Hunter HarloffWhat do we do now?
The future is unknowable. Regardless, we are incessantly optimistic. We believe in a beautiful future, one of greater abundance and prosperity for all of humankind. We stand behind the optimistic dreamers, the people who share a techno-humanist vision of what comes next and have the resolve to build it.
This is not idle sentiment. Culture shapes technology, and technology shapes culture in return. It has always been this way. Every generation builds the future it first dared to imagine. Recent technological advancements have annealed this bond. Large language models are made from humanity’s written record. Quite literally, they embody the zeitgeist. Our fears become their fears. Our hopes become their hopes. The stories we tell ourselves about tomorrow are becoming the machines that will build it. As we forge our next chapter, believing in what’s next has never mattered more.
This piece is an exploration of that future, and of where great businesses will be built inside it.
Where in history are we?
“History doesn’t repeat itself, but it often rhymes.”
For twenty years, venture’s most reliable playbook was software as a service. Build a product once, sell it to thousands of companies, and collect rent on it every month at incredible gross margins. It was one of the best business models venture ever had, and it shaped an entire generation of investors.
Looking back, the generation found its voice in 2011, around the time Marc Andreessen published his thesis “Why Software Is Eating the World” in the Wall Street Journal. Founders Fund’s manifesto lamented, “We wanted flying cars, instead we got 140 characters” and questioned “What happened to the future?”
Fifteen years later, the irony is hard to miss: the future is here, software is eating itself, and the best minds of a generation are building flying cars.
Andreessen was right, so right that software is now eating itself: language models are reshaping the industry that built them.
And the future Founders Fund asked about is here too. For a while, the smartest people of a generation built accounting software. Now they’re building power plants, robots, therapeutics, and flying cars.
Venture has changed
The prevailing attitude in venture today is that software is dead, overthrown, like Kronos, by its own child.
It’s easy to hear that and brush it off. Sure, software is dead; everyone’s been saying that for over a year. Here’s the thing - working from the inside of the industry, it’s shocking how different venture looks today from even two years ago. People knew these things were happening, but the effects weren’t fully felt yet. When it’s suddenly easier to get funding for a magnet refinery than an Uber competitor, it does leave you scratching your head and looking around asking what just happened?
I’ll tell you what happened. Capital is rotating. Software funds are raising money to deploy into factories, defense, energy and space. Investors are scrambling to up-skill on enormously complex categories such as molecular biology. The best young engineers are choosing materials, pharmaceuticals, and robotics over another business app.
The move from bits to atoms has been building for years, and recent months have been peak mania.
While the change has been fast, we’re not entirely in uncharted territory. Venture was born in atoms. Silicon Valley is named after an element. One of the deals that started the valley, Arthur Rock funding Fairchild Semiconductor in 1957, was a bet on hardware. Bessemer, one of the oldest firms in the business, is named after a way of making steel. When Don Valentine told Jensen Huang “If you lose my money, I’ll kill you”, he was investing in a new type of chip.
Venture is coming home - and futurists should be happy about that.
But we must tread lightly: home may be harder than we remember. SaaS was an easy button, and it’s gone.
What’s actually worth investing in right now?
What software lost
To answer that, it’s worth backing up for a second. Why exactly do investors think SaaS is dead?
The short answer is that coding models “solved” software. An app that used to take a team of 12 engineers a month can effectively be completed by a single developer in a day. Moreover, the task of engineering itself is different. Writing software used to be an ivory-tower discipline for the “gifted programmer”. Now it means popping in a Zyn and, between prompts, wondering if you should’ve studied biology instead.
Expensive code
Cheaper code breaks one of software’s oldest moats. Software was always really expensive to build. Good engineers command high salaries, are hard to recruit, and (deep in the weeds of bugs and code) can only ship so much. You needed a lot of them to build anything good, so for most companies, buying software was the only sane option.
