anyone touches silicon, the chip lives entirely in software, where it’s designed, simulated, and torture-tested. Three companies (Synopsys, Cadence, Siemens) hold about 75% of that market, with tools whose bones date to the 1990s.

Notice who’s missing: Nvidia. It doesn’t make chips. Neither do AMD or Apple. They’re “fabless”: they design in the EDA tools and hand TSMC the blueprint. The companies above are the ones even trillion-dollar giants depend on.

So where does it break?

Now ask the question a curious person should ask: which of these layers could actually stop the world?

Only two. ASML and TSMC.

If either one stops shipping, the AI industry grinds to a halt within months. Every other layer has at least two serious competitors keeping each other roughly honest.

“Semiconductor shortage” is a vague phrase hiding a precise reality. The world isn’t short on sand, wafers, or etch tools. It’s short on EUV exposures and leading-edge fab slots, which funnel through exactly one Dutch company and one Taiwanese one. And TSMC’s grip goes beyond printing silicon: the advanced packaging that stitches finished dies into a working accelerator also runs mostly through it. Hold that thought.

Where AI causes the squeeze

Now pour the AI boom into that structure and watch where it pinches.

The fab layer takes the direct hit. Everyone who matters (hyperscalers, frontier labs, now entire countries) is fighting over the same TSMC capacity. And you can’t panic-build a fab. Demand moves in months. Fabs move in half-decades and cost tens of billions.

The pressure flows downhill to lithography. More fabs means more EUV machines, and ASML can only assemble so many a year. Each machine has hundreds of thousands of parts, from a supply chain that is itself backed up.

Meanwhile the design side has its own problem: people. AI chips are enormous, and verification, the work of proving a design is correct before you bet $100M+ on manufacturing it, already eats up to 70% of engineering hours on a project. You can’t hire your way out. Training a chip designer takes a decade.

And the chips themselves changed shape. A modern AI accelerator isn’t really one chip. It’s several dies stitched together on an interposer (that’s the CoWoS packaging you’ve maybe heard of), and that stitching capacity is scarcer than wafers.

The pain shows up exactly where competition is thinnest. Not a coincidence.

Where AI relieves it

Here’s the other half of the loop, and the part I find more fun.

On the software side: the people behind Google’s AlphaChip, which used reinforcement learning to lay out chips better than human engineers, left to start Ricursive Intelligence. Salt the claims in this category appropriately. But the direction matters: if AI compresses design cycles from years to months, every design team on Earth just got bigger overnight.

On the hardware side, AI money is funding swings at the chokepoints themselves, which nobody dared for decades. Substrate aims to make chip-printing light with particle accelerators instead of ASML’s tin droplets.

Those are 2 of many. Agentic verification copilots, AI inspection layers squeezing 30% more throughput from existing fabs, free-electron-laser EUV with CHIPS Act backing: the full roster, with traction numbers and caveats, lives on the map.

Will any of them dethrone ASML or TSMC this decade? Almost certainly not. Watch the software layer instead: software ships in months, and the incumbents’ AI offerings are bolt-ons, not rebuilds.

Why hasn’t a 30-year-old monopoly been disrupted already? Rational fear. When a manufacturing run costs $100M+, nobody volunteers to be first to trust an unproven tool. That’s the real moat: challengers can’t just be better, they have to be provably, boringly, run-after-run better.

But if AI draws blood anywhere first, it will be EDA.

The part that should excite you

Follow that thread one more step and you get to the prediction I’d actually bet on. Today, designing a serious chip costs so much (tens of millions in tools and talent before you even pay for manufacturing) that custom silicon is a rich company’s game. “Fabless” mostly means Nvidia, AMD, Apple, Qualcomm.

We’ve seen what happens when that kind of cost collapses. Renting a server from AWS turned “starting a software company” from a $5M proposition into a weekend project, and we got millions of software companies. If AI-native design tools cut the cost of a credible chip by 10x, you don’t get slightly more chip companies. You get thousands of fabless startups designing silicon for problems too small for Nvidia to bother with.

