# Trajectory’s $15M Round, Robotics Simulation Progress, and New Vertical-AI Wedges *By VC Tech Radar • May 28, 2026* Trajectory’s continual-learning raise leads this investor brief, followed by emerging YC teams in compute, finance, labor, and nuclear, plus technical signals from robotics simulation, portable MoE inference, and AI-search infrastructure. The broader pattern is value moving toward vertical scaffolding, open-source positioning, and new distribution surfaces such as AI search. ## Funding & Deals - **Trajectory raised $15M** from Conviction, Bessemer, Radical, Jeff Dean, Fei-Fei Li, and others. The company is building a continual-learning platform that uses product-usage signals to continuously post-train agentic models; its research team comes from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, and Scale AI, with product talent from Stripe and Figma, and it says partners including Clay, Harvey, Decagon, Mercor, and Rogo are already using the system, with some deployments in production [^1][^2][^1] - **TechCrunch’s Equity podcast surfaced Scrunch’s capital path**: a $4M seed in March and a $15M Series A in July. The company pairs AI-search visibility analytics with an "agent experience" product that strips non-semantic page elements and serves bot-optimized versions at the edge [^3] ## Emerging Teams - **InfinityOS / ProjectX_Cloud** is a web-based OS that lets any device run Windows or Linux desktop apps, each with its own GPU but inside the same workspace and filesystem. YC’s demo claims included a Chromebook running Isaac Sim, an iPad rendering Blender in 4K, and a phone running 10 parallel agents; founders are Rounacc, Bishal, and runallapps [^4] - **KelAI** is an autonomous research engine for hedge funds and institutional investors, built by an experienced quant PM, that runs idea generation, validation, monitoring, and feedback in one agentic system [^5] - **Apollo Atomics** is building compact nuclear reactors with less than 24-month deployment timelines. Its wedge is a modified pressurized-water design that flips the steam generator to make the plant an order of magnitude smaller without reducing power; founders are Assil Halimi and Drew [^6] - **Rentahuman and Eden** both point toward AI-native labor models in the physical world. Rentahuman lets AI agents communicate with and pay humans for real-world tasks and frames the mission around creating jobs and coordinating workers at global scale; Eden launched Eden I, an industrial semi-humanoid robot available for hourly hire [^7][^8] ## AI & Tech Breakthroughs - **Genesis World 1.0** is an open-sourced robotics simulation stack aimed at turning physical-world iteration into a compute problem. The release says one hour of real testing can become 100 days of simulation and describes a rebuilt stack including a GPU-accelerated cross-platform compiler, penetration-free multi-physics contact solvers, unified rigid and deformable physics, a photo-realistic Nyx renderer, and the Quadrants engine, which the team says delivers 10x faster launch and up to 4.6x runtime versus the prior Genesis release; it also reports near real-time dexterous manipulation across multiple embodiments with a lower sim-to-real gap [^9] - **TritonMoE** is a portable MoE inference kernel worth watching. A new preprint describes it as written entirely in OpenAI Triton for NVIDIA and AMD portability without vendor-specific code; the authors report a fused gate+up GEMM that eliminates 35% of global memory traffic, 89-131% of Megablocks throughput at inference batch sizes up to 512 tokens on A100, identical execution on MI300X, and limitations at 2048+ tokens or with 64+ experts under extreme routing skew [^10] - **Scrunch’s "agent experience" layer reframes technical SEO for LLMs.** The company says key pages can be reduced by 98-99% in token count—for example from roughly 100k tokens to 1-2k—by stripping code, JavaScript, image tags, and other non-semantic content, then serving the optimized version only to bots while humans keep the normal page [^3] ## Market Signals - **Startup formation and monetization look faster than the last SaaS cycle.