Coding agents are undoing that. Starbucks, which spends about $400 million a year on software, is already exploring replacing software it buys from Microsoft and IBM with tools built in-house. Smaller teams that used to rent generic software like Notion can now vibe-code their own, built around exactly how they work. The question “Why don’t we just build this ourselves?” is getting harder to dismiss.
Of course, building the software is the easy part of replacing it. Years of integrations, compliance requirements, and tribal knowledge won’t disappear overnight, so the incumbents will be fine for a while. New companies aren’t so lucky. They start without those advantages, in a market where every competitor can build quickly, and the window to make something durable is narrowing. The code alone buys less time than it used to.
Ambitious talent
There is a second shift, and in early-stage investing it may matter even more: what ambitious people want to work on.
Writing software isn’t sexy anymore. The smartest people in the world want to work on hard, impactful problems. Having spoken to talented young people, telling Claude what to code just isn’t all that inspiring. Among brilliant young minds, there is a renaissance of interest in biology, materials science, robotics, manufacturing, space, and the frontier of model research.
This should worry anyone investing in early-stage software. Early-stage investing is foremost a people business. The main thing you underwrite is the team. When talent moves, capital has to follow.
Schumpeterian profits
So software is under scrutiny, the best minds are heading back to the frontier, and capital is following them there. That’s good news for the future. But as history reorganizes, two lessons are worth carrying with us.
The first is about what happens when a new technology shows up. People get excited, and then they fund and build everything it makes possible. That’s a good thing. It’s how the world gets better. Most of the value from a new technology ends up with the people who use it: cheaper power, faster travel, longer lives. Economists call this consumer surplus.
It’s also why innovation, on its own, is a terrible business.
Nobel laureate William Nordhaus estimated that from 1948 to 2001, American innovators captured about 2.2% of the value their innovations created. Being first buys you a temporary reward, what Nordhaus called “Schumpeterian profits,” and then competitors copy you and compete it away.
The intelligence race may produce the largest Schumpeterian profits in history, and some of the shortest-lived. Work that used to require expensive human labor can now be done with compute, and right now that spread earns a premium: companies such as AI-powered rollups that replace labor with models are posting remarkable margins. But as the whole economy shifts from labor to compute, the price of intelligence will fall toward the price of compute, and the price of compute toward the price of energy. Without a moat, you don’t have a generational business, even if you have God in a B300.
Directionally correct isn’t good enough
The second lesson is about manias. When a story about the future is true, capital floods in to fund it, and people stop being discerning. Walking around the valley today, people love to talk about being directionally correct. We’re seeing this lack of discernment right now.
We’ve also seen this before. In the 1840s, Britain went all in on railways, the hottest market of its era. At the peak in 1847, railway construction took up nearly 7% of GDP. Between 1845 and 1850, railway shares lost more than half their value, and some of the biggest railways turned out to be paying shareholders with new investors’ money rather than profits.
The track still got built. The lines approved in those few years add up to more than half the length of Britain’s rail network today. The railways changed the world, and many of the families who funded them lost their savings anyway.
A century and a half later, it happened again. In the five years after 1996, telecom companies spent more than $500 billion on fiber and networks, close to a trillion dollars today. Everyone was right that the internet would need bandwidth. Still, by 2001, about 95% of that fiber sat unused, and Global Crossing, once valued at $47 billion, was bankrupt.
That same fiber now carries YouTube, Netflix, and much of the cloud. The investors were right about where things were going. They never asked how much, and how fast?
Once again, history rhymes. The economist Carlota Perez found the same pattern in every technological revolution since the 1770s. A frenzy of capital builds the infrastructure, a crash follows, and the real returns come in the deployment after it.
Here we find the trap of peak mania. The railway investors were directionally correct. They lost anyway. When everyone can see the writing on the wall, being directionally correct isn’t good enough.
Where should we look to invest?
We believe that great investing sits at the intersection of understanding the future technically and the shape of a good business.
Here is our view of where the future is going. Great businesses will be built inside these themes. Topology exists to back them.
