Over a trillion dollars has been wiped from software stocks since September 2025. The IGV software sector ETF is down roughly 30%. Forward earnings multiples for software companies have collapsed from 39x to 21x — a 46% compression in months, not years. Morgan Stanley published what they diplomatically called a “gut check” on the sector, identifying a “trinity of software fears” that amounts to: AI isn’t just disrupting software companies, it’s eating the concept of software as a business.
There’s no venture-backed SaaS IPO filing on the horizon. Klarna ripped out Salesforce and built its own CRM with AI. Every time Anthropic launches a new product — cybersecurity tools, legal copilots — a cluster of stocks in that vertical sells off like someone yelled fire in a theater.
The honest version of every LP meeting happening right now is four words: what the fuck now?
I’ve raised $108.5 million over ten years. Seed to Series B, angels to Autodesk to Lockheed Martin to the CIA’s venture arm to the British government. I have over a hundred investors on my cap table spanning every category of capital except public markets and private equity. Between 2019 and 2022, I took roughly a thousand VC meetings to keep my company alive through Covid. I’ve been skiing with these people. I’ve been on holiday with them. I’ve read their books, made thousands of versions of my investor deck, and sat through more pitch meetings than I can count. I’ve also sat on the other side of the table — nearly sixty angel investments and counting. I’ve been the founder begging for checks and the check-writer deciding who gets one. The view is different from each side. The herd looks the same from both. I’m not writing this as an analyst. I’m writing it as someone who’s spent a decade watching this from the inside.
The Herd
Most VCs lose money.
The Kauffman Foundation — one of America’s largest institutional investors and a major LP — published a landmark study in 2012, “We Have Met the Enemy... And He Is Us.” They analyzed 99 VC funds from 1989 to 2011 and found that 62 of 99 failed to exceed returns available from small-cap public equities after fees. Not “slightly trailed the S&P.” Beaten by a boring index fund with zero management fees.
The power law in venture capital is real, but it’s even more extreme than most people realize. A tiny fraction of funds capture nearly all of the industry’s returns. AngelList’s 2024 Access Fund analysis — the work of Abraham Othman and team on access versus selection — showed that broad market-rate exposure to the startup ecosystem is virtually assured of capturing return-driving companies. The individual stock-picker, statistically, is not.
This matters because of what it reveals about how VCs actually make decisions. If you’re a fund manager and you know — consciously or not — that your odds of picking winners are low, what’s your rational strategy? It’s not to develop genuinely contrarian theses. It’s to stay close to consensus. If you invest in what everyone else is investing in and it fails, you were unlucky along with the market. If you go contrarian and fail, you’re an idiot.
Career risk dominates investment risk.
The incentive structure of venture capital — 2% management fees on committed capital regardless of returns — means that staying in the herd is individually rational even when it’s collectively stupid. Steven Sinofsky, writing on his Substack, put it plainly: “Wall Street is filled with investors of all types... they tend to run in herds. The past couple of weeks have definitely seen the herd collectively conclude that somehow software is dead.”
Sequoia — arguably the most successful venture firm in history — has published its “wake up” memo three times. “R.I.P. Good Times” in October 2008. “Adapting to Endure” in May 2022. Their generative AI memo in 2024. The pattern itself is the data point. Every cycle, the herd stampedes in one direction, the smartest firm in the room publishes a correction, the landscape reshuffles, and the herd finds a new direction to stampede.
So when every VC in San Francisco is saying “software is dead, AI is everything, atoms are the new bits” — and when every VC in London is saying “see, we told you hardware matters” — the first question to ask isn’t whether they’re right.
It’s whether this is analysis or another stampede.
The Track Record
The personal computer was supposed to kill the mainframe. IBM’s mainframe business — the thing everyone wrote off in the ’90s — still processes roughly 68% of the world’s production IT workloads, according to IBM’s own figures. (Take that stat with a grain of salt; it’s IBM marketing data, not independently verified. But the directional point stands: mainframes didn’t die.) The PC didn’t kill centralized computing. It created so much new demand for computing that centralized systems grew alongside distributed ones.
The graphical user interface was supposed to kill the command line. In 2026, the most sophisticated developers in the world are typing commands into a black screen with a blinking cursor. Claude Code runs in a terminal. The CLI isn’t just alive — it’s back as arguably the primary interface for the fastest-growing category in software.
E-commerce was supposed to kill retail. Amazon and Walmart are both trillion-dollar companies. U.S. e-commerce is still under 20% of total retail sales per the Census Bureau, a quarter century after Amazon went public. Physical retail shed its weakest players; the survivors are stronger than ever.
