Uploaded November 2025 | Updated September 2026, 3 weeks ago
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#AI #Science #Biology #Research #MachineLearning #BioTech #WetLab #ScienceCommunication #TechTalk #ArtificialIntelligence #FutureOfScience #ScienceExplained #STEM #Philanthropy #Collaboration #CZI #PriscillaChan #MarkZuckerberg



![[State of Research Funding] Beyond NSF, Slingshots, Open Frontiers — Andy Konwinski, Laude Institute
From co-founding *Databricks* and *Perplexity* to launching the *Laude Institute*—a dual venture fund and nonprofit designed to turbocharge the path from *research breakthrough to breakout company*—*Andy Konwinski* is building the infrastructure to recreate the Databricks motion at scale: fund researchers doing open work, help them ship products that matter, and turn paradigm-shifting ideas into trillion-dollar companies. We caught up with Andy live at *NeurIPS 2025* to dig into the origin story of Laud (right resource, right researcher, right time), why the *Databricks founding model* (eight co-founders, deep research scars, years of collaboration) is becoming the gold standard for AI startups (not an anomaly), how Laudes *venture arm* backs researchers-turned-founders with 50+ professors and PhDs as LPs (Jeff Dean, top Berkeley/Stanford faculty, Databricks and Perplexity co-founders), why the *nonprofit arm* does no-strings-attached grants to fund open research before incorporation (the upstream funnel that feeds the next generation of companies), the *slingshot program* funding breakthrough projects like *DSPy, Terminal Bench, LMArena, and continual learning research,* why *NSF isnt broken but insufficient* (its $1B/year for computer science when we need $10-100B, and Silicon Valleys picker model can deploy capital more effectively), how the *post-post-training layer* (prompt optimization, context management, RAG, memory curation, tool usage) is becoming the new frontier above pre-training and post-training, why *Chinese labs are outpublishing Western labs* in open research (Moonshot, DeepSeek shipping twice as many interesting papers as American startups because OpenAI and the frontier labs stopped publishing), the launch of *Open Frontiers*—a live-streamed conference in San Francisco bringing together the 100 most influential open researchers (Yann LeCun, François Chollet, Jan Leike, Percy Liang, Berkeley AI Research, Allen Institute, and more) to share roadmaps and unify the ecosystem, why the *Laude Lounge at NeurIPS* became the VIP gathering spot (Starlink WiFi, free food, couches, and the gods of AI hanging out because conferences need a place for the VVIPs to actually sit down), and his thesis that *open research is the path to world-changing impact*—and Laud is the bridge from grant to company, from paper to product, and from researcher to billionaire founder. We discuss:
* What *Laude Institute* does: dual structure with a *venture fund* (backing researchers-turned-founders post-incorporation) and a *nonprofit* (no-strings-attached grants for open research pre-incorporation)
* The *slingshot program:* funding *DSPy, Terminal Bench, LMArena, continual learning, and Jepa-style prompt optimization* projects across Berkeley, Stanford, MIT, CMU, Wisconsin, Caltech, UI Urbana-Champaign, Toronto, Waterloo, and beyond
* The *post-post-training layer:* compound systems, prompt optimization, context management, RAG, memory curation, tool usage—the layer above pre-training and post-training where innovation is exploding
* *GEPA and DSPy:* evolutionary prompt optimization (genetic algorithms reinvented by PhD student Laxia) and the DSPy framework (a reverse compiler that takes code and compiles natural language)
* Why *NSF isnt broken but insufficient:* $1B/year for computer science (and theyre trying to cut it in half) when we need $10-100B for frontier AI research—Laud complements NSF with Silicon Valleys picker model and high-velocity grant writing
* The *PhD entrepreneurship clubs:* Computer Science Grad Entrepreneurs (CSGE) at Berkeley (started 2012), Agent at University of Washington, Research to Impact at Wisconsin, Saplings at Stanford, and more clubs forming at CMU, MIT, UI Urbana-Champaign
* *Open Frontiers:* a live-streamed conference in San Francisco (next five months) bringing together the 100 most influential open researchers (Yann LeCun, François Chollet, Jan Leike, Percy Liang, Berkeley AI Research, Allen Institute, and more) to share roadmaps and unify the ecosystem
* The vision: *open research as the path to world-changing impact,* and Laud as the bridge from grant to company, from paper to product, and from researcher to trillion-dollar founder
— Andy Konwinski
* Laude Institute: https://www.laude.org/lounge
* X: https://x.com/andykonwinski
00:00:00 Introduction: Andy Konwinski and the Laud Institute Vision
00:01:17 The Databricks Motion: From PhD Research to Billion-Dollar Companies
00:02:15 Lauds Two-Sided Model: Venture Fund and Philanthropic Grants
00:06:37 Slingshot Program: Funding the Layer Above Foundation Models
00:07:56 JEPA and DSPy: Evolutionary Prompt Optimization
00:10:29 Beyond Berkeley and Stanford: Expanding the Research Network
00:13:22 NSF Complementarity: Not Broken, Just Insufficient
00:17:03 The Laud Lounge: Creating a VIP Experience at NeurIPS
00:18:41 Open Frontiers: Reclaiming Leadership in Open AI Research
00:19:06 The Open Research Crisis: Why [State of Research Funding] Beyond NSF, Slingshots, Open Frontiers — Andy Konwinski, Laude Institute](https://i.ytimg.com/vi/ZagdY6UJYL4/mqdefault.jpg)






![Scaling Past Informal AI - Carina Hong, Axiom Math
Carina Hong, founder and CEO of Axiom Math, joins the AI for Science podcast right after closing a $200M Series A to argue that the road to superintelligence runs through formal verification — not as a bug fix, but as the only way to compound and scale AI brilliance. Her company, seven months old and 30 people strong, scored a perfect 120/120 on the 2024 Putnam exam, beating the best human and every other AI system at the time. We dig into the Lean theorem prover, why verified generation gives better training signal than informal RL, the hard specification problem, and why Carina believes an informal system alone can never reach math AGI.
00:00 — [INTRO — spliced from final take at 01:47:28]
00:52 — The $200M Series A and the Math Startup Thesis
04:52 — Verified AI: Scaling Brilliance, Not Fixing Lousiness
13:42 — Axioms System: Lean Data, RL, and the Putnam Perfect Score
22:12 — Mathematical Discovery — Before the Conjecture
25:12 — Rices Theorem, Incompleteness, and Practical Limits
30:42 — Code With Proof — The Verina Benchmark
37:57 — Proof Trees, Context Windows, and Scaling Limits
43:57 — Markets, Moat, and the Business Case ($1.6B valuation)
55:27 — Personal Origin Story: Oxford, UCL Gatsby, Stanford Law
01:00:57 — The Erdos Controversy and the Difficulty of Search
01:06:02 — AlphaZero for Math, Self-Improvement
01:08:47 — Startup Advantage and the OpenAI GPTF Thread
01:13:17 — Axle API — Open Infrastructure for Lean at Scale
01:20:47 — Collaboration, Polymath, and Human Attention as the Bottleneck
01:22:21 — Founding Story — Obsession, Law School, and Julie Zhuo
01:26:17 — The Bigger Vision — AGI, Science, and Transfer Learning
01:35:02 — Bottlenecks, Fragmentation, and the Fields Future Scaling Past Informal AI - Carina Hong, Axiom Math](https://i.ytimg.com/vi/abYcV5LHMG4/mqdefault.jpg)