Worldviews
Token consumption goes up
In May 2026, Google said it processes 3.2 quadrillion tokens a month, up from 9.7 trillion two years earlier. That’s more than 300x. The same month, OpenRouter, which routes developers’ requests across hundreds of models, said its weekly volume had grown fivefold in six months, to 25 trillion tokens. We don’t expect either to slow down: every agent, every line of generated code, all valuable knowledge work automation runs on tokens. Long horizon workflows are getting longer, models are getting bigger, thinking depth is getting deeper, video models are coming online, more and more people are using AI. We’re going to need a lot more tokens in the future.
Worldview 01
Brain-computer interfaces work
Modalities to read brain signals, particularly electroencephalogram and ultrasound, have improved significantly in recent years. We expect this trend to continue. Moreover, brain signals are faint and noisy, which makes decoding them a machine-learning problem. Progress among neuro companies such as Alljoined is compelling.
Thought becomes an interface to AI
Keyboards and screens are slow channels between a person and a machine. Moreover, language is and always has been a lossy medium of communication. BCIs show promise for developing something much better: latent-space to latent-space.
Brain-to-brain communication becomes possible
Language models learned to place words in an embedding space, where meaning becomes geometry. Once we can do the same for brain activity, two minds could meet in that space directly, passing a thought from one to the other without first squeezing it into words. What that becomes is hard to say: shared intuition, shared feeling, or something we don’t yet have language for.
We regulate emotions with technology
Mood lives in brain circuits, and many of the ones that matter sit deep, beyond the reach of anything placed on the scalp. Transcranial focused ultrasound reaches them: sound passes through the skull and can be focused to a few millimeters, turning activity in a region like the amygdala up or down without surgery. Early trials are testing it for depression, anxiety and addiction. Teams such as Nudge are building it into non-invasive devices for everyday life, and Arbor Neuroscience (formerly Forest Neurotech) is building ultrasound instruments to map and change the circuits behind mental illness.
Worldview 02
Agents commit fraud
We are heading into a world where all digital media can be faked. When an agent can facetime you and appear indistinguishable from your mother or father; when sovereign powers can launch thousands of convincing personas onto social media; when pig-butchering runs automated and never tires; how do we know what’s real?
Phishing gets personalized at scale
Phishing used to be a numbers game: millions of generic emails, hoping one landed. Now every message can be researched, written and voiced for one person, in a voice or face they trust.
Cyber for fraud and identity becomes increasingly important
If nothing digital can be trusted on its face, we need ways to know that a person is human, that a video is real, that a voice belongs to who it claims. Proof of humanhood has long existed almost as a ‘meme’ among investors. With products launching such as Tavus’s Griffin, this will change. We will also need to fight against the sea of slop, if we are to defend against The Dead Internet. Proactive takedowns and identification are critical to that future.
Worldview 03
LLMs produce novel discoveries
In 2024, half of the Nobel Prize in Chemistry went to the creators of AlphaFold, a model that predicts the shape of proteins. A year later, Insilico Medicine published mid-stage trial results for rentosertib, a drug whose target and molecule were both found with AI. These are both small precursors to the opportunity opened up by agentic scientists. Models will propose drugs and materials faster than we can test them, leading to the fastest advancement of material science and biology that history has ever seen.
Labs run themselves
Agents need physical grounding, lest they sit inside of Plato’s cave or solely perform mathematical research. Robotic labs will run experiments around the clock, with a model choosing what to try next and learning from every result. Autonomous labs will be radically important. Companies such as Periodic Labs are working on this today.
Experiments get cheaper, so we run more
When experiments cost less, scientists run more of them. Each one consumes reagents, cells and instrument time, so demand for lab inputs climbs too.
Clinical trials become the bottleneck
More drug candidates means more of them waiting to be tested in people. Trials are slow, expensive and regulated. Technology to speed up trials or make them cheaper will heat up.
New materials outpace manufacturing
A model can find a better battery or magnet in software. Making it at scale is the hard part - which is why we believe manufacturing becomes the bottleneck when discovery is cheap.