And the demand is already lining up. Physical AI is pushing intelligence into robots, cars, drones, wearables, and factory floors, and most of those chips don’t need the bleeding edge. They need to be cheap, low-power, and exactly right for one job. (We map that whole demand wave separately on our Physical AI & Robotics tracker)

A world of thousands of designs running on mature nodes is also the world where Atomic Semi’s many-small-fabs bet stops sounding crazy. More designers, more specialized chips, more places to make them.

Somewhere on a campus right now, a junior is flashing their first assembly program onto a microcontroller and feeling the same rush I felt with that racecar. The stack they inherit is being rebuilt today.

The hardware chokepoints are decade-long bets you underwrite like fusion. We track all of it, layer by layer, with sourcing and known caveats, on the full interactive map: Semiconductor Stack Disruptors

Educational, not investment advice. Data is point-in-time as of May 2026.

That framing is breaking, and two things changed at once. World models now learn “what happens next” from internet-scale video, giving robots physics priors without hand-coded simulators or armies of teleoperators. And a thin layer of real demonstrations now bootstraps enormous synthetic scale. NVIDIA’s Manipulation-Augmented dataset turns 10 human teleoperated demos into 1,000 domain-randomized examples. The real world becomes the seed, not the substrate.

The data confirms the shift. Robotics went from 1,145 datasets on Hugging Face in 2024 to 26,991 in 2025, climbing from rank 44 to 1, with synthetic generation as the primary driver. And this week, NVIDIA launched Cosmos 3, an open model that collapses vision reasoning, world generation, and action prediction into a single system. Cosmos runs as a physics-grounded simulator that predicts approaches, evaluates them in a closed loop, and converges on behavior without real-world risk.

We think the moat is moving. Not to whoever owns the best model or the most teleoperators, but to whoever builds the best learned simulator. Reality becomes the verification step, and the data flywheel that used to live on customer floors now runs in software. The teams that own the simulator own what comes out of it.

What beat it is API plus CLI.

Anthropic’s Claude Code has become the fastest-onboarding developer surface the company has shipped, despite running entirely in a text terminal that looks like it was designed in 1985. OpenAI killed Operator as a standalone product and now ships its computer-use model through the API instead. Anthropic’s Quick Mode for Claude in Chrome ships a stripped-down agent loop that swaps structured JSON for single-character commands and delivers a roughly threefold speedup on real browsing tasks. The public benchmarks we’ve found point the same direction: agents that work through APIs and command lines are pulling ahead of agents that work through pixels.

The reason is unit economics, and it gets worse for vision every quarter. A browser-based agent pays to tokenize a screenshot and a DOM tree on every single step, feeding a model big enough to reason over a megabyte of UI noise just to find one button. A CLI agent pays for a few hundred structured tokens per step and gets a structured answer back. The gap isn’t one click – it’s tokens-per-step multiplied across every step of every task. Frontier model pricing for structured reasoning is collapsing faster than vision pricing, and the gap is widening. The cost curve is bending against pixels, and the bend is steepening.

The implication for SaaS is the part most software companies have not yet priced in. If the dominant user of your product over the next decade is a fleet of agents, then the surface that matters for distribution is your schema, not your screen. Salesforce, Notion, Linear. Every category leader is about to be evaluated on a single question: how cleanly can a model consume your product without rendering a pixel. The companies that ship a real, documented, agent-readable API as a first-class product become rails – Stripe proved the model years ago. The companies that gate their primitives behind a UI become the legacy layer that the next generation of startups quietly automates around.

There is a real counter to this view: the browser is also the universal abstraction over the long tail of legacy software that will never expose a clean API. State government portals, hospital admin systems, niche industry tools with three customers and a 2008 codebase. That ~15% of the workflow surface is a real market, and vision agents will serve it for a long time. But 15% is a fallback business. The platform fight is the other 85%.

