# Ulysses’ Series A, Physical Intelligence’s Deployments, and New AI Infrastructure Moats

*By VC Tech Radar • April 17, 2026*

A new defense-tech Series A, credible early robotics deployments, and several technical shifts reshaping AI competition stand out this cycle. The strongest read-throughs are around infrastructure concentration, hardware-specific inference economics, and a widening gap between AI-native products and incumbents shipping weak agent layers.

## 1) Funding & Deals

- **Ulysses — $46M Series A led by a16z American Dynamism.** Ulysses is building small, autonomous underwater vehicles aimed at outperforming incumbent systems at a fraction of the cost, with the pitch tied to undersea deterrence, contested fiber-optic cables, offshore resources, and maritime chokepoints. a16z says the company has a vertically integrated manufacturing facility and is hiring engineers and operators in San Francisco. [a16z writeup](https://a16z.com/announcement/investing-in-ulysses/) [^1][^2][^3]

- **Gecko Materials — priced seed round.** Gecko Materials said its recent priced seed was led by Kitty Hawk, with Alumni Ventures and Stanford participating. The financing follows a manufacturing breakthrough that reduced production time from 48 hours to under 15 minutes for its bio-inspired dry adhesive, which is already being used on the ISS and is being pushed toward semiconductors, automotive lines, robotics, and drones. [^4]

## 2) Emerging Teams

- **Physical Intelligence.** PI combines unusually strong robotics pedigree with early deployment evidence: the team includes former Google Robotics members Brian, Chelsea, Sergey, and Quan Vuong, plus Locky and hardware lead Adnan from Anduril. The company says it wants a model that can control any robot for any task, and it has already shown deployments with YC companies Weave and Ultra, including laundry folding on unseen items in a real laundromat and long-duration pouch packing in a live warehouse, with a working system assembled in roughly two weeks for one task. [^5]

- **Datost.** YC’s launch positions Datost as an AI data analyst inside Slack that keeps a semantic layer over business definitions, CRM data, docs, and code so it can interpret questions with company context. YC says it scored 75.2% on the hardest public text-to-SQL benchmark, versus 33% for Opus 4.6, and identified founders @maceock and @jasonhywang on the launch. [Launch page](https://www.ycombinator.com/launches/Pxg-datost-the-most-accurate-ai-data-analyst) [^6]

- **CompanyHelm.** CompanyHelm is building an open-source control plane for remote coding-agent sessions, where each session gets its own isolated environment. The workflow is explicitly assign task, let it run, then inspect results, and early user feedback says it solves the pain of browser conflicts and port collisions while enabling parallel sessions, end-to-end testing, PR-linked demos, and adversarial reviews. [GitHub](https://github.com/CompanyHelm/companyhelm) [^7][^8][^9][^7]

## 3) AI & Tech Breakthroughs

- **Anthropic’s Mythos looks like a genuine capability jump in code-security automation.** Multiple summaries describe it as autonomously finding thousands of zero-day vulnerabilities across large codebases, including bugs that had remained dormant for years, without needing prompt-by-prompt steering. Anthropic withheld public release for six months and shared it with security vendors instead. [^10][^11]

- **Physical Intelligence’s technical stack suggests a credible robotics foundation-model path.** PI says its Open Cross Embodiment / RT-X work produced a generalist model that outperformed specialist policies by 50% across 10 robot platforms. It also says it can run cloud-hosted inference inside real-time control loops via chunking and pipelining, and that π0 and π0.5 were open-sourced with the same pretrained weights used internally. [^5]

- **Gemma 4 pushes open models further onto the edge.** The 2B model is described as running offline on phones, in browsers, and even on an original Nintendo Switch, while the 31B version is framed as the third-best open model and competitive with models 10-20x larger. The release also matters because it moved to an Apache 2.0 license and expanded context length to 256k. [^12]

- **ResBM is a notable infrastructure paper for low-bandwidth training.** Macrocosmos describes a residual encoder-decoder bottleneck across pipeline boundaries that delivers state-of-the-art 128x activation compression without significant convergence loss, positioning it as progress for decentralized or internet-grade pipeline-parallel training. [Paper](https://arxiv.org/abs/2604.11947) [^13]