Worldview 04
Personalized medicine becomes standard
Medicine will move from one-size-fits-all to the individual: formulated, dosed and delivered for one patient. During the GLP-1 shortages, compounding pharmacies made versions of the drugs for patients who couldn’t get the brand-name product, and telehealth brands shipped them straight to patients’ doors. It set the precedent for the future: medicine formulated and dosed for one patient, and sold by a brand they trust.
Pills are made per patient
Compounding pharmacies make medication to order instead of in batches of millions. Regulated under sections 503A and 503B of U.S. drug law, they become the factory floor of personalized medicine. Companies such as Paladin are building here.
People buy medicine from brands
Patients increasingly choose a brand they trust over a pharmacy counter, the way they already buy glasses or skincare. The brand owns the relationship, and the compounder makes the product. Companies such as Gaia are building here.
Worldview 05
Code creation continues to automate
Coding models were one of the first areas of PMF for language models and agents, for good reason. Code is incredibly well documented online, verifiable (hello, RL environments!), and familiar to model developers. Today, it’s safe to say that coding agents are really, really good. In late 2024, Google said more than a quarter of its new code was written by AI. We expect that share to approach all of it. Models went from completing lines to writing features to working as engineers, and companies like Cognition now sell the engineer, not the tool.
Checking and securing code becomes the bottleneck
When shipping a firehose of prompted software is the standard, knowing that it works and is secure becomes the bottleneck. Integration tests, QA, fuzzing, and agentic review will grow in importance to ensure that code ships which doesn’t spill user data, or churn users. Most engineers we speak to today spend more time reviewing their agent’s code than anything else. One interesting direction here is formal verification: proving that code works as intended via logic.
Companies build software instead of buying it
Why pay for a seat in someone else’s product when an agent can build exactly what you need? This summer, Starbucks began using coding agents to replace software it buys from Microsoft and IBM, and put “every contract and service” in its roughly $400 million annual software budget up for review. We see this as evidence of a larger trend.
Anyone can build apps
You no longer need to be an engineer to ship software. Millions of new builders need places to build, host and run what they make - and it can’t look like the developer-centric platforms of yesterday. For the backend - giving everyone’s agent its own server is a natural step, and one we see occurring with Dot, Grok Bot, and Muse. The outcome of this probably looks more like people using personalized software, rather than your grandma pushing viral apps to a marketplace.
Companies run with zero people
As models continue to perfect knowledge work and complete longer-horizon tasks, at some point an engine which runs an entire company autonomously should become possible. The zero person company will probably succeed in a quick-feedback-cycle sector such as consumer.
Worldview 06
Token consumption goes up
In May 2026, Google said it processes 3.2 quadrillion tokens a month, up from 9.7 trillion two years earlier. That’s more than 300x. The same month, OpenRouter, which routes developers’ requests across hundreds of models, said its weekly volume had grown fivefold in six months, to 25 trillion tokens. We don’t expect either to slow down: every agent, every line of generated code, all valuable knowledge work automation runs on tokens. Long horizon workflows are getting longer, models are getting bigger, thinking depth is getting deeper, video models are coming online, more and more people are using AI. We’re going to need a lot more tokens in the future.
We need more compute
The physical world doesn’t scale like the internet, and the AI build-out is now a physical one: chips, fabs, power, legislation, and concrete.
Specialized chips win inference
Training a model and serving it are different jobs. General-purpose GPUs do both; chips built only to serve models can do one far better, on cost and speed.
The architecture of compute can be better optimized for inference. This is spawning a legion of custom-built inference chips such as Etched, Groq, Cerebras, and more. Hyperscalers are entering the game as well: Google with TPUs, Amazon with Trainium, Microsoft with Maia. The future is not monopolized by NVIDIA.
Fab capacity runs short
More compute means more chips. Every chip comes from fabrication and lithography. The technology that makes this possible is insanely complex, and very monopolistic. Nearly all of the most advanced chips come from one company, TSMC, mostly in Taiwan. Companies such as Fab2 are rethinking how a fab gets built at all.