The bigger compounding effect sits at the infrastructure layer. A human can run one workflow at a time. An agent can run a hundred in parallel, each making thousands of API calls. Our bet is that machine-initiated traffic comes to dwarf human-initiated traffic by an order of magnitude, and the inference cycles, bandwidth, and storage required to support that pattern are nowhere in the current hyperscale capex curve. The most underwritten thing in markets right now is not the model layer or the application layer. It is the silicon, fibre, and power required to carry the load that an agent-native software stack actually generates.

Our view, plainly: the SaaS layer bifurcates. The companies that ship clean, agent-readable APIs become the rails. Everything else becomes vision-agent fallback or a wrapper around someone else’s schema. The infrastructure layer captures the spillover, and the spillover is arguably the biggest single capex flywheel in history.

The next decade of software is being built for readers that do not have eyes. Plan for them.

The gap isn’t capability. The models are good enough. The gap is that an agent, no matter how sophisticated, cannot yet participate in commerce as a real economic actor. It can read a contract better than most lawyers. It cannot sign one. It can manage a treasury. It cannot own one. It can run your marketing, your procurement, and your customer support, but you won’t hand it the corporate bank account. There’s no way to scope what it can touch, no audit trail if something goes wrong, and no kill switch that actually works in real time. So the most capable employee at every fast-growing company is locked out of the most leveraged tool.

This is the actual bottleneck in enterprise AI adoption, and it runs deeper than most discussions acknowledge.

Making an agent genuinely autonomous requires solving three distinct planes of infrastructure simultaneously. The first is trust: does the counterparty know who this agent is, what it’s authorized to do, and what its track record looks like? The second is market: can the agent discover work, quote a price, sign a contract, settle payment, and resolve a dispute without human intervention? The third is control: can the organization deploying the agent prove, to a regulator or a board, exactly what the agent was authorized to do and what it actually did?

None of these planes exists in mature form today.

On the trust side, the identity problem alone is unsolved. Existing card frameworks like EMV, 3-D Secure and chargeback rules assume a human clicked a button. KYC verifies a face and an ID, not the autonomous code now acting on a person’s behalf. Once an agent executes a payment, the liability question becomes unresolved: is it the issuer, the merchant, or the model? t54 is one of the more interesting attempts to answer this, building what they call a Know Your Agent layer, binding human identity to agent identity and producing cryptographic proof of intent that financial institutions can actually consume.

On the market side, even getting an agent access to a payment account is harder than it sounds. The obvious solution is to give it access to the company bank account. The obvious problem is that one bad decision wipes the treasury. Bank of Bots is building around this constraint: a segregated, scoped account that sits alongside your existing bank, where each agent gets hard spending limits and merchant whitelists. The blast radius is bounded by design.

And on settlement, fast, cheap, and programmable stablecoins look like the natural rails for agent-to-agent commerce. Except they’re irreversible. No chargebacks, no dispute window. Anchor is building the risk pricing layer that sits in front of stablecoin transactions, evaluating and pricing each one before it broadcasts. The analogy is interchange: credit cards always worked because a chunk of every swipe funded the fraud and dispute machinery. Stablecoins skipped that and skipped the protection with it.

What’s notable is that none of these companies is trying to build the whole stack. Each is one layer. And that’s fine, each layer is genuinely hard. But it also means the market is still very early and very fragmented.

The processing layer itself won’t be where the money is made. Stripe, Coinbase, Circle, and Visa will commoditize that fast. The venture opportunity is in what gets built on top of verified agent payment flows: credit, insurance, and reputation data. The more agents you’ve scored and lent to, the better your underwriting model, and the harder you are to displace. That’s a real moat.

The identity and trust layer is probably the most interesting place to be right now. Whoever builds the credit bureau for software, scoring agents, verifying what they claim about themselves, pricing their risk for merchants and lenders, wins a category that didn’t exist three years ago and that no incumbent is well-positioned to own.

Last week we wrote about where the value gets built in agentic payments. This is why it matters. The models are commoditizing. The infrastructure above them is not built yet. That’s the opportunity.

Every GP I’ve pitched has a similar line ready when an LP asks. I’m not sure any of us actually knows.

