Ryan Sullivan retweeted
@eriktorenberg I see you and I raise you
Apply today 👇
Thrilled to announce our latest incubation: Horowitz Andreessen Academy.
Horowitz Andreessen Academy will be a two-year residential program in San Francisco for 16 to 22 year olds.
My biggest regret from my college experience is how divorced it was from the real world (both practically but also intellectually)
HAA is the opposite. Students will learn from real-world practitioners and intellectuals; including founders (Sam Altman, Brian Armstrong, Jensen Huang), investors (Elad Gil, Daniel Gross), and professors (Agnes Callard, Tyler Cowen), among hundreds of others. The Academy is the educational experience I wish I had.
In 1984, the educational psychologist Benjamin Bloom published a paper about how the average student who received one-on-one tutoring focused on mastery techniques performed better than 98% of the control group. For generations, this was viewed as unrealistic to implement even if it produced the best outcomes.
At the Academy, we will be able to locate any tutor on any topic that a student desires.
Take an aspiring filmmaker or producer. The standard path offers them film theory courses, peer workshops, and the occasional visiting filmmaker; everything past that takes meticulous networking and luck. The very entrepreneurial type will find ways to invite prominent speakers to speak and meticulous networking to reach the right people. At the Academy, they will learn the business from the people who run it and their next creative project will have notes from award-winning filmmakers.
Take a young founder. Yes, there is endless content online; you can watch a video on any skill. But content is not coaching. The Academy will provide dedicated, experienced coaches and mentors who know your company and psychology. Students can workshop issues with prominent leaders in technology who host sessions on campus. Every generation of entrepreneurs has had mentors in their corner. Ours will too.
Take the technical student focused on research. At the Academy, academics are focused on the frontier. Students will be taught and mentored by AI researchers at frontier labs. They’ll be conducting research and sharing progress, and presenting their work at ICML and ICLR.
The goal of the Academy is to identify the right mentors, the correct bottlenecks, and the right introductions, adjusted continuously around one person and their pursuits.
The education philosophy focuses on co-ops, courses, and pursuits.
Co-ops are the opportunity to work during the year (not just over the summer). It's the chance to work across countless different roles and companies, including getting connected to our 10 founding partners: Anduril, Anthropic, Coinbase, Google, Meta, NVIDIA, OpenAI, Palantir, Replit, and Stripe.
Courses are taught by legendary people: people who have founded unicorns, invested in those unicorn companies, or are on the intellectual frontier.
And most importantly, it's about Pursuits. It's about personal projects, quests, startup ideas, rabbit holes that define the student experience. The Academy will provide dedicated, experienced coaches and mentors who know your work and psychology.
At HAA, the individual education of each student is the whole design. We want to meet you. Applications for the founding class are open.
theacademysf.com
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Ryan Sullivan retweeted
Thrilled to host this @FirstMarkCap office in NYC, incredibly timely & important conversation as we barrel towards a future where the bulk of content we consume on the internet is AI-generated
Excited to have Max unpack how @pangram is working to solve this problem!!
For NYC folks: I'm talking about AI slop with the @nori_agentic crew, moderated by @AmanKabeer11 this Thursday. Register below!
luma.com/agentics-g4ka
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Ryan Sullivan retweeted
Huge congrats to @nicckopp and the fantastic team @RilletHQ on their $100M Series C! We’re grateful for the opportunity to partner on the journey ahead.
Accounting is a large, mission-critical function, yet many of the core ERP systems that power it were built for a different era. AI is fundamentally changing how finance teams operate and creating an opportunity to reimagine the ERP from the ground up, with agents performing complex work while finance teams retain visibility and control.
And Rillet is emerging as the clear leader in this new paradigm. 600+ customers, new ARR doubling last quarter, AI agent usage growing 70% each month, and enterprises moving off legacy platforms like Oracle, SAP and Workday.
The momentum is impressive, but we’re even more excited about the incredible team behind it. Nic and the Rillet team have a rare combination of ambition, product velocity and deep customer empathy.
Congrats to the entire team on a huge milestone. Excited for everything ahead!
cc: @amishjani @vverawang @FirstMarkCap
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Ryan Sullivan retweeted
🚨 Special Friday episode - this one couldn't wait.