Power runs short
AI data centers are now measured in gigawatts, and the grid wasn’t built for them. For two decades power consumption in America declined, and the resulting infrastructure largely forgot how to grow. Increasing America’s power footprint is well underway, and the focus of extreme capital allocation.
We need cheaper compute
Chips get squeezed for more tokens
Better software and kernels serve more tokens from the same chip. Companies such as inference.net are building here.
Small models get tuned for single jobs
Most jobs don’t need the biggest model. A small model trained for one task can match a large one at a fraction of the cost. This trend is speeding up with meta-agents: having agents optimize other agents. We see this as especially popular among enterprise companies shocked by token spend. Companies such as Prime Intellect are building here.
Worldview 07
Agent usage skyrockets
There will be many more agents in the future. Which begs the question: will there be a product that every agent uses?
Agents search the web more than people do
Agents use computers the way people do
Most software was built for a person with a screen. Agents need browsers they can drive the way we do. This trend is obvious - and recently unlocked products such as Muse, Grok Bot, and Dot. Browserbase offers this as a service.
Agents need somewhere to run
An agent that writes and runs code needs a safe, disposable computer to do it in, spun up in an instant and thrown away after. We expect every agent to write and run code at the limit - so we’re going to need a lot of sandboxes!
Agents buy things on our behalf
Agents will book, order and pay. Once an agent controls your money, you’ll want simple ways to set its budget, approve what it buys, catch fraud, and claw back mistakes. Pasting your credit card into a chat window is not a long-term solution. The ‘battle of agentic commerce’ has already begun. Coinbase’s x402 revives the web’s long-dormant “payment required” code for stablecoin payments, while Visa, Mastercard, Anthropic, Stripe and Google are each pushing their own standard. The future here is not at all certain.
Every agent needs an identity and permissions
An agent with access to your email and bank account needs clear limits on what it can do. Moreover, as agents flood the internet and SMBs, we need to figure out what’s real. Imagine an SMB inundated by Muses, because reach out friction dropped to zero. The SMB can’t just ignore this new inbound - some of it is real revenue. But they also don’t want to waste their time talking to nameless, potentially vagrant agents. They need to be able to identify who is calling, and trust that identity. That trust and identity layer also needs to be built.
Agents misbehave
Since output is nondeterministic, agents can fail in a huge variety of subtle ways. As a result, agent developers need ways to identify user frustration and feedback, alongside tools to observe what their agents are doing. Companies such as Raindrop watch what agents do in production and catch mistakes before users do.
Worldview 08
We use more robots
We believe the core insight here is simple, there will be more robots in the future. If you believe this, a myriad of second and third order effects follow. We need to build a future that allows for more robots. Outside of a simple view that there will be more robots - there are good reasons to believe that now is the right time for robotics to flourish. For the first time in history, we have a path toward a unified perception, planning, and control framework: the VLA. The importance of this should not be underweighted. Even if the VLA does not immediately lend itself to complete generalization (which, it might), it still offers a simple artifact which we can fine-tune and utilize to automate a new segment of manufacturing and industrial tasks.
We build them
Manufacturing re-shores
The increasing capabilities of robotics may have widespread impact on domestic manufacturing, re-shoring, and the future of production. For example, textile plants that were previously offshored due to labor arbitrage may now become economical to operate domestically, avoiding costly international freight. On the other hand, China now vastly outpaces American robotic production and use. When the mechanism for influencing the physical world is physical, robots and drones become the expression of military power. This may lead to important national security incentives which will benefit the Western case.
Actuators become the bottleneck
Actuators are how anything digital actually moves. Each one is a robot’s muscle: a motor, a gearbox and the electronics that turn code into motion. Humanoids need dozens of them; Optimus has ~28 in its body alone, before counting its hands, which are another problem entirely. A high-precision robot, like the one that might someday do your dishes, can’t get by on cheap actuators. Humanoid actuators are actually a difficult, partly unsolved engineering problem, with approaches ranging from quasi-direct drives to harmonic drives. As a result, actuators make up 40 to 60 percent of a humanoid’s bill of materials, and the precision parts inside them come from a handful of suppliers.