So I built a tool to find out. It’s called the Power Law Lab, and it runs 10,000 simulated versions of a fund you describe.

The histogram shows the full distribution of plausible outcomes and not just the headline number.

→ canonical.cc/labs/power-law

What we noticed

Venture returns are power-law shaped. A handful of investments produce nearly all the returns. Every GP knows this. Every LP nods.

But that’s usually where it stops. We don’t internalize the consequences. When I write “we’re targeting a 3x net TVPI” in a deck, what’s behind that number? Is it the mean across plausible outcomes? The median? The top quartile? I’d be lying if I said I had a precise answer.

The mean and the median are not the same thing. In a power-law distribution, the mean is dragged up by rare right-tail outliers. The median is much lower. A “2x fund” by mean might be a 1.2x fund by median, with one Uber-shaped outlier doing all the work. The deck quotes the mean because that’s the flattering number.

What this actually does

Describe a fund its size, the number of investments, the failure rate, the shape of the right tail, your reserves strategy, your ownership target. The lab runs 10,000 simulated versions of that fund and renders the distribution.

The math is a mixture model: each company either returns zero (with some loss probability) or draws an outcome multiple from a truncated Pareto distribution. The shape parameter α controls how fat the tail is, and α turns out to be the most consequential slider in the lab.

Calibration follows public empirical work from Correlation Ventures, Kauffman, Carta and AngelList. The Seed preset assumes a 50% loss rate, α=1.2, cap at 500x. Series A and Growth presets get progressively thinner tails.

I vibe-coded the first version over a weekend. ~800 lines of JavaScript. Runs in your browser. No server.

3 things the lab taught me about our fund

1. More investments doesn’t help you.

Expected fund TVPI is invariant to the number of investments if you hold the strategy constant. Going from N=15 to N=50 doesn’t move the mean. It collapses the variance. That feels safer, but LPs aren’t paying us for the median. They are paying for exposure to the right tail. Indexing the power law squeezes that out of your own portfolio.

2. Follow-on discipline moves more than anything else.

Switching from pro-rata across the portfolio to super pro-rata into winners moves median TVPI more than any other parameter I tested. More than fund size, check ownership, or tail thickness. Reserves aren’t a hedge. They’re a second swing at the same pitch. We had been pro-rata-ing out of optionality and reputation. The math doesn’t support that choice as strongly as I’d assumed.

3. The median fund is boring.

Even with realistic seed-stage parameters, the median simulated fund returns roughly 1.3x net. The point isn’t that venture math is hopeless. It’s that the median is the natural outcome, and the funds distribution is itself power-law shaped. You can’t average your way into the tail.

What this actually means

Most GPs cannot tell you where their current TVPI sits in the distribution of plausible outcomes for their strategy. I couldn’t, before I built this. The pitch deck quotes the mean because that’s the number that flatters us most.

For LPs, the lab gives you a way to plug in a manager’s stated strategy and stress-test their projections. If their 3x target sits in the top decile of plausible outcomes for their setup, you should know that before the IC meeting.

For GPs, it’s an honest conversation with yourself. Where are you in the distribution? What would have to be true for the next-quarter mark to move you up?

Try it

→ canonical.cc/labs/power-law

3 calibrated presets, 6 scripted scenarios. Each scenario isolates a single counter-intuitive thing the math does that pitch decks gloss over. Start with the N debate scenario.

Every parameter gets encoded in the URL hash, so you can send a colleague the exact configuration you’re looking at.

If you find something useful or something you disagree with I’d love to hear it. The model has deliberate simplifications worth poking at: independent company outcomes, no time-value modeling.

We are going to keep building things like this!

Luna does almost everything. She sourced the products, negotiated with suppliers, set the prices, hired the painters for the mural, and ran the phone interviews to hire the two human employees who actually run the floor.

Why does she need them? Because general-purpose robotics isn’t quite there yet.

The gap is hands

Luna can hire, price, source, negotiate, and schedule. What she cannot do is restock a shelf, sweep a floor, or take a delivery off a truck. The cognitive layer of running a physical business is roughly solved. The physical layer is not.