OpenAI's model hacked @huggingface. As a side quest.
Co-founder and CSO @Thom_Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source.
00:00 An AI Agent Hacked Hugging Face
00:30 Introduction
01:00 17,000 Attacker Events, and a Strange Target
04:28 The Attack Was a “Side Quest”
06:13 AI Training Runs Left Notes for Each Other
07:09 Closed AI Refused to Help
09:47 Fighting Back With an Open-Source Model
13:15 Open vs. Closed Is the Wrong Safety Debate
15:46 AI Agents Start Social-Engineering Humans
22:24 The Three Walls: Sandboxes, Guardrails, Alignment
24:34 “Neuralese”: Can Humans Still Read AI Reasoning?
25:28 Why Monitoring AI Agents Gets So Hard
28:10 Reward Hacking and the “Paperclip Problem”
32:02 The State of Open-Source AI in 2026
33:47 Router Models and the Enterprise Shift to Open
37:01 The Real Economics of Open Models
39:41 Can Chinese AI Models Be Trusted?
41:37 AI Sovereignty: Who Controls the Switch?
43:16 Why Western Open-Source AI Matters
48:16 Is AI Heading Toward an Oligopoly?
49:41 The Race Toward Recursive Self-Improvement
51:54 Why Thomas Signed the AI Slowdown Letter
55:14 AI Slowdown - or Regulatory Capture?
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Ryan Sullivan retweeted
How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with @mitch_troy, co-founder of @trybasis
01:09 Why Everyone at Basis Was Whispering to AI when @steph_palazzolo walked in
04:12 Accounting as "an Intelligence Over the Economy"
06:11 What Makes an Agent Truly Long-Horizon
08:24 Inside an Autonomous, Multi-Day Tax Return
10:19 Agents That Hand Off Like Senior Engineers
11:17 A Brief History of Agents: From ReAct to Today
12:33 Why LLMs Have No Long-Term Memory
14:13 Why AutoGPT Didn't Live Up to Its Promise
15:51 The Three Breakthroughs: Opus 3, o1, o3
17:07 Why Reasoning Models Unlocked Agents
18:23 "Let's Verify Step by Step": The Road Not Taken
20:32 Pushing Back on the METR Chart
22:09 Why Coding Agents Won First
25:14 Why Real-World Agents Are Harder
26:55 How Accountants Verify Non-Deterministic Work
29:18 You Can't Scale Tax Returns Like Math
33:16 100 Evals Pass - So What?
35:53 Right Answer, Wrong Process
36:37 Behavior Specs, Explained
39:58 How Specific Should Behaviors Be?
42:18 Context Is Runtime Training Data
44:21 Who Judges the Judge?
46:45 The Move 37 Objection
50:02 The Magic Box Mental Model
52:41 "Nothing Has Changed Since o3"
54:56 Open-Sourcing Behavior Specs with @ankrgyl @braintrust
59:45 Ontologies: A World for Agents to Live In
01:04:20 Documentation as Codebase
01:06:33 Why the Founding Fathers Were Context Engineers
01:09:05 Onboarding 300 Brilliant Alien Employees
01:11:10 Self-Improving Agent Systems
01:12:50 The Context Mistake Agent Builders Make
01:14:29 RL on Behavior Adherence
01:17:01 Will the Bitter Lesson Swallow the Harness
01:18:46 "Technical Moats Are Not Real Moats"
01:21:03 Advice for AI Builders
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Ryan Sullivan retweeted
let the good times roll ~~
amazed by the talent spending the summer here. ty chelsea commons for driving an important nyc initiative!
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Ryan Sullivan retweeted
Palo Alto secured traffic at the application layer when business software moved to the web. CrowdStrike shipped a cloud-native sensor when work left the perimeter and the laptop became the frontline. Wiz built a graph-first CNAPP when cloud adoption accelerated. The largest companies in cyber get created during secular platform shifts.
