86 Out of 100: The Clips No Priors Could Not Leave on the Cutting-Room Floor

Sarah Guo and Elad Gil, the investor hosts of No Priors, open their 2025 clip reel with a moment of timing. Winston Weinberg, later CEO of the legal AI company Harvey, was not planning to start a company. His future co-founder Gabe showed him GPT-3 when the model was already public. Weinberg’s shock was specific: something this strong, and nobody was talking about it or using it. He showed Gabe his legal workflows. The aha was immediate.

They went to r/legaladvice, a subreddit where almost every thread ends with some version of “so who do I sue?” They pulled about a hundred landlord-tenant questions, wrote chain-of-thought prompts before that phrase was fashionable, and generated answers. Then they handed the packets to three practicing landlord-tenant lawyers and said nothing about AI. The brief was simple: here is a potential client’s question and an answer; would you send this, unedited, to the client? Is it ethical? Is it good enough?

Eighty-six out of a hundred came back yes. They cold-emailed the results to OpenAI’s general counsel, whose reply was that he had no idea the models were this good at legal work. A couple of weeks later two unknown founders were in a room with OpenAI’s senior executives. Guo and Gil use the clip as the year’s thesis in miniature: lean into a new capability before the rest of the market has language for it.

The reel then leaves language for the physical world. Fei-Fei Li, speaking as a neuroscientist as much as a computer scientist, calls spatial intelligence one of the hard problems evolution had to solve for animals. A creature must grow organs that collect light — eyes — then reconstruct a three-dimensional world in the brain so it can navigate, act, and interact. Humans are Earth’s most capable manipulators, and manipulation sits on that spatial stack. For Li, that is the root of intelligence, and it is why robotics has never had its GPT moment.

She is careful to say animals have solved spatial intelligence only in part. Close your eyes and try to build a 3D model of the room around you: most people cannot. Architects and designers can, after years of training and some talent. Imagine doing that at your fingertips, with fluid interactivity and editability. “That would just be a whole different world for people,” she says, no pun intended. The bet behind World Labs is that language models solved talking; spatial intelligence has to solve doing — robots, AR, design tools, all stuck on the same missing layer.

Mercor supplies experts and training data to major AI labs, so CEO Brendan Foody spends his days watching the ceiling of model capability. His forecast for the labor market has no comfort in it. Displacement in a lot of roles, he says, is going to happen very quickly, it is going to be very painful, and it will become a large political problem. He expects a populist movement around it. The economic question he cannot drop is what people now in customer support or recruiting should be doing in a few years, and how wealth gets reallocated if the gains of superintelligence follow a power-law distribution.

Asked where displaced knowledge workers go, he points to the physical world and to niche skills. Physical world, in his mouth, is a wide bucket: people creating robotics data, waiters, therapists, anyone in a job that still needs human presence. The logic is a speed difference. Digital systems have a self-reinforcing loop — models help write the next model — so capability compounds. Physical automation does not get that loop for free; each robotics generation costs metal, time, and data collected in the real world. That gap, not a moral preference for in-person work, is his map of the next few years of employment.

Dan Hendrycks, director of the Center for AI Safety, reaches for Cold War nuclear strategy. States deterred first strikes because they shared a vulnerability: an aggressive bid to wipe the other side out would bring retaliation. He transposes that logic onto AI once the technology is viewed as pivotal to a nation’s future, and once a lab is on the verge of automating almost all AI research. States, he thinks, will try to deter one another from turning that lead into a superweapon, or from kicking off a rapid automated R&D loop that bootstraps current systems into something vastly more capable than anything else on earth.

Later the situation becomes unstable enough that China might attempt a preemptive cyberattack on U.S. data centers, and the United States might do the same to China. Russia, coming out of Ukraine, reassesses, sees the U.S. and China far ahead on AI, and finds that espionage against those projects is comparatively easy. Its line becomes: if you try to crush us with this, we will hit your compute. Hendrycks even offers a clock. The logic starts to run “later in the year when a big chunk of software engineering is starting to be impacted by AI.” Data centers stand in for missile silos; network attacks stand in for a first strike.