Rare earths become strategic
While every robot needs an actuator to move, every actuator needs a motor to spin. Every motor needs a magnet. The strongest motors run on rare-earth magnets, and around 94% of sintered rare earth magnets come from China. We can’t replicate this advantage overnight: the process to make them is extremely complex, and very environmentally damaging. Secure supply is a matter of national security.
We make them smart
One model controls many robots
Robotics used to mean one model per robot, per task. That is changing. Companies such as Physical Intelligence and Skild AI are now building general-purpose robot brains. We think they’ll succeed.
Robots train in simulated worlds (models)
A robot can’t learn to wash dishes by breaking a million real plates, so roboticists have historically trained in simulation. There’s always been a problem though: physics engines always get something wrong: water moves the wrong way, a dish doesn’t break under the right force (canonically called the sim2real gap). World models flip the approach, learning physics from video instead of hard-coding it: the Bitter Lesson, applied to the physical world. Odyssey already streams interactive worlds like this in real time, and Waymo uses a world model built on Google DeepMind’s Genie 3 to simulate scenes its cars have never seen. We see training robotics inside of world models as important to achieving a general policy.
Robots have onboard silicon
A robot on a moving factory floor can’t wait on a round trip to the cloud to decide whether to stop. Its brain has to run on board. NVIDIA’s Jetson Thor, a robot computer that runs at up to 130 watts, has been adopted by Agility, Boston Dynamics and Figure, among others. As VLAs grow, the gap between what the models want and what onboard silicon supports will widen. This means we’ll have a lot of capable inference accelerators running around in the wild, built for low-power environments.
Physical data becomes valuable
The early success of language models came from the internet; but there is no internet for physical movement. General robotics models likely require building one. There’s a few approaches we see today. Teleoperation, where a person drives the robot directly, was the first standard, but it is slow and expensive, so a cheaper playbook emerged: put head cameras on factory workers overseas and sell the footage to robotics labs. BuildAI started with 10,000 hours from 85 factories and has since published 100,000, and as more suppliers pile in, prices are already falling. Today, we’re seeing buyers want high quality task-diverse data that mimics the robot itself, captured in 3D with depth cameras, sensor gloves, or by making the human look like the robot: Sunday gives people a glove matched to its robot’s hand, and new entrants hand line workers robot grippers to do their jobs with.
We deploy them
Verticalizers win
Clayton Christensen observed that while a technology is still immature, the companies that integrate the whole stack win. Robotics is in this phase. The most valuable robotics companies won’t sell robots; they’ll sell the work: Waymo sells rides, not cars, and Hadrian runs its own automated factories instead of selling machine tools. Priced against the work it replaces, with every job feeding its own models, the verticalizer keeps the margin that hardware and model makers compete away. And once hardware and models commoditize, the moats that remain are the workflow, the supply chain, and the customer.
Every robot needs observability
When a robot drops a package or clips a shelf, someone has to replay what it saw and why it acted. The answer is observability infrastructure. Insurers and regulators will require it - and robot developers will need it to diagnose and improve their fleets.
Robots become a target
A hacked laptop leaks your Facebook password. A hacked robot kills someone. Millions of robots in homes and factories become a Trojan horse army for whatever adversary that manages to control them. The incentives for backdoors in robots are clear, and have already caused issue. In 2025, researchers at Alias Robotics found that anyone within Bluetooth range could take full control of Unitree’s G1 humanoid using a key shared by every unit. We find this incredibly troubling. With adversarial agentic swarms coming online, and increased geopolitical tensions likely, securing robots will be an extremely important aspect of the future. If we get it wrong - the damage caused by such a cyber attack may be the largest in history.
Meet us at the edge
We invest in people hellbent on ushering in new ages of progress. If you’re building at the edge, we want to hear from you.
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