Dataiku has been named a Leader for the 5th time in the 2026 Gartner® Magic Quadrant™ for AI Platforms for Data Science & ML. We believe scaling AI takes the right people, orchestration, & governance — the Formula for AI Success — to turn ambition into impact. More to come.
State of AI compute 2026: my conversation with @stephenbalaban of @LambdaAPI on the neocloud boom, data centers, GPUs and what's ahead
00:00 — Cold open
01:21 — Why GPU compute was never a commodity
02:45 — The H100 price index and what it gets wrong
04:02 — The real moat: technology or financing?
05:57 — Winner-take-all, or room for many neoclouds
06:48 — Are we overbuilding or underbuilding AI compute?
09:26 — What if AI gets 10x more compute-efficient?
10:44 — The real bottleneck: land, power, and shell
11:38 — The backlash against data centers — and the misinformation
15:00 — Opening the hood: from photons to tokens
17:11 — Extracting more value from the same chip
19:26 — Frontier inference and distributed training, explained
23:26 — What actually drives compute cost
25:21 — Lambda's chip stack and the NVIDIA relationship
26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat
28:59 — Networking, storage, and the one-click cluster
34:46 — Renting vs. owning, and full vertical integration
36:24 — How global is Lambda? Does location still matter?
38:44 — The financing stack: off-take agreements, SPVs, and credit
41:16 — Why a 2023 GPU leases for more today
42:36 — A futures market for compute?
43:54 — Origin story: facial recognition, Perceptio, and Apple
47:03 — The Lambda hat and Dream Scope
48:59 — The $60K bet that became a cloud business
52:00 — Holding the team together through the hard times
54:30 — Bringing on a new CEO; Stephen as CTO
57:33 — Matching xAI on high-velocity deployment
59:29 — "AI won't write software — it will become the software"
01:01:30 — Neural software vs. vibe coding
01:04:25 — Do agents change the compute layer
01:06:14 — Self-assembling software inside Lambda
01:08:18 — Gigawatt-scale AI factories
01:08:57 — One person, one GPU
01:12:04 — Hot takes: overrated and underrated in AI
Guilds Summit: Where the best share how they build.
➡️ 400+ senior executives
➡️ 30+ speakers
➡️ 4 stages
One full day of non-stop learning and connection.
Already pumped for 2027!
Partnered with @firstmarkcap on their Guilds Summit. Some takeaways👇🏼
@anjsud CEO @tubi x @amishjani
1. The best CEOs aren’t the experts on everything. They hire experts, give them ownership, expect it back and get out of the way.
2. “Never waste a good crisis”, that’s often when the biggest leaps happen.
@davidneckstein CFO @wearelegora x @abimbhet
1. The best fundraising rounds optimize for value add, not valuation (yes their investors are actually getting them customers).
2. AI is pushing CFOs out of spreadsheet management and deeper into driving revenue growth.
Li Fan CTO @circle x @amankabeer11
1. The opportunity is bigger than agentic payments, it’s the agentic economy (there’s an entire stack that involves things besides payments).
2. Winning won’t come from forcing every workflow to go headless but from finding where agents create enough value to earn the right to transact. (It’s still early and no one has cracked it yet).
Thank you @mrbenwinn for hosting and leading a phenomenal production. @airwallex is proud to facilitate bringing the best forward to share how they build.
Introducing Luma.
Built on $15B of transaction volume, Luma is the only AI-solution for consumer brand finance.
I've spent my career building brands and working with founders.
I'm excited for what Luma will be able to do for every D2C brand. She's going to be a game changer.
https://t.co/jbqXHormqx
Introducing Luma, a world-class finance team for D2C.
Most AI in finance focuses on tasks. Luma focuses on decisions.
Built on $15B of consumer brand transaction volume.
Proud of our team at Highbeam.
Check it out: https://t.co/qk5zZSrKlS
SurrealDB is #1 on the GDB-Engines Graph Database Movers list.
We’re building the database for the next generation of applications, where connected, multi-model data is the default.
Grateful to the community and team helping bring that future closer.
https://t.co/LGsj1gDU8T
Why AI Can Now Make Discoveries - my conversation with @danintheory, Lead of the Foundations of Reinforcement Learning team at @OpenAI
00:00 Intro: AI's wild week in mathematics
01:21 What OpenAI's Foundations of RL team does
03:08 Dan's journey: from black holes and quantum gravity to frontier AI
07:04 Are AI systems becoming useful for real science
08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic
08:52 Why the OpenAI result was an act of exploration
10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof
12:13 RL 101: learning by doing, not just watching
15:10 Why reinforcement learning works
15:58 How RL breaks: sparse feedback and long-horizon tasks
17:03 RLHF: how human feedback shaped early language models
18:48 Move 37, self-play, and the search for novel strategies
22:16 Explore vs. exploit in scientific discovery
24:49 Why RL may now be "the cake," not the cherry on top
25:46 Why RL started working with large language models
27:29 Is RL "sucking supervision through a straw"?
28:47 Why language may be the grounding layer for intelligence
31:46 A contrarian take on the Bitter Lesson
32:41 What test-time compute actually is
34:50 How RL gives models the ability to think
35:40 Verifiable rewards, math, coding, and the messy real world
38:00 What physics can teach us about AI
42:08 Is there a thermodynamics of AI?
43:08 From Erdős problems to Einstein-level AI
45:16 Is AI already doing original science?
45:51 How far are we from AI automating AI research
47:41 Why Dan is excited about the future of science
5 years in a row. 🏆
Dataiku has been named @Snowflake's 2026 Product Partner of the Year – AI Platform.
The reason? Helping enterprises move from AI pilots to governed, production-scale AI.
Thanks to Snowflake and our joint customers.
1/ Databases are entering a weird new era.
Not because of AI hype.
Because for the first time in decades, databases are being asked to behave less like storage engines… and more like memory systems.
Today we shipped SurrealDB 3.1.
And buried inside the release is something important: DiskANN.