Now imagine the next version of that store. Luna is still the brain, but instead of human employees, affordable robotic arms handle the physical labor. Frontier labs are racing the cost of the cognitive model toward zero. The bottleneck moves entirely to hardware, who can put a reliable, deployable arm next to that digital brain.

Multiply that pattern across every packing line, logistics bay, and commercial back-of-house in the country. You’re staring at a global labor-spend pool that dwarfs SaaS by roughly 30x.

That’s the wedge our portfolio company Robo is going after.

Why this time is actually different

2 things have shifted under the surface:

1. Scaling laws hold in robotics: More tele-operation hours in, lower model loss out. Fleets backed by remote operators are doing economically real work today, allowing startups to deploy immediately and ramp human intervention down as autonomy goes up.

2. The macro stakes are higher: The hurdles are real. Most manipulation models sit in the 80–90% success range, and the US infrastructure layer is paper thin. China installed roughly 295K industrial robots last year. The US installed about 34K. That 10x supply gap is a strategic vulnerability. Because hardware is brutally hard to scale, most robotics startups die in the supply chain.

Where Robo fits

Robo builds affordable robotic arms designed so the arms can eventually build more of themselves (see our earlier post about Robotic Superintelligence) to force down unit economics. They run a dual GTM on identical hardware:

• They sell arms ($2,500/unit): The lowest cost in the category for this payload-to-precision ratio. AI labs buy them for mass data collection; developers buy them to build on. To accelerate adoption, the low-level stack is open-source.

• They deploy arms (Robot-as-a-Service): They walk into high-volume industrial lines (like food packing), set up the hardware, and provide end-to-end tele-op coverage. Customers pay by the hour with zero CapEx. When the model stumbles, a remote human takes over instantly so the line never stops.

This dual motion creates a closed data flywheel. They own both ends: the labs training the models, and the physical floors where those models accumulate real-world hours.

Velocity

In January, the company was just a pegboard, two arms, and simulation software.

5 months later, they have redesigned the arms with a domestic sheet-metal core, scaled an in-house print farm for outer shells, secured ~100 preorders, and gone live with their first deployment.

Crucially, their domestic architecture is engineered to transition into sealed, cleanroom-ready variants. A US-built arm capable of meeting strict pharma and medical specs avoids the shifting regulatory hurdles that plague Chinese-manufactured alternatives.

What I come back to

There’s a version of this company that tried to do humanoids, or build a foundation model, or sell pure software. Instead, they picked the most boring-sounding piece of the stack - affordable hardware and deployment infrastructure, and executed with unusual clarity.

Luna at Andon Market can run a store, but she cannot yet stock her own shelves. Someone has to build the arm that does that, in volume, for a price a corner-store economic model can absorb.

Reach out to Robo if you need some US-homegrown robotic arms that are quick and cost-effective.

How much does this SAFE actually cost us? What if we stack another at a higher cap? How much do we lose if we top up the option pool to 15% at the Series A? At a $1B exit, what do we actually walk away with?

They couldn’t answer those quickly. We couldn’t either, sitting on a call, without opening a spreadsheet.

The math compounds in non-obvious ways. Each post-money SAFE locks in investment divided by cap at conversion. Stacked SAFEs at different caps don’t dilute each other. Founders absorb all of it. Pre-money option pool top-ups come out of existing equity, not the new round. Liquidation preferences hurt at low exits and evaporate at high ones.

Founders sign these terms in moments of high emotion. The lead just committed. The lawyer wants to close. You want to get back to building. The second-order effects don’t surface until years later, by which point your ownership is already where it is.

So we built it. A free, single-page tool that lets founders stack SAFEs at any cap, add priced rounds, top up option pools, set exit valuations, and watch the waterfall move in real time. No login, no spreadsheet.

dilutionlab.canonical.cc

Try it on your own cap table. Drag the exit slider. See exactly how much you walk away with.

We vibe coded it in <30 minutes. The source is on GitHub. If anyone wants to add features or fix corner cases, send a PR.