John Fries

SF AI-Native Operator Takeaway #2: In AI-native PLG, the hard part isn’t conversion... it’s discovery. Many AI-native teams are still talking about PLG using a classic SaaS mental model, but based on operator conversations in SF, that model is starting to break down in fairly obvious ways. The biggest bottleneck right now isn’t conversion. It’s discovery. In traditional PLG, users generally understood the category before they ever signed up. The problem was obvious, the product’s value was legible from the homepage, and the “aha” moment tended to show up quickly in first use. In that world, PLG meant optimizing onboarding, reducing friction, and improving free-to-paid conversion because user intent already existed. AI changes that assumption. In AI-native products, users are often curious but unclear. They don’t yet know what’s possible, value depends heavily on workflow, context, data, and role, and the product can feel abstract until it’s applied directly to their job. As a result, many users stall not because the product isn’t valuable, but because they haven’t discovered how it fits into their world and how they can't live without it. This is the real distinction people kept coming back to. PLG conversion answers, “Is this worth paying for?” PLG discovery answers, “What problem does this solve for me, right now?” What’s working best in practice is less about funnel polish and more about clarity up front: role- or workflow-specific entry points, guided examples instead of blank states, and opinionated first actions that show users a concrete outcome before asking them to explore. This also explains a broader pattern across AI-native companies. Forward-deployed teams and services-heavy delivery aren’t just implementation tools; they’re discovery mechanisms. They translate abstract AI capability into concrete workflow value, observe real use cases users wouldn’t self-discover, and feed those learnings back into what eventually becomes productized. PLG isn’t going away, but in AI-native companies it’s being redefined. Self-serve no longer means self-explanatory. Education becomes part of the product, and discovery has to come before optimization. The teams making progress aren’t obsessing over conversion rates yet. They’re focused on whether users see themselves in the product, how quickly they reach a meaningful outcome, and whether the product helps users get to a meaningful outcome for themselves quickly, without too much guesswork. Bottom line: in AI, PLG is less about removing conversion friction early and much more about creating understanding first. Once they understand, they may be hooked. Tomorrow is my last SF AI operator takeaway focusing on everyone's favorite topic du jour: 996 work schedules.

15

1 Comment

Today, we're releasing Goedel-mHC-1B, the first open 1B+ language model built with multi-stream Hyperconnections. Standard transformers carry all information through a single residual stream. Hyperconnections replace this with 4 parallel streams that mix through learned matrices at every layer, giving the model multiple channels to route information through the network. The result: 3.8% better bits-per-byte and wins on HellaSwag, ARC, and WinoGrande, with 15% fewer parameters than our standard transformer baseline. Same data, same compute, architecture is the only variable. The full stack: → Gated GQA (sigmoid output gate) → ReLU² FFN → mHC with 4 Sinkhorn-constrained streams → NorMuon optimizer Built for under $1000 total R&D cost. One researcher, Vast.ai H200s, and Claude Code for infrastructure. We're Gödel Machines — a bootstrapped AI startup. This is day one. Currently training on 100B additional tokens. Code release and technical writeup coming soon. Weights (Apache 2.0): https://lnkd.in/gyFcNMMB Blog: https://lnkd.in/gZU2CBts X: https://lnkd.in/gDWTzMpA

67

3 Comments

I just published my weekly reflections: https://lnkd.in/gPVzjPCM. Software's Hall-Héroult moment. Micro-intellectual gravity. An armada of slop cannons. Token furnaces. Hyper-productivity cage. Escape hatch abduction. The sharpness of schelling points. Pace layer induction. Shifting scarcity. Cognitive debt. Induced pull. Finding Grubby Truffles by smell.

15

Ok so RL isn’t dead, despite Richard Sutton's alarm bells. Yejin Choi offered a much more grounded view: pre-training + SFT + RL are still the only production-scale pipeline we have today, and we’re nowhere near the limits of what these methods can do. The real risk isn’t that the stack stops working; it’s that only the leading labs will be able to use it. Her argument was that progress won’t come from choosing between ‘post-training’ or ‘experience-based learning'. It’s an AND. And if we want healthy innovation, the post-training workflow (synthetic data generation, long-form reasoning, dynamic RL) needs to be accessible beyond OpenAI, DeepMind, and Anthropic. So, another 2026 prediction: We will have a DeepSeek moment in RL.. an open-source model hitting SOTA performance on domain-specific benchmarks with a fraction of the resources, powered by smarter training, synthetic data, and novel RL environments. #NeurIPS #NeurIPS2025