Streaming was supposed to kill Hollywood. The MPA’s annual THEME report shows global filmed entertainment revenue — theatrical plus home and mobile — north of $100 billion and growing. More video content is being produced than at any point in history. The economics shifted. The total output expanded. The weakest players died.
Sinofsky’s core argument — drawn from decades of tech industry experience — is that every disruption plays out WAY slower than predicted, on 25+ year professional career timescales rather than the 2-3 year windows panicking investors imagine. He’s broadly right. The historical track record of “X is dead” predictions is abysmal.
But sometimes the herd is directionally right even when it’s wrong on timing.
The music industry really did collapse. U.S. recorded music revenue (per the RIAA) fell from its peak of roughly $14.6 billion in 1999 to $6.7 billion in 2014. It recovered through streaming — but the recovery took 15 years and the value accrued to entirely different companies. If you were a VC invested in a traditional label in 2001, “the music industry will eventually be fine” was cold comfort. It was fine. You were still broke.
Print media really did collapse. Not “evolved” — collapsed. U.S. newspaper advertising revenue fell from roughly $47 billion in 2006 to $9 billion by 2020, per Pew Research Center. The total amount of written content produced exploded, exactly as optimists predicted. But the economic value migrated to platforms the newspaper industry never owned and couldn’t build.
The pattern isn’t “nothing ever dies.” The pattern is: the activity expands, but the economic value migrates to new structures, and the migration takes longer than pessimists fear but is more total than optimists expect.
Is software in the “retail” bucket — disrupted but fundamentally sound? Or the “newspapers” bucket — activity explodes, value migrates, incumbents die?
What’s Actually Different This Time
The cost of building software is approaching zero. Not declining. Not getting cheaper. Approaching zero. Lex Zhao at One Way Ventures says “the barriers to entry for creating software are so low now thanks to coding agents that the build versus buy decision is shifting toward build.” This isn’t aspirational. It’s descriptive. Klarna built its own CRM. AI-native startups — Cursor, Lovable, Bolt among them — have reached staggering revenue milestones at speeds that would’ve been physically impossible three years ago.
This is genuinely new. In every previous technology disruption, the cost of the new thing dropped while the cost of the existing thing held. PCs got cheap; mainframes stayed expensive. Streaming got cheap; producing prestige TV stayed expensive. This time, the cost of building the very thing venture capital has been funding for 30 years — software — is the thing that’s cratering.
The per-seat model is breaking. SaaS economics were built on a beautiful flywheel: more employees at the customer means more seats, more seats means more revenue, revenue grows with the customer’s headcount. But if AI agents do the work humans used to do, the customer’s headcount shrinks — and their software subscriptions shrink proportionally. Morgan Stanley’s “trinity of fears” identified this: if software lets a company cut staff by half, it cuts software subscriptions by half too.
The revenue model and the value proposition are in direct conflict.
Some companies are adapting. Sierra, Bret Taylor’s AI customer service company, reportedly hit $100 million ARR in under two years using outcome-based pricing — you pay for results, not seats. But this isn’t a minor pricing adjustment. It’s a fundamental restructuring of how software value is captured, and most existing SaaS companies have their entire go-to-market, sales compensation, and financial projections built on the old model.
The foundation model layer is commoditizing faster than anyone expected. Sequoia flagged this in their late-2024 memo: the “one model to rule them all” hypothesis was wrong. GPT-4-level token pricing has dropped roughly 98% in under two years (track it yourself — compare OpenAI’s pricing page from March 2023 to today’s frontier model rates). The model layer is a knife fight between Microsoft/OpenAI, AWS/Anthropic, Meta’s open-source play, and Google DeepMind. Foundation models largely failed to become application-layer products. The value isn’t in the model. It’s in what you build on top of it.
Here’s where it gets interesting. Sinofsky argues that “AI changes what we build and who builds it, but not how much needs to be built.” Demis Hassabis has suggested the shakeout will take a decade or more before anything like “radical abundance” materializes. An F-Prime investor told TechCrunch: “This may be the first time in history that the terminal value of software is being fundamentally questioned.”
Sinofsky is probably right about volume. Hassabis is probably right about timescale. And F-Prime has identified the crux: it’s not that software stops existing. It’s that software stops being a business in the way venture capital has understood that word for three decades.
If more software than ever gets built (Sinofsky), but building it costs nothing (observable), and per-seat monetization is broken (observable), and the shakeout takes a decade-plus (Hassabis) — then: the volume of software will explode while the venture-backable economics of software implode.
More software. Fewer software businesses worth funding.
That’s not “software is dead.” It’s more precise and, for VCs, more unsettling: software is becoming a commodity input rather than a venture-scale opportunity. Like bandwidth. Like cloud compute. Essential, ubiquitous, and not where the margin lives.