** A circulated Stripe datapoint set claimed new business creation was up 2x YoY in March, 20% of startups charged their first customer within 30 days versus 8% in 2020, time to $1M/$10M/$100M ARR is down about 2x, and average revenue per business is still up despite 2x more company creation [^11] - **The app-layer thesis is shifting toward vertical scaffolding, not just better models.** a16z argues that in complex verticals, value comes less from raw model capability than from the surrounding infrastructure that makes output trustworthy, compliant, and operational; horizontal categories like code generation improve directly with pre-training spend, while vertical problems require industry-specific systems [^12] - **Founder edge may be moving toward domain expertise and distribution.** Another circulated take argued for vertical SaaS in under-softwared industries, said domain experts can outperform traditional Silicon Valley pedigrees, and pointed to a teacher-built MagicSchool AI as an example of how quickly AI-native companies can scale [^11] > "the moat in software was never the code > it was always coordination, distribution, and knowing what to build" [^11] - **AI search is becoming a distinct distribution surface.** Scrunch says many customer sites now see more AI bot traffic than human traffic and that the gap is widening month over month; it also says AI referrals convert 400% higher than traditional organic search, while the broader backdrop is rapid growth in Google AI Overviews, AI Mode, Gemini, and longer open-ended queries [^3] - **Open source positioning remains a real wedge in AI dev tools.** Pragmatic Engineer reports OpenCode rose from roughly 650k MAU to nearly 8M and almost 1M DAU in a few months, argues open source positioning was a major reason it captured the category, and also flags GPU demand as a system-wide bottleneck [^13] ## Worth Your Time **TechCrunch Equity on AI search** — [watch here](https://www.youtube.com/watch?v=f3x0F8rEDQ0). Useful background on how Google AI Overviews, AI Mode, and Gemini are changing discovery flows, plus a founder-level explanation of bot-optimized pages [^3] [![Google just broke SEO. Here’s what replaces it. | Equity Podcast](https://img.youtube.com/vi/f3x0F8rEDQ0/hqdefault.jpg)](https://youtube.com/watch?v=f3x0F8rEDQ0&t=372) *Google just broke SEO. Here’s what replaces it. | Equity Podcast (6:12)* **Trajectory announcement thread** — [X post](https://x.com/rronak_/status/2059644771262730624) for the clearest primary source on the round, team pedigree, and early partner list [^1] **OpenCode episode** — [Pragmatic Engineer](https://newsletter.pragmaticengineer.com/p/opencode) if you want a tighter view on open-source positioning, GPU bottlenecks, and the shift toward AI coding agents [^13] **TritonMoE** — [paper](https://arxiv.org/abs/2605.23911) and [code](https://github.com/bassrehab/triton-kernels) for a concise read on cross-platform MoE inference portability [^10] **InfinityOS and Apollo Atomics** — [InfinityOS launch](https://www.ycombinator.com/launches/QXY-infinity-a-new-computation-layer) and [Apollo Atomics launch](https://www.ycombinator.com/launches/QXj-apollo-atomics-the-modern-nuclear-company) for quick primary-source product pages [^4][^6] --- ### Sources [^1]: [𝕏 post by @rronak_](https://x.com/rronak_/status/2059644771262730624) [^2]: [𝕏 post by @saranormous](https://x.com/saranormous/status/2059785254077116890) [^3]: [Google just broke SEO. Here’s what replaces it. | Equity Podcast](https://www.youtube.com/watch?v=f3x0F8rEDQ0) [^4]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2059741438502396315) [^5]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2059635742981132468) [^6]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2059726337967878398) [^7]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2059681042248974741) [^8]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2059650319680962672) [^9]: [𝕏 post by @gs_ai_](https://x.com/gs_ai_/status/2059690796266491946) [^10]: [r/MachineLearning post by u/bassrehab](https://www.reddit.com/r/MachineLearning/comments/1tpj6e5/) [^11]: [𝕏 post by @rexan_wong](https://x.com/rexan_wong/status/2059732190834094368) [^12]: [𝕏 post by @a16z](https://x.com/a16z/status/2059650543027708021) [^13]: [Building OpenCode with Dax Raad](https://newsletter.pragmaticengineer.com/p/opencode)