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AI in 2026: 3 Predictions For What’s To Come (a16z Big Ideas)

AI is reshaping how discovery, connection, and business advantage are created. In part three of Big Ideas 2026, we explore three shifts defining where AI goes next when the stakes are real and the impact compounds. Oliver Hsu explains how advances in AI reasoning and robotics are moving science toward autonomous labs, accelerating discovery while making interpretability essential. Bryan Kim explores how consumer AI is evolving beyond productivity toward connection, identity, and helping people feel seen. David Haber breaks down why the most durable AI companies are those where AI reinforces the business model itself, driving revenue, outcomes, and compounding advantage. Timecodes: 0:00 Big Ideas for 2026 0:28 Autonomous Labs and AI in Scientific Discovery 3:55 Market Dynamics and Early Adopters in Autonomous Science 5:08 Public-Private Partnerships Accelerating AI-Driven Science 6:21 AI in Consumer Applications: From Productivity to Connectivity 7:08 AI and Human Connection: Startups vs. Incumbents 7:47 AI as a Relationship Facilitator 8:39 Personalization and the Future of Consumer AI 9:31 AI Reinforcing Business Models 10:05 Case Study: AI in Plaintiff Law and Lending 11:26 Compounding Advantages and Proprietary Data 12:29 Smarter Outcomes and the Future of AI-Driven Platforms Resources: Follow Oliver on X: https://twitter.com/oyhsu Follow Bryan Kim on X: https://twitter.com/kirbyman01 Follow David Haber on X: https://twitter.com/dhaber Read more all of our 2026 Big Ideas Part 1: https://a16z.com/newsletter/big-ideas-2026-part-1 Part 2: https://a16z.com/newsletter/big-ideas-2026-part-2 Part 3: https://a16z.com/newsletter/big-ideas-2026-part-3 Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see http://a16z.com/disclosures.

ETErik TorenberghostOHOliver HsuguestBKBryan KimguestDHDavid Haberguest

Dec 31, 2025·12m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   Big Ideas for 2026: Autonomous science, connected consumer AI, and AI-reinforced business models

   The episode tees up three 2026 predictions from a16z partners. It frames AI’s next phase as (1) accelerating discovery via autonomous labs, (2) shifting consumer AI from productivity to human connection, and (3) creating durable winners where AI strengthens revenue-driving business models.

2. 2

   From lab automation to “autonomous labs”: combining AI reasoning with robotics

   Oliver Hsu explains what’s new about today’s “autonomous labs” versus traditional lab automation. The breakthrough is pairing AI reasoning/experiment planning with robotic execution to create a tighter human–AI–robot collaboration loop in real lab environments.

3. 3

   Interpretability and audit trails as prerequisites for AI-driven research

   Hsu argues that scientific settings demand more transparency than many general AI applications. Because AI systems can be non-deterministic, researchers need clear records of why the system chose specific experiment steps and how results informed subsequent iterations.

4. 4

   The destination: closed-loop, self-driving science (and why it’s still farther out)

   The long-term vision is fully autonomous, closed-loop science: AI generates hypotheses, runs experiments, ingests results, and iterates without human intervention. Hsu positions this as a destination that depends on uneven progress across multiple capability areas.

5. 5

   Where adoption happens first: market maturity and clear buyers of research outputs

   Hsu predicts early adoption will correlate with domains that have established demand for research outputs and can pay for speed/cost advantages. Mature markets like pharma, chemicals, and parts of materials science have clearer ROI pathways for autonomous lab systems.

6. 6

   Startup landscape and examples: building blocks of autonomous science

   Hsu highlights early companies pursuing pieces of the autonomous science stack, from life sciences to chemistry-focused efforts. These startups represent different wedges into autonomous discovery: automation, AI “scientist” software, and verticalized lab workflows.

7. 7

   Public–private partnerships accelerating AI-driven scientific discovery

   Beyond startups, Hsu points to government, academia, and industry collaborations as key accelerants. These partnerships aggregate resources, data, and infrastructure—potentially speeding translation of AI advances into real scientific progress.

8. 8

   Consumer AI in 2026: the shift from productivity tools to connectivity tools

   Bryan Kim predicts consumer AI will move from helping users “do work” to helping them “stay connected.” The emphasis becomes emotional utility—helping people feel understood, maintain relationships, and spend time in new AI-native interaction models.

9. 9

   Startups vs incumbents: winning via new interaction models and creative primitives

   Kim argues that incumbents’ network effects aren’t insurmountable if AI enables fundamentally new interaction patterns. If the “atomic units” of sharing and creativity change, startups can win by building AI-native behaviors that don’t fit existing platforms well.

10. 10

    AI as a relationship facilitator: “my AI talks to your AI”

    Kim describes a future where people share more of their inner life with AI and allow agents to coordinate socially. AI-to-AI communication could prompt check-ins, open difficult conversations, and create relationship moments that wouldn’t happen otherwise.

11. 11

    Personalization through digital footprint ingestion (and the tradeoff)

    To facilitate connection, Kim suggests AI must understand users quickly without requiring exhaustive manual setup. He points to ingesting digital footprints—messages, online activity, photos—as a path to rapid personalization, implying new expectations around permissioning and trust.

12. 12

    AI that reinforces business models: adoption is strongest when revenue grows, not just costs fall

    David Haber argues the most powerful AI application companies will be those that strengthen customers’ business models—especially by increasing revenue or outcomes—rather than only automating labor. When AI directly improves earnings, customer demand can be uncapped.

13. 13

    Case studies: plaintiff law (Eve) and loan servicing voice agents (Salient)

    Haber details two portfolio examples where AI improves customer outcomes. In plaintiff law, AI helps contingency-based firms take more cases and win more; in lending, compliant multilingual voice agents reduce costs and improve collection rates.

14. 14

    Compounding advantage and defensibility: end-to-end workflow + proprietary outcomes data

    Haber explains how AI apps build moats by embedding deeply in daily workflows and collecting proprietary data that improves decisions over time. For Eve, outcomes data from intake-to-resolution is private and enables smarter triage, valuation, and strategy—making the platform stronger with every case.