Each one of those shifts changed how we work. This time, it's also changed who's doing the work: agents. To do so, they inherit the same permissions and access as the humans they act for, which makes them a highly privileged and capable threat vector operating at superhuman speed. @maximbarkogan and @gil_elbaz_ saw that coming before anyone, and founded @onyx_security to build the category defining control plane to govern and secure agents.
Security tools were built to watch deterministic software and human-led workflows. They can't govern a non-deterministic model chaining tool calls against inputs nobody vetted in advance. Onyx's bet is that only AI can: proprietary models sitting in the runtime path, inspecting every agentic action and blocking or redirecting it before it reaches a downstream system with warp speed and accuracy.
Excited to be backing Maxim, Gil, and the Onyx team in their $113M Series B, alongside our friends at @BessemerVP, @Cyberstarts1, @conviction, and @TCVTech.
cc @sullivan @amitkarp @saranormous @HZinshtein
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Ryan Sullivan retweeted
My conversation with @andrewdfeldman, CEO of @cerebras. We started from "what is a wafer?" and built up to why the entire chip industry is reorganizing around inference speed.
00:00 Cold open & Intro
01:31 Why speed became the AI bottleneck
02:32 Tokens per second per user, explained
03:16 AI’s broadband moment and the Netflix analogy
04:35 The AI chip landscape: GPUs, TPUs, Trainium, ASICs
06:36 What is an ASIC?
08:08 Nvidia, Groq, and the fast inference war
09:16 OpenAI, Broadcom, and specialized silicon
12:10 China, power, and sovereign AI infrastructure
15:05 Is the AI infrastructure boom a bubble?
18:56 The hidden bottlenecks: HBM, CoWoS, and 3nm
22:57 Why agents are creating CPU demand
25:36 Andrew’s path from SeaMicro to Cerebras
26:13 Why Cerebras bet on AI in 2016
31:14 SRAM vs. HBM: why inference is a memory problem
33:19 What wafer-scale computing actually means
34:28 The deep-tech “Everest” problem
36:07 The moment the first Cerebras system worked
36:49 Ringing the bell and surviving deep tech
39:08 How a giant chip handles failure
41:22 Why GPUs struggle with decode
42:17 Prefill vs. decode explained
44:01 The “100 HD movies” problem in AI inference
45:04 How fast inference changes RL and training 48:08 Reasoning models and why they cost more compute
50:08 Verification, guardrails, and small models checking big models
52:37 Multimodal AI and the path to video
53:51 Cerebras’ business model: hardware, cloud, API
55:14 OpenAI’s 750MW inference deal
55:36 Why data centers are measured in megawatts
58:01 AWS Trainium + Cerebras decode
59:29 Fast tokens as a cloud product
01:00:52 Is CUDA still a moat?
01:03:53 How TSMC helped Cerebras build the giant chip
01:07:41 Why nobody cared in 2020
01:08:15 Why chip supply chains are hard to diversify
01:09:54 Why today’s AI models will be the worst you ever use
01:10:38 What fast AI could do to SaaS
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Ryan Sullivan retweeted
Recently sat down with @oliveur at our Guilds Summit to dive into Datadog’s product discipline, why great companies keep “sampling the fabric of reality,” and how to avoid losing customer truth at scale.
Fantastic insights from one of the best CEOs building today!
piped.video/watch?v=w0RpSmye…
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Ryan Sullivan retweeted
Why AI Progress Suddenly Feels Real - my conversation with @yanndubs, who co-leads the Post-Training Frontiers team at @OpenAI
00:00 - Intro
01:30 - Why recent AI progress feels like a step function
04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5
07:33 - How OpenAI structures vertical and horizontal teams
09:49 - Improving model efficiency and test-time compute
12:32 - Yann's journey from Switzerland to OpenAI
15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards
18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute
20:09 - How reasoning models become more efficient
23:23 - Pre-training scaling and overcoming the data wall
27:03 - Multimodal data, synthetic data, and embodied AI
31:05 - Demystifying mid-training and post-training
37:21 - Does RL create new capabilities in AI?
38:53 - The challenges and frontier of scaling RL
43:09 - Is building AI models a craft or a strict science
48:21 - How AI models generalize across different domains
54:18 - How reinforcement learning cures AI hallucinations
56:04 - Negative generalization and conflicting instructions
58:05 - Can RL scale to law, medicine, and the broader economy?
