# Open-Model Adoption, Physical AI Data, and the Agent Loop Stack *By VC Tech Radar • July 25, 2026* Investor signals this period center on open-model adoption, the data and control layers needed for physical AI and long-horizon agents, and concentrated AI financing. The brief also highlights an enterprise SaaS round, emerging teams, and several technical developments worth monitoring. ## 1. Funding & Deals ### Pitchit reports a $3M raise to turn agency work into enterprise software The founder of **Pitchit** reports raising an initial **$200K** from Plug and Play’s co-founder, followed by **$3M** from Silicon Road Ventures, Gray Ventures, and angel investors. The company originated from an agency that reached $120K MRR; the founder shut it down to build software automating its marketing-consulting work. [^1] The founder’s operating background includes roles as the eighth employee at a YC W24 startup later acquired by Square, first sales hire at a YC W25 startup, and sixth employee at a 500 Startups-backed company. [^2] ## 2. Emerging Teams ### Encord is building the data layer for physical AI **Encord** is positioning itself around multimodal data management, curation, annotation, and evaluation for physical-AI workloads. Co-founder Eric Landau brings particle-physics big-data experience and a decade in high-frequency trading; he left that role during COVID to start the company. [^3] The company began with computer vision, then expanded into multimodal systems as physical AI became a larger application area. Its current growth areas include robotics, humanoids, consumer robotics, and manufacturing; it operates a Bay Area data-collection facility and says it serves hundreds of customers across multiple petabytes of multimodal data. [^3] A useful product-market-fit signal: Landau describes a point at which a sale closed without the founders recognizing the buyer, after previously conducting every sale themselves. [^3] ### Piyaz.ai targets collaboration and context management for agentic teams A solo engineer has released **piyaz.ai**, a free, open-source collaborative workspace built around a specific pain point: project context becoming stale as agents deliver quickly and make assumptions. The product breaks requirements into tasks, constructs task-specific context from status, and supports iterative delivery while keeping teams in control of direction and quality. [^4] ### Handshake AI is a large-scale human-data demand signal A post about founder and CEO Garrett Lord says **Handshake AI** began recruiting STEM PhDs for a frontier lab within two weeks of a December 2024 conversation about model-training challenges. Fifteen months later, the post reports $1B in gross annualized revenue and describes the company as a human-data engine for frontier labs. [^5] ## 3. AI & Tech Breakthroughs ### Induction Labs proposes video-trained “imagination models” **Induction Labs** introduced *imagination models*, a foundation-model architecture intended to learn from internet-scale video. Its first model, **Photon-1**, reportedly learned computer use from 18 years of unlabeled screen recordings, without action labels. [^6] The technical proposition is notable because it seeks to derive interactive-computer behavior from passive visual data rather than conventionally labeled action trajectories. [^6] ### Profluent Bio pushes protein design toward complex molecular machines **Profluent Bio** says its models can design, interpolate, and extrapolate from monomeric proteins to complex 1,400-amino-acid machines with multiple domains and dynamic interactions. [^7] ### Long-horizon agents shift the infrastructure problem from prompts to loops Lightspeed frames **loop engineering** as the practice of making agents behave productively over long time horizons without human intervention. Its described control pattern is OODA—observe, orient, decide, act—with deterministic termination conditions and verification intended to prevent unbounded runs and context-window exhaustion. [^8] The emerging stack includes AI-native search through Exa, long-trajectory training data through Protronus, durable execution through Temporal, and observability or remediation through Judgment Labs and Raindrop. [^8] ### A reported mathematical result is worth independent scrutiny Not Boring reports that Anthropic mathematician Levent Alpöge, working with Claude Fable 5, produced a counterexample to the Jacobian conjecture. The article describes a three-variable polynomial map with a constant Jacobian determinant of -2 that maps three distinct inputs to the same output—therefore lacking an inverse. This is a reported claim from the publication, rather than independent validation in the supplied material. [^9] ## 4. Market Signals ### Open models are becoming a product, policy, and sovereignty debate NVIDIA CEO Jensen Huang’s stated case for open models is that they strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty; he argues the world needs both frontier closed and frontier open models. [^10] The policy stakes are rising alongside the technical case. More than 200 startups and investors, including leaders associated with Y Combinator, Replit, Proton, and Yelp, are forming the **Little Tech Alliance**; its first initiative was letters opposing a ban on Chinese open-weight models. [^11] ### OpenCode demonstrates demand for model choice at significant scale Open-source coding agent **OpenCode** reported approximately **4.6M weekly active users**, **13M monthly active users** at the end of June, and roughly **7T tokens processed daily**—up from about 300B daily tokens at the start of the year. [^12][^13] Its founder says low cost, speed, and task-specific strengths influence model selection: DeepSeek Flash lets users extend coding-agent usage at low cost, while some users perceive GLM 5.2 as stronger for front-end design. [^13] The company’s product separates the agent loop from the UI and enables users to select among models; it says it is the largest customer by token volume for most open-source model labs. [^13] ### Capital is concentrating around AI According to a PitchBook report cited by Newcomer, corporate venture firms have supplied almost **90% of AI VC dollars** so far this year, up from less than 50% a decade ago. [^11] For early-stage investors, this concentration makes differentiated access to technical founders and lower-capital formation wedges more consequential. ### Physical AI is gaining multiple infrastructure and application wedges A16z’s Qasar Younis argues that physical AI could become larger than LLMs, emphasizing that many workers and industrial companies are focused on physical machines rather than software agents. [^14] Encord’s growth in robotics and manufacturing data infrastructure offers one operational signal behind that thesis. [^3] ## 5. Worth Your Time - **[OpenCode CEO: Blocked, 20X Growth in 6 Months, Building the Coding Agent for the World](https://www.youtube.com/watch?v=_O6x4ktK6JA)** — A detailed discussion of model choice, token economics, global developer adoption, and the architecture separating an agent loop from its interface. [^13] [![Opencode CEO: Blocked, 20X Growth in 6 Months, Building the Coding Agent for the World](https://img.youtube.com/vi/_O6x4ktK6JA/hqdefault.jpg)](https://youtube.com/watch?v=_O6x4ktK6JA&t=1332) *Opencode CEO: Blocked, 20X Growth in 6 Months, Building the Coding Agent for the World (22:12)* - **[From Prompt to Loop Engineering](https://www.youtube.com/watch?v=dDqk4fbam5o)** — A practical map of the emerging long-horizon-agent stack: termination, verification, durable execution, trajectory data, and observability. [^8] - **[Yann LeCun’s Meta Exit and the End of Open Source AI for the U.S.](https://www.youtube.com/watch?v=nhpE6K2Nnkc)** — LeCun explains his move to Advanced Machine Learning Labs, his long-running alternative-architecture research, and the case for federated, open foundation models through Project Tapestry. [^15] - **[High-Stakes Battle Over China Policy & Open Source AI Pits LLM Giants Against Their Customers](https://www.newcomer.co/p/high-stakes-battle-over-china-policy)** — Useful context on the Little Tech Alliance, Chinese open-weight-model policy, and the growing role of corporate capital in AI financing. [^11] --- ### Sources [^1]: [r/SaaS comment by u/redsuitguy](https://www.reddit.com/r/SaaS/comments/1v5ob7e/comment/ozku59r/) [^2]: [r/SaaS post by u/redsuitguy](https://www.reddit.com/r/SaaS/comments/1v5ob7e/) [^3]: [Why Physical AI Is the Next Platform Shift](https://www.youtube.com/watch?v=F3INH9wZXoQ) [^4]: [r/SideProject post by u/The_Mr_Suit](https://www.reddit.com/r/SideProject/comments/1v53zy8/) [^5]: [𝕏 post by @nakul](https://x.com/nakul/status/2079650288097759337) [^6]: [𝕏 post by @induction_labs](https://x.com/induction_labs/status/2080322704973160760) [^7]: [𝕏 post by @nathanbenaich](https://x.com/nathanbenaich/status/2080600912817451044) [^8]: [From Prompt to Loop Engineering | Lightwork](https://www.youtube.com/watch?v=dDqk4fbam5o) [^9]: [Weekly Dose of Optimism #203](https://www.notboring.co/p/weekly-dose-of-optimism-203) [^10]: [𝕏 post by @JensenHuang](https://x.com/JensenHuang/status/2080643682408321103) [^11]: [High-Stakes Battle Over China Policy & Open Source AI Pits LLM Giants Against Their Customers](https://www.newcomer.co/p/high-stakes-battle-over-china-policy) [^12]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2080655833810231505) [^13]: [Opencode CEO: Blocked, 20X Growth in 6 Months, Building the Coding Agent for the World](https://www.youtube.com/watch?v=_O6x4ktK6JA) [^14]: [𝕏 post by @a16z](https://x.com/a16z/status/2080744869241421840) [^15]: [Yann LeCun’s Meta Exit and the End of Open Source AI for the U.S.](https://www.youtube.com/watch?v=nhpE6K2Nnkc)