Where the Logic Leads
If the venture-scale margin isn’t in software, where is it?
AI makes software nearly free to produce. The bottleneck — the scarce thing, and therefore the valuable thing — shifts to whatever AI and software can’t easily replicate. Physical systems. Regulatory moats. Domain expertise forged through years of real-world operations. Supply chains. Manufacturing capability. Anything where the constraint isn’t “can we write the code?” but “can we move atoms, navigate regulations, or accumulate irreplaceable operating knowledge?”
Not a novel observation. But the reasoning that gets you here matters, because it reveals the scale.
Every previous wave of information technology ultimately drove demand for physical things. The PC revolution created demand for monitors, printers, cables, data centers. The internet created demand for fiber optics, cell towers, server farms, delivery logistics. Mobile created demand for rare earth mining, precision glass manufacturing, semiconductor fabs. Each wave of bits eventually crashed into the shore of atoms, and when it did, the companies at that interface captured enormous value.
The AI wave will be no different, except the interface is broader. When AI can write software but can’t machine a turbine blade or build a semiconductor fab or navigate FDA approval, capital should flow toward companies that combine AI with physical capability. Not AI or atoms — AI applied to atoms.
But here’s what this thesis gets wrong when stated too simply: not all “atoms” businesses are venture-backable, and pretending otherwise is how you lose a billion dollars.
The graveyard is real. Bright Machines raised over $400 million to bring AI to manufacturing and is essentially dead. Sight Machine raised north of $100 million and flatlined. Rethink Robotics — Rod Brooks’ company, with a pedigree that should’ve been unkillable — shut down in 2018. Veo Robotics: fire-sold for pennies on the dollar. The “AI + physical world” thesis has been tried repeatedly, particularly in manufacturing, and the default outcome has been failure.
Why? Three reasons that the current hype wave conveniently ignores. First, selling to manufacturers is brutal — long sales cycles, conservative buyers, fragmented markets, no virality. Second, manufacturing data is heterogeneous garbage: every shop floor is different, the data is owned by the customer, and building training datasets requires site-by-site integration that doesn’t scale like scraping the internet does. Third, the economics were always fighting gravity — manufacturing technology companies historically trade at 2-4x revenue, not the 10-15x SaaS commands, because the margins are lower and the capital requirements are higher.
What’s structurally different now? The foundation model layer. Previous “AI + atoms” companies had to build their own ML from scratch, which meant each vertical application required years of R&D before producing a useful product. Today’s foundation models provide a massive head start — the reasoning, vision, and language capabilities come pre-built, and the application layer can focus on domain integration rather than reinventing perception. That doesn’t eliminate the sales cycle problem or the data problem. But it compresses the R&D timeline from “five years to first value” to “six months to first value,” and that changes the venture math.
The second structural shift is orchestration infrastructure — multi-agent systems that can ingest unstructured data, navigate undefined workflows, and produce useful output even when the deployment target has no formal process to automate. Previous generations of enterprise AI required clean data, well-defined processes, and months of integration before delivering any value. The new orchestration layer inverts this: you can drop an AI system into a company that doesn’t even know how it’s deploying AI, and it works — not because the model is smarter, but because the scaffolding around it can handle the mess. That changes the addressable market from “companies with clean data and defined workflows” to “everyone,” and it changes the integration timeline from months to days.
Sequoia’s framework is useful here. They argue that the real world requires “domain-specific cognitive architectures” — AI systems purpose-built for specific, complex workflows. The foundation models are commodity infrastructure. The value is in the application layer. And the hardest, most defensible application layers are the ones that touch physical reality, because they require not just code but operational knowledge, regulatory compliance, and domain-specific data that can’t be scraped from the internet.
But you have to disaggregate. Lumping manufacturing, biotech, defense, and energy into one “atoms” bucket is the kind of lazy pattern-matching the essay’s first section warns against. These sectors have radically different market structures:
Defense and aerospace have genuine winner-take-all dynamics — classified clearances, program lock-in, multi-year contracts, and switching costs measured in decades. Once you’re on a platform, you’re on it. Anduril is the obvious example of venture economics working here, because the market structure rewards scale and lock-in.
Manufacturing technology mostly lacks network effects. Each customer deployment is bespoke. The winners will be companies that crack horizontal applicability across shop types — but that’s been the grail for two decades and no one’s found it yet. The venture-favorable play is software-layer manufacturing (AI that makes existing machines smarter) rather than vertically-integrated factories, because the former has software-like margins applied to an atoms problem.