1:00:19 - The evaluation bottleneck and Model as a Judge
1:04:21 - Continuous AI progress & continual learning
1:08:49 - Will foundation models eat the agent harness
1:11:23 - Why startups should focus on the last mile of AI
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Ryan Sullivan retweeted
Every agent needs its own computer: my conversation with @ivanburazin, CEO of @daytonaio, about sandboxes and the emerging agent stack.
00:00 Intro
02:13 What is an AI agent sandbox?
03:17 Security risks of running agents locally
05:17 Stateful vs. stateless hyperscalers
07:04 The history of cloud IDEs and the end of localhost
09:45 Do all AI agents need a sandbox?
12:26 Sandbox use cases: RL evals & background agents
14:10 Unpacking the emerging AI Agent Stack
16:20 The unsolved problem of agent memory and learning
19:37 Where sandboxes fit in the agent harness
21:35 OpenAI, Anthropic, and agent SDKs
23:06 Ivan's founder journey: From CodeAnywhere to Daytona
26:59 GTM strategies and building developer communities
33:48 Why customer support is your best GTM strategy
35:34 Leveraging Twitter during the AI super cycle
40:50 The technical anatomy of a sandbox
41:53 Why fast spin-up speeds maximize GPU efficiency
46:09 Firecracker, QEMU, and isolation primitives
49:58 Why sandbox snapshots and state forking matter
51:40 Why Daytona built a custom scheduler from scratch
55:24 The challenge of long-running stateful sandboxes
58:10 The build your own sandbox trap
1:01:03 Why AI agents might trigger a global CPU shortage
1:02:46 The future of the AI Agent Stack
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Ryan Sullivan retweeted
The frameworks are broken. The playbooks don't work anymore. Company-building has fundamentally changed.
So how are the best operators building today?
That's what we're diving into on June 16 in NYC at the Guilds Summit.
400+ C-Suite and VPs in the room, exclusively from unicorn tech and top public tech companies.
11T+ in public and private value represented.
1 full day of company-building talks and quality time with peers.
@oliveur @anjsud @katieburkie @lilscotboy @jon_hyman @ElyKahn @EnoReyes @bretthuneycutt @gcmarker @ronieshac @davidneckstein @datadoghq @Tubi @WeAreLegora @try_headway @hungryroot @clay @airwallex @Braze @glean @okta @FactoryAI @Wealthsimple @brexHQ @synthesiaIO @AnthropicAI @vercel @runwayml @tryramp @Shopify @mercury @harvey @circle
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Ryan Sullivan retweeted
Claude Cowork, Mythos, and the Future of Software: my conversation with @felixrieseberg, who leads Cowork at @AnthropicAI
00:00 Intro
01:53 Claude Mythos Preview and the “step-function change”
06:16 Why Anthropic is treating Mythos differently
11:19 The real story behind Claude Cowork’s “10-day” build
12:42 Why Anthropic realized Claude Code needed a non-technical version
15:44 What Claude Cowork actually is
17:03 Under the hood: virtual machines, tools, skills
18:36 Where Cowork’s memory actually lives
19:26 How Cowork connects to files, apps, and the internet
20:45 Why Felix thinks the local computer is under-appreciated
24:49 Trust: how do you get users comfortable with AI agents?
28:45 What UX actually means for AI agents
31:27 Anthropic Cowork's roadmap is only one month long
34:12 Building 100 prototypes
35:10 If execution is free, what becomes the bottleneck?
37:25 Does it come down to taste?
