The Best of 2025 (So Far) with Sarah Guo and Elad Gil
2025: The Year AI Found Its Feet
"We've spent the year talking to the architects of the future—from Harvey to OpenAI. But before we look forward, we have to look at the moments that captured the magic of leaning into new capabilities at exactly the right time."
The r/legaladvice Experiment
Winston Weinberg and Gabe met years ago, but the spark for Harvey wasn't a business plan—it was a Reddit thread. They took GPT-3 (back when it was public but ignored) and pointed it at the chaos of r/legaladvice. They focused on landlord-tenant disputes, used primitive "chain of thought" prompting, and then did something daring: they didn't tell the lawyers it was AI.
Lawyer Approval Rating (%)
Source: Harvey internal test with 100 cases
"We cold-emailed the GC of OpenAI. His response: Oh, I had no idea the models were this good at legal."
Spatial intelligence isn't just a "feature"—it's the hardest problem evolution ever solved.
Dr. Fei-Fei Li argues that animals evolved eyes primarily to reconstruct a three-dimensional world in their minds. It's the foundation of navigation, interaction, and manipulation. For humans, we are the masters of manipulation, yet we still struggle with the generative side of it.
"If I ask you to close your eyes right now and build a 3D model of the environment around you... it's not that easy. We don't have that much capability to generate extremely complicated 3D models until we get trained."
Imagine a world where AI gives us this capability at our fingertips—fluid interactivity, editability, and the power to manifest the physical world digitally. It's not just a tool; it's a whole different world for humanity.
The Displacement Debate
I think displacement in a lot of roles is going to happen very quickly. It’s going to be painful and a large political problem. We’re going to see a big populist movement around this.
What happens eventually? X percent of white-collar work is gone. What do those people do?
They move to the physical world. Robotics data creation, waiters, therapists—places where people crave human interaction. Automation in the physical world is going to happen much slower than the digital world. The virtual world has self-reinforcing gains; the physical world doesn't.
Coming up next
Dan Hendrycks on the Geopolitics of Superintelligence
The New Nuclear Deterrent
Dan Hendrycks on why the race for Superintelligence mirrors the Cold War—and how cyber-preemption becomes the new first strike.
"We’ve moved from discussing how AI replaces a coder to how AI replaces a nation's sovereignty. The shift from spatial intelligence to global dominance is happening faster than our treaties can keep up."
The Shared Vulnerability Doctrine
Think back to nuclear strategy. States didn't abstain from a first strike out of kindness; they did it because of shared vulnerability. If you wipe me out, I still have enough in the silo to end you. We are approaching a similar inflection point with AI.
When a nation is on the verge of automating all AI research—essentially creating a bootstrap loop where AI builds faster AI—that is viewed as a "pivotal" moment. It's a super weapon. And the response won't just be a stern letter from the UN.
"States will try to deter each other from trying to develop a super-weapon that would allow other countries to be crushed."
It becomes destabilizing. China sees the U.S. nearing a breakthrough and launches a preemptive cyber attack on the data center. Russia, looking at the U.S.-China race, decides to keep tabs via high-level espionage because software engineering is no longer just "tech"—it's the foundation of state power.
Noubar Afeyan: No More "Shots on Goal"
"I started a company in 1987 when 24-year-old immigrants didn't start companies... only IBM executives were trusted with $23M."
The Critique
Why is entrepreneurship treated as "random, idiosyncratic, and emotional"?
The "Gamey" Fallacy
We treat healthcare, climate, and food security like a game of chance. "Oh, we tried 20 things and one worked." As an engineer, that's a put-off. We need to make entrepreneurship a scientific profession, not a gamble.
What do you mean by 'gamey'?
It’s the idea that it’s supposed to fail most of the time. We deploy hard-earned money to do the 'damn near impossible.' We can't just call it 'shots on goal.' We have to get better at the process itself.
The Test-Time Scaling Revolution
Brandon McKinzie and Eric Mitchell from OpenAI are seeing a "magic" shift. It’s no longer just about the size of the model (pre-training); it’s about how much the model thinks before it speaks (test-time).
Visual Reasoning Scaling Slope
The slope becomes significantly deeper once the model allocates compute to specific tools and visual cropping.
