investing & engineering @FirstMarkCap
New York, NY
Joined July 2013
- Tweets46
- Following378
- Followers155
- Likes405
Ryan Sullivan retweeted
We just raised a $60M Series B at a $1B valuation to build @daytonaio into the Agent Cloud.
@FirstMarkCap led the round, with all key existing investors and angels doubling down.
We're also thrilled to welcome the founders of some generational companies aboard, including @patrickc (Stripe), @tobi (Shopify), @AravSrinivas (Perplexity), @BrendanFoody (Mercor), @oliveur (Datadog), @adamdangelo (Quora) and @ericsyuan (Zoom)
We're a 35-person team. But that's about to change; we're hiring across the board, and we'd love for you to join us.
Ryan Sullivan retweeted
Crazy launch! Copycats! Massive round! On the one-month anniversary of the launch of Jev, we unpack it all with @CompleteSkeptic, CEO of @typesafeai: what Jev is, how it fixes what's broken in AI, and why the fast-followers are missing the hard part.
00:00 “95% Einstein, 5% Mr. Bean”
1:09 Why intelligence per dollar matters
3:25 Why TypeSafe built in stealth for two years
5:50 “We could have had AGI last year”
6:48 Why the product carries the marketing
9:33 What is Jev? Machine-native AI, built for software
10:53 System One vs. System Two
12:26 From simple decisions to composable AI
15:21 Automating email: giant prompts vs. smart if-statements
20:03 Probabilities, thresholds, and developer control
23:43 Why Jev is faster and cheaper than chat models
25:39 “Classification is AI-complete”
27:16 RLHF: how OpenAI trained models to please humans
31:28 Sycophancy, refusals, and the hidden costs of RLHF
34:22 RLVR: why benchmark gains don’t mean reliable automation
37:51 RLCD: optimizing for machine-native intelligence
39:54 Why TypeSafe builds its training data in-house
41:49 The Bitterest Lesson: define the task before scaling
44:39 Building Jev from open-weight models
45:56 Is Jev just a classifier? Why that’s a compliment
48:35 “Build prod, not God”
50:11 Embedding AI in software, beyond coding assistants
53:05 Jev’s copycats: matching the interface vs. the intelligence
55:33 Why benchmarks mislead the users they should help
58:37 Open weights vs. APIs: how TypeSafe weighs the tradeoff
1:00:04 Jevons paradox: a new market for machine intelligence
1:02:50 Why Diogo is skeptical of recursive self-improvement
1:04:47 Can practical automation avert an AI winter?
Ryan Sullivan retweeted
Special treat for the @andy_pavlo fans out there: an in-depth discussion about what happens to databases when billions of AI agents hit them, his move to build a research lab at @ClickHouseDB, and plently of hot takes on the database market (Postgres, vector, graph, GPU etc)
00:00 Cold open & Intro
01:21 From vector databases and RAG to the age of agents
04:09 Neon's stat: agents create 80% of databases? 07:25 Why agents keep deleting production databases 08:19 Guardrails: the toddler-and-stairs rule
10:51 The four eras of database volume: 10–100x more queries
15:10 Agent memory: files vs. databases ("everything is a database")
18:24 Which database do AI models recommend?
19:25 "It cites me back to myself"
23:14 MCP for databases, and text-to-SQL from 60% to 99.5%
27:09 Trust an agent the way you'd trust a junior developer
27:47 Can AI build an entire database? Opus 4 and the CMU projects
29:57 60% of open-source databases now have AI commits
30:43 Ten years of self-driving databases, Peloton to today
33:52 What LLMs changed: 85% of the tuning in 15 minutes
35:42 Should anyone still study databases?
38:27 Free CMU courses, the DJ, and the Wu-Tang final exam
42:09 Why start a research lab inside ClickHouse?
45:53 Why ClickHouse looked like vaporware in 2016
47:46 What makes ClickHouse fast: columns, vectors, Snowflake's lineage
52:22 Why Databricks, Snowflake and ClickHouse all added Postgres
58:15 Vector, graph and GPU databases: thumbs up or down?
1:08:34 Is the database market stagnant? "A cheetah on cocaine in a Ferrari"
1:10:14 The relational model is arithmetic; SQL as the new assembly
1:11:59 Larry Ellison, Linux, and why databases still matter
Ryan Sullivan retweeted
Phenomenal evening co-hosting an event last week with @theahura_ & @max_spero_ at the @FirstMarkCap office in Chelsea on what I believe is going to be the most important topic of the next decade: AI Slop
Kicked it off with a convo about @pangram, the story behind the company & how they address this problem. Followed up with a panel (moderated by yours truly) & including the Nori team on AI Slop in Code (and the tension b/w building a truly autonomous software factory and maintaining clean, thoughtfully architected codebases)
Links to the recordings below 🧵
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
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 @amankabeer this Thursday. Register below!
luma.com/agentics-g4ka
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
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?
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
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
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
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!
youtube.com/watch?v=w0RpSmye…
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
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
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
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
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
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.
