No Priors Ep. 138 | The Best of 2025 (So Far) with Sarah Guo and Elad Gil
A recap of key conversations from the No Priors podcast in 2025, featuring insights from leaders at OpenAI, Harvey, and the Center for AI Safety on topics ranging from reasoning models and spatial intelligence to the geopolitical risks of superintelligence and the human impact of AI in healthcare.
The Spark of Innovation: Early Discoveries and New Markets
Unlocking Legal Workflows with Foundational Models
Winston Weinberg of Harvey shared the origin story of his company, which began with a surprising observation: in the early days of GPT-3, almost no one was using it. The pivotal moment came from an experiment using prompts on the r/legaladvice subreddit. They fed about a hundred landlord-tenant questions into GPT-3 using an early form of chain-of-thought prompting. The generated answers were then presented to three attorneys. Without telling them AI was involved, they were asked if they would send the answers to a client without edits. In 86 out of 100 cases, the answer was “yes.” This result was so powerful that it even surprised OpenAI’s own general counsel, who admitted he “had no idea the models were this good at legal,” leading to a meeting with OpenAI’s C-suite.
Revitalizing Enterprise Search with SaaS and Cloud
Arvind Jain of Glean discussed how he tackled enterprise search, a market widely considered a “graveyard” for startups. The failure of past companies was largely due to the inability to access siloed data in on-premise servers. The landscape, however, had changed due to two key technological shifts:
- The Rise of SaaS: The proliferation of SaaS applications with standardized APIs solved the data access problem. It became possible to build a turnkey product that could connect to hundreds of systems and aggregate enterprise knowledge in one place.
- Cloud Scalability: The sheer volume of enterprise data has exploded. Jain noted that one of Glean’s largest customers has over a billion internal documents, equivalent to the size of the entire public internet in 2004. The pre-cloud era lacked the infrastructure to build systems capable of handling this scale.
The idea for Glean originated from an internal problem at his previous startup, Rubrik, where employees struggled to find information across 300 different SaaS systems.
The Evolution of AI Capabilities
From Reasoning and Tool Use to Spatial Intelligence
Dr. Fei-Fei Li framed spatial intelligence as a fundamental and difficult problem that evolution has worked to solve. It involves reconstructing a 3D world from 2D inputs (light collected by eyes) to enable navigation, interaction, and manipulation. Even for humans, this is not a fully solved problem; most people cannot generate a complex 3D model of their surroundings from memory without significant training. AI has the potential to make this capability fluid and accessible, fundamentally changing human-computer interaction.
Expanding on model capabilities, Brandon McKinzie and Eric Mitchell from OpenAI explained that reasoning models that use tools represent a major paradigm shift. The key insight is enabling models to recognize their own limitations and allocate compute more efficiently.
- Efficient Problem-Solving: A model can acknowledge it can’t see an object in an image clearly and use a tool to crop or manipulate the image for a better view. This leads to a “much steeper” test-time scaling slope compared to models without tools.
- Comparative Advantage: For tasks like financial analysis, instead of attempting to perform calculations within its context window, a model can write and execute a simple program to get a verifiable and precise answer. This defers the task to a tool that is better suited for it.
The Visceral Experience of Research Breakthroughs
Isa Fulford of OpenAI provided a behind-the-scenes look at the R&D process. She described the “visceral experience” of seeing a model trained on a new dataset of browsing tasks work for the first time. Despite having strong conviction that the approach would be effective, seeing it in action was still “pretty incredible” and surprising. She also noted that the model’s failure modes can be equally surprising, where it can perform highly complex tasks and then make a simple, inexplicable mistake, highlighting that there is still significant room for improvement.
Societal and Geopolitical Implications
Workforce Displacement and Economic Realignment
Brendan Foody of Mercor offered a stark prediction about the impact of AI on the workforce. He believes the displacement of white-collar roles like customer support and recruiting will happen “very quickly and it’s going to be very painful,” likely leading to a significant populist political movement. He identified two of the most important economic problems to solve:
- Labor Reallocation: Determining the future roles for displaced workers. He predicts a shift toward jobs in the physical world (e.g., robotics data collection, skilled trades, in-person services like therapy) and niche industries, as physical automation will progress more slowly than digital automation.
- Wealth Redistribution: Addressing the power-law distribution of wealth and value generated by superintelligence.
The Geopolitics of Superintelligence
Dan Hendrycks from the Center for AI Safety discussed the geopolitical risks of advanced AI, drawing a parallel to nuclear strategy. In the nuclear era, the concept of “shared vulnerability” and mutually assured destruction acted as a deterrent against a first strike. A similar dynamic could emerge with AI. As AI becomes pivotal to national power, the situation could become highly destabilizing. Hendrycks outlined a scenario where one nation on the verge of creating a superintelligence—capable of automating AI research—could trigger a preemptive strike from a competitor. He warned, “China just says we’re going to do something preemptive like do a cyber attack on your data center and the US might do that to China.” Other nations like Russia would also become acutely aware of the stakes and could engage in espionage and cyberattacks to remain relevant.
Reshaping Industries: From Entrepreneurship to Healthcare
Making Entrepreneurship a Scientific Endeavor
Noubar Afeyan, founder of Flagship Pioneering and co-founder of Moderna, challenged the conventional view of entrepreneurship as a “random, improvisational, idiosyncratic, almost emotional, gamy” process. He advocates for transforming it into a systematic, scientific profession, especially in high-stakes fields like healthcare, climate, and agriculture. Instead of relying on a “shots on goal” mentality where most attempts fail, he argues for a more rigorous process to turn near-impossible ideas into valuable realities.
The Human Impact of AI in Healthcare
Dr. Shiv Rao of Abridge shared powerful stories about AI’s impact on physician burnout. The administrative burden of documentation is a primary driver of burnout, and AI tools that automate clinical note-taking are having a profound effect. He shared anecdotes that fuel his team:
- A doctor who, thanks to the technology, decided not to retire.
- A physician who wrote, “My son asked me mommy why aren’t you working right now? I literally took my phone out and explained to him that Abridge is a new tool that lets mommy come home early and eat dinner with her family.”
Dr. Rao distinguishes between the “dopamine hits” of hypergrowth and the “oxytocin hits” of purpose and fulfillment. He believes this deeper, mission-driven impact is what sustains the team, stating, “That’s I think what we’re really after in this company.”