Visual Self-Awareness
The model can now estimate its own uncertainty. It will tell you: "I can't see the thing you're talking about very well." Instead of hallucinating, it manipulates the image or crops a section to see better.
Deferring to Tools
Why have an LLM try to "crank through" a complex valuation model in its context? It's more efficient to have it write the code to do it the right way. We are moving from LLMs as 'know-it-alls' to LLMs as 'expert coordinators.'
As these models learn to delegate tasks to tools, the next challenge is training them for deep, autonomous research. Coming up: Isa Fulford on the frontiers of Deep Research.
The Moment It Clicks
Beyond the theoretical breakthroughs of reasoning models, there's the visceral reality of the training floor. Isa Fulford on the "taste" and "grind" of bringing deep research to life.
"It really was one of those things where we thought training on browsing tasks would work... But seeing it actually working and playing with the model was pretty incredible. Honestly, just that it worked so well was surprising, even though we thought it would."
"It’s the visceral experience of, oh, the path is paved with strawberries."
Innovating in the Graveyard
At Conviction, we look for how technology makes "bad" markets suddenly good. Arvind Jain built Glean in enterprise search—a category everyone else had left for dead.
The SaaS Unlock
In the pre-SaaS world, search was impossible. You couldn't find the servers, let alone the data. But SaaS changed the physics: systems became open, versionless, and interoperable via APIs.
"The biggest problem was actually solved: I could easily bring all the enterprise information into one place."
Scale Check
1,000,000,000
Documents inside a single modern enterprise customer today.
Data Explosion: Then vs. Now
When Arvind worked on Google Search in 2004, the entire internet was the size of one large company today.
"Mommy’s gonna be able to eat dinner with us every night now."
— A Doctor’s Husband, via Abridge
Shiv Rao distinguishes between the "dopamine hits" of hypergrowth—the sprints, the deals, the scale—and the oxytocin hits. The latter is what sustains a company through the "telephone pole journey" of building in healthcare.
Abridge isn't just optimizing notes; it’s returning time to people who have spent decades on the brink of retirement due to burnout. When a rural doctor realizes she can actually see her children because of an AI tool, the technology ceases to be an abstract reasoning model and becomes a fundamental human unlock.
These conversations remind us that we’re living through a hinge moment in history. As we wrap up this look at the builders leading the way, we see a common thread: whether it's the grind of model training or the resurrection of "dead" markets, the goal is to create space for what matters.
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Where the Code
Meets the Conscience
We’ve journeyed from Arvind Jain’s vision of an enterprise that finally "remembers" everything, to Dr. Shiv Rao’s mission to put the "human" back in healthcare. It leaves us with one question: If AI solves our efficiency crisis, what do we do with the time we get back?
"The ultimate ROI of AI isn't a percentage point on a spreadsheet; it’s the restoration of human presence in the moments that actually matter."
— Closing Reflection
The "Search" for Meaning
Arvind’s work at Glean proves that we aren't suffering from a lack of information, but a poverty of attention. By automating the "find," we unlock the "create." The enterprise of the future isn't a database; it's a living organism.
The Shiv Rao Take
"If the doctor is looking at a screen, they aren't looking at the patient. AI fixes the gaze. It’s that simple, and that profound."
The Friction Metric
85%
Reduction in 'keyboard-fatigue' predicted for high-output professionals by 2026.
We are moving from a world of "Tools we use" to "Partners we trust." That transition is messy, it's loud, but it's finally here.
It’s easy to get lost in the LLM hype—the benchmarks, the token counts, the valuation frenzies. But after speaking with Arvind and Shiv, the noise settles. What remains is a very clear picture of augmentation over replacement.
Arvind is building the "Internal Brain" for the global workforce, ensuring that no genius idea is ever lost to a silo again. Shiv is reclaiming the "Sacred Space" of the clinic, ensuring that a terminal diagnosis or a birth isn't recorded by a clicking keyboard, but witnessed by a pair of human eyes.
"Thanks for listening to the shift. The next chapter is yours to write."
The Road Ahead
Integration. AI moves from a separate tab to the background of every application.
Agency. Systems begin taking multi-step actions, not just answering prompts.
Invisibility. We stop talking about "AI" and start talking about "Work" again.