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No Priors Ep. 144 | The 2026 AI Forecast with Sarah & Elad

Pundits are screaming about the so-called “AI bubble.” But historically slow-to-adopt industries like medicine and law are actually embracing AI at an unprecedented speed. Sarah Guo and Elad Gil look ahead to 2026, breaking down the major trends that will define the next era of AI technologies. They explore the future of AI foundational models, predicting breakthroughs in solving complex scientific problems. They share competing views on the timeline for robotics and self-driving cars, debating whether startups have a chance for survival or if incumbents will dominate. Elad and Sarah also discuss the return of tech IPOs and M&As, forecast a new wave of AI consumer agent software, and explore why consumer product innovation has been slower than expected. Finally, the two offer bold non-AI predictions for the new year, including the acceleration of defense tech startups and the second-order underrated impacts of GLP-1 drugs on biohacking. Plus, stick around to hear predictions on what’s next for AI in 2026 from some of tech’s biggest names and industry leaders. We hear from Jensen Huang (Founder/CEO NVIDIA), Arvind Jain (Founder/CEO, Glean), Winston Weinberg (Founder/CEO, Harvey), Scott Wu (Founder/CEO, Cognition), Raiza Martin (Founder/CEO Huxe), Zach Ziegler (Founder/CTO, Open Evidence), Aaron Levie (Founder/CEO, Box), Misha Laskin (Founder/CEO, ReflectionAI), Noam Brown (Research Scientist, OpenAI), Joshua Meier (Founder/CEO Chai Discovery), Bryan Johnson (Living Man, Don't Die), Sholto Douglas (Member of the Technical Staff, Anthropic), Ben & Asher Spector (Stanford PhDs) and Dylan Patel (Founder/CEO SemiAnalysis). Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil Chapters: 00:00 – Introduction 02:43 – AI Predictions for 2026 04:40 – Adoption of AI in Professional Fields 07:17 – Robotics and Self-Driving Cars 08:25 – Robotics: Incumbents vs. Startups 13:59 – Future of IPOs and M&A in AI 16:42 – Challenges in Consumer AI Innovation 21:08 – Funding of Neo Labs, RL Research 26:28 – Predictions for 2026 Beyond AI 26:44 – The Future of Defense and Technology 28:23 – Biohacking and Peptide Therapies 30:37 – 2026 Prediction from AI Industry Leaders 40:46 – Conclusion

Sarah GuohostElad GilhostGuestguest
Dec 18, 202540mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

AI’s 2026 Outlook: Agents, Robotics, New Labs, and Real-World Impact

  1. Sarah and Elad look ahead to 2026, arguing that despite market volatility and hype cycles, AI’s real economic and professional impact is only in the early innings. They highlight rapid enterprise adoption in conservative fields like medicine and law, consolidation in key verticals, and an impending wave of robotics and self‑driving deployments that will test current hype. The conversation explores foundation model competition, “neo labs,” alternative architectures, and evolutionary approaches to AI, alongside capital markets dynamics such as IPOs, M&A, and infrastructure constraints. A series of guest predictions round out the episode, focusing on reasoning, agents, context-rich products, energy efficiency, AI drug discovery, and the broader social and political consequences of AI’s rise.

IDEAS WORTH REMEMBERING

5 ideas

AI’s real impact is rising even as hype and skepticism oscillate.

The hosts argue that reports downplaying AI’s productivity gains miss the long diffusion curve; adoption in areas like coding, medical documentation, and legal work is already substantial and will compound over the decade regardless of short-term sentiment swings.

Professional services and conservative sectors are becoming leading AI adopters.

Doctors, lawyers, compliance, and accounting—historically slow to adopt tech—are embracing AI for documentation and decision support, suggesting that reasoning over unstructured data maps particularly well to high-value expert workflows.

Robotics and self-driving will face a reality check before unlocking major value.

Humanoid and semi-humanoid robots are expected to see small-scale deployments that won’t fully match the hype, creating a sentiment pullback similar to early self-driving; long term, self-driving and industrial robotics should still become highly consequential markets, likely with strong roles for incumbents like Tesla, Waymo, and Chinese firms.

Foundation model competition will hinge on capital, architectures, and evolution-like methods.

While scale and capital advantage push the field toward a few dominant labs, the hosts see room for alternative architectures (diffusion, SSMs, continual learning) and evolutionary or self-improvement approaches that more closely resemble specialized biological systems and may unlock new performance leaps.

Consumer AI is underdeveloped, leaving room for breakout agentic products.

Apart from chat-style interfaces, there are few truly novel consumer AI experiences; the hosts expect new agentic, context-aware products and hardware experiments (many of which may fail) and note that only a relatively small pool of truly elite consumer PMs may be capable of inventing enduring new interfaces before incumbents copy them.

WORDS WORTH SAVING

5 quotes

The reality of the technology will always take ten years to propagate, and people are getting enormous value out of AI already and they're gonna get way more out of it in the future.

Elad

All the people who always never adopt technology are now adopting this stuff fast… physicians, lawyers, certain accounting types, compliance. I do think that's really notable and very under-discussed.

Elad

There'll be a couple of anecdotal one-offs in science that will make people say, 'Look, science is solved,' and they'll realize science isn't solved, and then later science will be solved.

Elad

I always thought that eventually you end up with evolutionary systems as really how you build AI… where you just evolve these systems with some utility function they’re evolving against.

Elad

I did not believe that we were gonna see that many unique consumer experiences besides ChatGPT… but I am seeing magical experiences of really different consumer agent software that I actually want and will use.

Sarah

AI hype cycles, sentiment, and secular adoption trendsVerticalization and consolidation of AI applications in professions (coding, medicine, law, customer support)Future of robotics and self-driving, and incumbents vs. startupsFoundation models, neo labs, alternative architectures, and evolutionary/self-improving AICapital markets: IPOs, M&A, infrastructure CapEx, and NVIDIA dependenceEmerging consumer AI experiences, agents, and context‑rich interfacesAdjacent fronts: defense tech, biotech/GLP‑1s, energy, and political/regulatory shifts around AI

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