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

Summary

• AI adoption is outrunning the backlash, but public markets may still treat any Nvidia stumble as a referendum on the whole cycle. Elad expects recycled “AI isn’t working” and bubble claims even though technology waves take “like 10 years to propagate”; he says adoption already looks “blinding,” especially among doctors, lawyers, accountants, and compliance teams. Sarah locates the anxiety in investor positioning and says a weak Nvidia quarter could trigger panic despite having little to do with the underlying secular change. Elad’s answer to skeptics: “So fast I don’t even know what people are talking about.”
• The next application value should concentrate in vertical winners, while Sarah makes the episode’s most explicit trading forecast: somebody will earn “hundreds of millions of dollars” using LLMs in markets in 2026. Elad points to coding, medical scribing, and legal software—including Harvey—as categories already consolidating into a handful of players, and expects the next verticals to reach massive scale. Aaron Levie later argues that agents tied to workflows, organizational data, context engineering, and change management have the clearest enterprise path.
• Robotics is set up for a sentiment break before a business-model break: small-scale humanoid and semi-humanoid deployments will arrive, some will fail, and investors will overreact. Sarah expects bifurcation as projected timelines slip; Elad counters that self-driving took roughly 15–17 years and is only now truly working. Their sharper dispute is structural: Elad points to Waymo and Tesla as self-driving’s current winners and sees a likely Tesla-robot winner, while Sarah argues that capital, hardware, manufacturing, and supply-chain needs can favor incumbents but that locomotion does not solve manipulation, leaving real room for startups and possibly Chinese companies.
• A major AI-lab IPO could become a reflexive benchmark trade rather than a clean fundamental decision. Elad’s hedge-fund example is stark: retail wants a pure-play AI winner, managers fear “missing Nvidia,” and annual benchmarking can force them to buy “regardless” of their company view. Elad expects one successful deal—if not priced too aggressively—to unlock many followers and raise huge sums for labs, even as investors worry whether AI demand supports capex, how credit and pay-on-delivery obligations distribute risk, and how concentrated the market is in Nvidia.
• Elad had not expected many distinct AI consumer products beyond ChatGPT, but now sees “magical” agent experiences he wants to use; Sarah agrees, while Elad still expects most new hardware to fail. Sarah’s durability test is “escape velocity”: without a network effect or another defense, a lab or Google can copy the feature two or three years later and catch up through distribution. The opportunity remains open because incumbents scare founders and too many teams merely rebuild last-generation products with current-generation models.
• Foundation-model competition is reopening around science, alternative architectures, and self-improving systems, yet scale still pulls capital toward a handful of winners. Sarah frames Ilya’s “age of research” as a chance to test compute-efficient ideas, diffusion models, and SSMs rather than fight only a resource war. Elad predicts one or two physics, materials, or math wins will prompt premature claims that “science is solved,” while the long-run impact is understated; his speculative fast-liftoff path is “code plus self-evolution.”
• Capital will keep spilling into defense autonomy and peptide medicine, though defense budgets and the boundary between fringe and mainstream adoption remain unresolved. Sarah expects drone-based defense and startup activity to accelerate; Elad agrees on the need but warns that spending must actually move from prime contractors to new companies at scale. Elad calls GLP-1 adoption “inexorable” and still underrated, while Sarah points to second-order effects and greater interest in engineered peptides and hormone therapies. They discuss fringe biohacking as a possible early indicator, not settled proof.
• The closing forecasts converge on proactive agents and “intelligence per watt” as 2026’s product and infrastructure constraints. Contributors expect AI to gather context, use screens, stop waiting for prompts, and bring a “Claude Code experience” to broader knowledge work; enterprise gains require agent harnesses and economically useful evals. Ben and Ash Spectre argue power is the near-term data-center constraint, but chips matter more over time because they depreciate faster and, at roughly 10 cents per kilowatt-hour, cost about an order of magnitude more than power over five years.

Deep dive

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