Ryan Sullivan

investing @FirstMarkCap

[Ryan Sullivan @sullivan]

Has anyone heard of this new customer service AI “US mail”?

[Matt Turck @mattturck]

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

[Matt Turck @mattturck]

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

[FirstMark @FirstMarkCap]

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

[Matt Turck @mattturck]

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

[Matt Turck @mattturck]

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

[David Waltcher @DavidWaltcher]

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.

[Matt Turck @mattturck]

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

[Arnav Bimbhet @abimbhet]

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.

[Ben Winn @mrbenwinn]

Did you know there's a ChatGPT-like system where other humans answer your questions instead of AI?

Surprisingly, doesn't work super well...

[Matt Turck @mattturck]

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

[Ryan Sullivan @sullivan]

@alvinalaphat @will__ye accidentally left opus temperature=1

[David Waltcher @DavidWaltcher]

Lots of chatter around which models are winning the enterprise war

We asked 150+ CTOs / CPOs in our @FirstMarkCap Guilds (mix of F1000 + private unicorns) what they use. Small curated sample that's very enterprise skewed, admittedly

This isn't "the market," but it is what the moment in time standard looks to be inside large co's:
- OpenAI leads on primary API usage
- Gemini and Anthropic have gained real ground YoY
- "Other" has collapsed as the large labs have consolidated spend
- 58%+ of respondents use 2+ model APIs across their org

Bigger takeaway: ask different audiences, get different answers. Hackers, startups, MM co's, and global enterprises are operating under different parameters (security, procurement, data residency, multi-model strategy, etc)

[Amish Jani @amishjani]

Excited to be leading @VillageSQL $25M Series A alongside @GVteam , @sparkcapital & @homebrew .

@VillageSQL is reigniting innovation in MySQL 🔥 - bringing permissionless extensions & AI-native functionality inside the database, without migrations.

Big moment for the MySQL ecosystem.

#AI #Databases #OpenSource #MySQL #VentureCapital

https://t.co/MZojBqEa2E

[Matt Turck @mattturck]

NVIDIA's New Moat & Why China is "Semiconductor Pilled” — loved my conversation with Dylan Patel (@dylan522p).

Raw, incredibly insightful, and... hilarious.

00:00 - Intro

01:16 - Nvidia acquires Groq: A pivot to specialization

07:09 - Why AI models might need "wide" compute, not just fast

10:06 - Is the CUDA moat dead? (Open source vs. Nvidia)

17:49 - The startup landscape: Etched, Cerebras, and 1% odds

22:51 - Geopolitics: China's "semiconductor-pilled" culture

35:46 - Huawei's vertical integration is terrifying

39:28 - The $100B AI revenue reality check

41:12 - US Onshoring: Why total self-sufficiency is a fantasy

44:55 - Can the US actually build fabs? (The delay problem)

48:33 - The CapEx Bubble: Is $500B spending irrational?

54:53 - Energy Crisis: Why gas turbines will power AI, not nuclear

57:06 - The "AI uses all the water" myth (Hamburger comparison)

1:03:40 - Circular Debt? Debunking the Nvidia-CoreWeave risk

1:07:24 - Claude Code & the software singularity

1:10:23 - The death of the Junior Analyst role

1:11:14 - Model predictions: Opus 4.5 and the RL gap

1:14:37 - San Francisco Lore: Living with roommates @dwarkesh_sp & @_sholtodouglas

[Matt Turck @mattturck]

Every human worker needs a computer. Soon, every AI agent will too, but not one: millions, provisioned instantly, securely isolated, and running in parallel.

Thrilled to be partnering with @ivanburazin (who didn't a break during the holidays), @JukicVedran and @daytonaio (+ @upfrontvc, @datadoghq, @figma, @PaceCap)

https://t.co/fV3sXFTWeh

[David Waltcher @DavidWaltcher]

Very excited to announce our $20m Series A in @porterdotrun, led by the fantastic Justin Rhee (@rheejust). Porter was built to make infrastructure something startups don’t have to relearn as they scale, delivering a full PaaS experience inside their customer’s own cloud accounts. Porter has quickly become the default choice for this generation of AI companies (cc @TennrOfficial, @outtake_ai, @ModelML_, @GigaAI), already a top-five all time most used dev tool in YC, but impressively touts public companies and $1bn+ revenue businesses as customers as well. Their product roadmap is endless and we couldn’t be more excited to be on the journey with Justin and team.

[FirstMark @FirstMarkCap]

NYC Angel Investors!

FirstMark, @alley_corp, @usv, @LererHippeau, and @InspiredCap are hosting the inaugural NYC Angels Summit, bringing together a select group of the most active and up-and-coming angels investing in the NYC ecosystem.

Apply to attend at the link below ⬇️

[Matt Turck @mattturck]

Gemini 3, scaling laws and the 'finite data' era: my conversation with @borgeaud_s, research engineer at @GoogleDeepMind and a pre-training lead for Gemini 3

00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system

00:58 – @OriolVinyalsML's “secret recipe”: better pre- + post-training

02:09 – Why AI progress still isn’t slowing down

03:04 – Are models actually getting smarter?

04:36 – Two–three years out: what changes first?

06:34 – AI doing AI research: faster, not automated

07:45 – Frontier labs: same playbook or different bets?

10:19 – Post-transformers: will a disruption happen?

10:51 – DeepMind’s advantage: research × engineering × infra

12:26 – What a Gemini 3 pre-training lead actually does

13:59 – From Europe to Cambridge to DeepMind

18:06 – Why he left RL for real-world data

20:05 – From Gopher to Chinchilla to RETRO (and why it matters)

20:28 – “Research taste”: integrate or slow everyone down

23:00 – Fixes vs moonshots: how they balance the pipeline

24:37 – Research vs product pressure (and org structure)

26:24 – Gemini 3 under the hood: MoE in plain English

28:30 – Native multimodality: the hidden costs

30:03 – Scaling laws aren’t dead (but scale isn’t everything)

33:07 – Synthetic data: powerful, dangerous?

35:00 – Reasoning traces: what he can’t say (and why)

37:18 – Long context + attention: what’s next

38:40 – Retrieval vs RAG vs long context

41:49 – The real boss fight: evals (and contamination)

42:28 – Alignment: pre-training vs post-training

43:32 – Deep Think + agents + “vibe coding”

46:34 – Continual learning: updating models over time

49:35 – Advice for researchers + founders

53:35 – “No end in sight” for progress + closing