39

6 Comments

CRV Security: Request for Startups I never know if this actually works for our friends over at YC but figured we'd try. Here's what we want to fund in 2026! 1. Golden Artifacts: Think Chainguard but more broad. Artifact attestation exists for open source. Almost nothing exists for internal software — especially the vibe-coded tooling now running in production. We want the company building cryptographic proof of secure software delivered from secure artifacts: who built it, how, and whether it was reviewed. If more things are being yeeted into the world via Claude Code (myself included), this feels like an issue. 2. MCP & Agentic Security: Agents are getting real credentials and taking real actions. The security posture of most orgs around this is basically zero. That changes fast. You'd never give an employee hardcoded API keys or write access to your email without supervision/trust. Why give it to agents? 3. AI Governance: Boards are asking CISOs to account for AI risk. CISOs have no good answer other than "Palo has a module" 4. Next-Gen Endpoint: CrowdStrike was built for a world of static binaries and human operators. AI workloads, cloud-native infra, and AI-assisted attackers need a new architecture. The category is ready to be reinvented. 5. Networking in the AI Era: Zero trust was designed for humans. What does network security look like when the entity requesting access is an agent? Nobody's really solved this. 6. Email Security + Next-Gen Phishing: LLMs have made spear phishing infinitely scalable. I've never truly understood why Abnormal and KnowBe4 aren't one company. Maybe this time it's different. 7. Frontier Security Lab: We'd back a credible, well-staffed lab focused entirely on red-teaming models and setting the evidentiary standard the industry needs as LLM built apps become the norm. 8. Dependency Security: That Actually Remediates Malicious and vulnerable dependencies are a top attack vector. The tooling is mostly noise — scanners that don't close the loop. The winner here ships fixes, not just alerts. 9. Critical Infrastructure Cyber: Data centers, satellites, power grids, undersea cables. The physical backbone of the internet is increasingly exposed and wildly under-defended. We have data centers in space, for God's sake. Surely we need better cyber for critical infrastructure? 10. PAM for the Modern Era Legacy: PAM was built for static roles, human users, on-prem directories. Cyberark was founded in 1999.....Agents, ephemeral workloads, and cloud-native infra have broken all of those assumptions. Is anyone rebuilding this from scratch? If you're building in any of these areas — or something we haven't thought of — reach out. james@crv.com

452

49 Comments

I write a short piece like this every weekday (sign up to get it in your inbox at tbpn.substack.com): There are two types of reactions worth tracking when a new model releases these days. The folks who actually use the model and can evaluate or benchmark it, and then the other labs. Let’s start with the more direct reaction. Gemini 3 is a newer, better Gemini that’s smarter, faster, stronger, newer, and better. The model releases are starting to feel a little bit like iPhone releases in that way. Everything is definitely better, but we aren’t seeing hugely surprising “how did they pull that off” type of demos come out nearly as often as they did a few years ago. The benchmarks are impressive though. Gemini 3 hit double the current state of the art on the very difficult ARC v2 benchmark. There are a bunch of interesting findings as you dig into how Gemini works through particularly hard puzzles and it’s still not surpassing human level performance at this particular benchmark, but it’s impressive progress. In general, Google has been fantastic at delivering models right across the Pareto frontier for over a year now. At every benchmarkable level of performance, Google is going to offer a very competitive model. Their vertical integration across the DeepMind team, their TPU chips, and Google Cloud Platform continue to compound and deliver a very competitive foundation model product. Consumer adoption is a bit more complicated, since there are a variety of ways users get locked in. Little product features like smoother speech-to-text, easy navigation of previous chats, and maybe in the future better collaboration all add up to maintaining a user base even if a better experience exists elsewhere. Watching the indirect reaction, from the other labs, might actually tell you more about Gemini 3 than anything else. Anthropic took a page out of Sam Altman’s book and announced a big strategic partnership. Anthropic will spend $30B on Microsoft cloud compute. Anthropic gets a $10B investment from Nvidia and $5B from Microsoft. It’s the sort of circular deal that we’ve already hashed out a few times, but this deal seems to be missing a bunch of zeros and somehow that makes me more optimistic about it. OpenAI used to be really aggressive about front running Google PR moments, but it’s been a bit quiet. A new feature that allows you to spin up group chats in ChatGPT launched five days ago, and Fidji Simo did a profile in Wired which I took as another push toward advancing the ads product. Personally, I think ChatGPT is incredible at designing bespoke gift guides and will probably be responsible for influencing billions of dollars of gift buying this holiday season. Unfortunately, I don’t think their agentic commerce product will be anywhere mature enough to actually take a meaningful share of this. __________________________________________________ Continue reading today's piece in the comments below.

5

1 Comment