Energy and climate have regulatory moats and massive capital requirements. These are project-finance businesses dressed up as startups. Some will produce huge outcomes; most require patient capital structures that don’t fit a 10-year fund.
Biotech has always been its own animal — binary outcomes, regulatory gates, and a well-established (if brutal) venture model. AI is genuinely accelerating drug discovery timelines, but the FDA approval bottleneck hasn’t changed.
The point: “go invest in atoms” isn’t an investment thesis. It’s a sector rotation. An investment thesis would say which specific market structures within the atoms world produce venture-favorable dynamics, and why AI changes the historical math.
The Geography Question
There’s a geographic angle here that most American VCs can’t see clearly, for structural reasons.
The U.S. venture ecosystem is overwhelmingly concentrated in software. This made sense for decades — America’s comparative advantage was enormous, the returns were spectacular, and the Stanford-to-Sand-Hill pipeline was the most efficient capital deployment machine ever built for that asset class. But if software is commoditizing, that concentration becomes a vulnerability. A hammer that spent 30 years looking at a world of nails, and the nails are dissolving.
Meanwhile, Europe sits on industrial infrastructure the U.S. venture ecosystem has systematically undervalued: Germany’s Mittelstand, the UK’s advanced materials and aerospace clusters, the broader European deep-tech research base. The thesis writes itself — these assets become more valuable as software becomes less scarce. But two inconvenient facts complicate the narrative. First, the Mittelstand is famously family-owned and VC-hostile; you can’t just deploy venture capital into it. Second, European deep-tech venture returns have historically been poor, and not only because they missed the software boom — European markets are fragmented, exits are harder, and the buyer ecosystem is thinner. Whether that changes in a regime where physical-world expertise commands premium valuations is an open question, not a foregone conclusion.
The Honest Answer
So: WTF should VCs do now?
The essay has walked through the evidence, and the evidence points somewhere specific. Not to “software is dead” — that’s the panicked version. And not to “everything’s fine” — that’s the denial version. It points to a structural repricing of where venture-scale value lives.
Software isn’t dying. It’s becoming air. Everywhere, essential, and not something you can build a toll booth on. The volume of software will explode; the number of venture-backable software businesses will contract. That’s not a cycle. It’s a phase transition. And the VCs treating it as a cycle — “fears are only temporary,” as most VCs told TechCrunch — are the ones the Kauffman study will cite in its next edition.
The terminal value of software is being questioned because the scarcity that justified those valuations is evaporating. What remains scarce — what becomes scarce as AI makes knowledge work abundant — is physical capability, regulatory position, and domain expertise that takes years to accumulate and can’t be replicated by a foundation model with an API key.
This means capital should flow to AI applied to physical systems, but not indiscriminately. The graveyard of Bright Machines and Rethink Robotics is a warning: atoms businesses fail when they ignore market structure, sales cycle reality, and the margin profiles that make venture math work. The companies that will capture this transition are the ones that crack a specific problem: bringing software-like margins to atoms-world problems, using AI to compress the timeline from “decade of R&D” to “year of integration.” Defense companies with platform lock-in. Manufacturing software that makes existing infrastructure intelligent rather than trying to replace it. Biotech where AI genuinely shortens the discovery cycle inside a regulatory structure that creates natural moats.
The time horizon matters enormously. If Hassabis is right about a decade-plus, and Sinofsky’s historical parallels suggest even longer, then this transition is an investing opportunity, not a reason to freeze. The best returns in any regime change go to the people who reallocate early — before the consensus reprices the opportunity away. But “early” in a 15-year transition means an LP committing to a 10-year fund focused on atoms may not see returns in-life. The fund structure itself may need to change.
And the herd? The herd is doing what it always does. FOBO — fear of becoming obsolete — is driving a huge amount of current VC behavior. Firms are piling into AI deals not because the analysis supports it but because not being in AI feels existentially dangerous. That’s not investing. That’s panic buying. And panic buying — whether it’s toilet paper in 2020 or AI startups in 2026 — never generates good returns.
Here’s what would prove this thesis wrong: if per-seat SaaS pricing stabilizes because AI augments workers rather than replacing them — if headcount doesn’t actually shrink, just output per head grows. If foundation model commoditization reverses and one player achieves durable monopoly pricing. If it turns out that AI makes designing and manufacturing physical systems just as cheap as it makes software, eliminating the scarcity advantage of atoms. Any of those would break the logic chain. They’re not impossible. They’re just not what the data currently shows.
The firms that will be on the right side of the power law are the ones reallocating now — not to “atoms” as a vague category, but to specific sectors with venture-favorable dynamics where AI changes the historical math. The ones still chasing the last wave will be in the next Kauffman dataset too.
Just on the other side.