Noubar Afeyan has spent three decades on how biotech actually gets built. He is founder and CEO of Flagship Pioneering and a co-founder of Moderna. In 1987, he says, 24-year-old immigrants did not start companies in the United States. Large checks went to former Merck or IBM senior executives, on the order of two or three million dollars a round. He started a company out of graduate school anyway, raised real venture money, and grew increasingly irritated by the folklore around founding: that the process is supposed to be random, improvisational, idiosyncratic, almost emotional — “gamy.”

Guo asks what he means by gamy. He means the default that most projects fail, you win once in a while, you celebrate, you keep score of winners and losers. Playfulness is fine; over-seriousness misses things. All-play is not. “We take hard-earned money. We deploy it to do things that are damn near impossible.” Once in a while those bets become not only possible but valuable. Treating healthcare, climate, agriculture, and food security as shots on goal, then shrugging when nineteen of twenty miss, is a refusal to get better. Flagship’s internal method is to turn trial-and-error into a process with hypotheses and tests. mRNA was one proof. Afeyan thinks AI now lets every industry run that method again.

Guo frames reasoning as the biggest architectural paradigm shift in AI since the transformer. OpenAI’s Brandon McKinzie and Eric Mitchell make that claim concrete on vision. Give a reasoning model a question about an image and it will often admit, in the trace, that it cannot really see the thing you named. It estimates its own uncertainty. The magic is the next move. With a tool, it decides to crop, zoom, or otherwise manipulate the image and look again. The same tokens become more productive. McKinzie says the test-time scaling slope without tools and with tools, for visual reasoning specifically, is “very noticeably different.” The tool line is much steeper.

Mitchell’s example is code. An LLM can grind through a valuation model in context, fitting coefficients by brute force and self-verification. Or it can write a short program that computes the answer the right way, the way you would reach for a calculator instead of multiplying 375 by 812 in your head. The principle is allocation: defer the steps where the model has no comparative advantage to a tool that does. Compute goes to the work only the model is good at.

Isa Fulford, who worked on training Deep Research, supplies the human texture. The team had real conviction that training on browsing tasks would work. The first time they trained a model on a new dataset with that algorithm and then played with it, the thing still shocked them. “Just that it worked so well was pretty surprising, even though we thought it would.” Failures are equally strange: the model does something clever, then makes a mistake that makes you want to shout stop. The steep capability curve and the ragged edge arrive together.

One of AI’s surprises, Guo says, and a core principle at her firm Conviction, is that the right technology can turn a bad market good. Enterprise search was a graveyard. Glean CEO Arvind Jain’s autopsy is about access, not ranking. Before SaaS, data lived in the customer’s own data center. You had to find the servers and storage and then try to connect. You could not ship a turnkey product. SaaS changed the topology: every customer runs the same version, systems are open and interoperable, APIs can pull the content. The biggest blocker was gone. Jain could finally unify enterprise information and search it.

Glean’s origin was more pedestrian. At Rubrik, his previous company, information was scattered across 300 SaaS systems. Nobody could find anything; pulse surveys were full of complaints. Jain ran IT, tried to buy a search product, and found there was nothing to buy, so he wrote one. Scale is the other unlock. One of Glean’s largest customers has more than a billion documents inside the company. When Jain and Elad Gil worked on search at Google in 2004, the entire internet was about a billion documents. That internal explosion, plus cloud-era architecture, is why the market can finally support a standard product.

Healthcare is Guo’s other “right technology, right moment.” Abridge founder Shiv Rao, a cardiologist, turns doctor-patient conversations into notes. Physicians are unsparing: a missed medication name, a gastroenterologist who would never sequence problems that way in an assessment and plan because it makes him look terrible. Rao calls that feedback oxygen. The company also pipes praise into a Slack channel named Love Stories so anyone can remember why the hours matter. His favorite note is from a doctor at Tanner Health, a rural system. At dinner her son asked why she was not working. She showed him the phone and explained that a new tool lets mommy come home early. She teared up; her husband told the boy mommy would eat with them every night now. Hypergrowth supplies dopamine for sprints, Rao says. Oxytocin — purpose — is what holds a company.