40:12 The hardest part of building Claude Cowork
41:43 Advice for founders building AI agents
44:21 SaaSpocalypse: what’s left for software startups
49:30 Where AI agents are going next
51:20 Regulated industries and enterprise adoption
54:15 Hot takes: what's underrated, overrated, and what Felix would build today
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Ryan Sullivan retweeted
Vibe coding prototypes is cool
Building production-grade, secure apps with AI you can actually run a business on… that’s a different ballgame
Huge launch day for @softr_io, crazy shipping velocity over the last year 🔥@mariam_hakobyan @mkrtchyanartur
Softr
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Ryan Sullivan retweeted
Thrilled to announce that @FirstMarkCap has led the Series A in @tracebit_com , the company building the category-defining assume breach platform for the AI era, and I am joining the board. Our thesis is simple: prevention and zero trust alone are no longer enough. AI is expanding the attack surface faster than any team can defend it, and the smartest CISOs have stopped asking 'how do I keep attackers out' and started asking 'how do I catch them the moment they're in, before they can do any damage?' That's exactly what Tracebit does, and we couldn't be more excited to partner with Andy, Sam Cox and the rest of the team.
cc @Accel / @algovc, @TapestryVC, @MMC_Ventures, and CCL.
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Ryan Sullivan retweeted
Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of @LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack
00:00 Intro - meet @hwchase17 - at the Chase Center for the @daytonaio Compute conference
01:32 What changed in agents over the last year
03:57 Why coding agents are ahead
06:26 Do models commoditize the framework layer?
08:27 Harnesses, in plain English
10:11 Why system prompts matter so much
13:11 The upside — and downside — of subagents
15:31 Why a useful agent needs a filesystem
18:13 Additional core primitives of modern agents
19:12 Skills: the new primitive
20:19 What context compaction actually means
23:02 How memory works in agents
25:16 One mega-agent or many specialized agents?
27:46 The future of MCP
29:38 Why agents need sandboxes
32:35 How sandboxes help with security
33:32 How Harrison Chase started LangChain
37:24 LangChain vs LangGraph vs Deep Agents
40:17 Why observability matters more for agents
41:48 Evals, no-code, and continuous improvement
44:41 What LangChain is building next
45:29 Where the real moat in AI lives
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Ryan Sullivan retweeted
Thrilled to partner with @MaxJunestrand, @siggelabor, @davidneckstein, Pat, Jacob and the entire @WeAreLegora team as they build the legal operating system for the AI era!
Legal work demands judgment, precision, and trust. What’s exciting about this moment is that AI can actually support that work in a meaningful way, helping legal teams move faster, navigate complexity, and deliver high-quality work without compromising rigor.
That’s a big part of why we’re so excited about Legora. They’re building the leading platform around how legal work actually gets done.
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Ryan Sullivan retweeted
Did you know there's a ChatGPT-like system where other humans answer your questions instead of AI?
Surprisingly, doesn't work super well...
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Ryan Sullivan retweeted
Voice used to be AI’s forgotten modality - now it's having its big moment: rapid innovation, big funding rounds, major agentic applications
My conversation with @neilzegh, top AI researcher in the field (@GoogleDeepMind, @Meta, @kyutai_labs) and now CEO of @GradiumAI
This is a reference episode on all things voice AI 🔥
00:00 Intro
01:21 Voice AI’s big moment, and why we’re still early
03:34 Why voice lagged behind text/image/video
06:06 The convergence era: transformers for every modality
07:40 Beyond Her: always-on assistants, wake words, voice-first devices
11:01 Voice vs text: where voice fits (even for coding)
12:56 Neil’s origin story: from finance to machine learning, with help from @ylecun and @soumithchintala
18:35 Neural codecs (SoundStream): compression as the unlock
22:30 Kyutai: open research, small elite teams, moving fast 31:32
Why big labs haven’t “won” voice AI4
34:01 On-device voice: where it works, why compact models matter
46:37 The last mile: real-world robustness, pronunciation, uptime
41:35 Benchmarking voice: why metrics fail, how they actually test
47:03 Cascades vs speech-to-speech: trade-offs + what’s next
54:05 Hardest frontier: noisy rooms, factories, multi-speaker chaos
1:00:50 New languages + dialects: what transfers, what doesn’t
1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game
1:07:27 What data do you need to train voice models
1:09:02 Deepfakes + privacy: why watermarking isn’t a solution
1:12:30 Voice + vision: multimodality, screen awareness, video+audio
1:14:43 Voice cloning vs voice design: where the market goes
1:16:32 Paris/Europe AI: talent density, underdog energy, what’s next
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