## 4) Market Signals

- **Capital is concentrating harder at the top while AI-native winners capture outsized value creation.** In Q1 2026, 73.1% of LP capital raised went to five VC firms, and $195.6B, or about 75% of VC deal value, went to five companies. Separately, 48 gen AI unicorns created more aggregate new market cap in 2025 than the other 1,100+ unicorns combined, and the Bay Area accounts for about 91% of generative AI unicorn market cap. [^14][^15]

- **AI infrastructure moats are shifting toward hardware-specific inference economics.** Gavin Baker argues true model portability is eroding as accelerator topologies and memory systems diverge, pushing frontier labs toward co-design for specific systems such as GB300 racks, Cerebras, TPUs, and Blackwell/Rubin clusters. The reported OpenAI-Cerebras arrangement — more than $20B over three years, potentially $30B, plus warrants up to 10% of Cerebras and roughly $1B of funding — is a concrete example of that direction. Aravind Srinivas explicitly endorsed the argument as accurate. [^16][^17][^18]

- **Incumbent SaaS is being judged on whether agents are good enough to sell standalone.** The 20VC x SaaStr discussion argues that 60%-quality agents become free features rather than revenue drivers, which leaves incumbents exposed unless they can build products customers will pay for independently. SaaStr AI Annual registration data points in the same direction: the most popular sessions are about deploying AI in real workflows, and interest is clustering around AI-native operators such as Lovable, Gamma, Replit, and Anthropic rather than theory. [^10][^19]

- **Small systems choices are starting to matter as much as model choice.** Sarah Guo argues long-horizon ML research engineering is a systems problem, not just a local reasoning problem. At the tooling layer, one builder said adding an llms.txt file to API docs improved agent integration success from roughly 60% to near-perfect because the model stopped wasting context on HTML navigation. [^20][^21][^22][^23]

## 5) Worth Your Time

- **[Physical Intelligence on the robotics scaling moment](https://www.youtube.com/watch?v=4EsUaur0nsQ)** — best primary-source walkthrough in this set on cross-embodiment learning, cloud inference, and why PI thinks real deployment is arriving faster than expected. [^5]

[![The GPT Moment for Robotics Is Here](https://img.youtube.com/vi/4EsUaur0nsQ/hqdefault.jpg)](https://youtube.com/watch?v=4EsUaur0nsQ&t=1431)
*The GPT Moment for Robotics Is Here (23:51)*


- **[20VC x SaaStr on Anthropic Mythos](https://www.youtube.com/watch?v=UOY8hsBqjJo)** — the clearest explanation here of why autonomous vulnerability discovery changes the offense-defense balance and why the speakers think cyber budgets should rise, not fall. [^11][^10]

[![SpaceX's Financials Leaked: Is it Worth $2TN | Meta Debuts Muse Spark: Are They Back in the AI Race?](https://img.youtube.com/vi/UOY8hsBqjJo/hqdefault.jpg)](https://youtube.com/watch?v=UOY8hsBqjJo&t=188)
*SpaceX's Financials Leaked: Is it Worth $2TN | Meta Debuts Muse Spark: Are They Back in the AI Race? (3:08)*


- **[Unicorn Market Cap 2026: SF is the GenAI Super Cluster](https://blog.eladgil.com/p/unicorn-market-cap-2026-sf-is-the)** — useful macro data on Bay Area concentration, the rise of gen AI within total unicorn value, and the slowing pace of new unicorn creation. [^15]

- **[businessbarista’s thread on the enterprise Brain category](https://x.com/businessbarista/status/2044874360280723934)** — a strong framing for why knowledge-work automation is bottlenecked by distributed, unstructured, and unverifiable company context rather than raw model capability. [^24]

- **[The llms.txt docs thread](https://x.com/bpizzacalla/status/2044824325145153973)** — short, practical signal that documentation format is becoming agent infrastructure. [^22][^23]

---

### Sources

[^1]: [𝕏 post by @Willob](https://x.com/Willob/status/2044785662155796484)
[^2]: [𝕏 post by @a16z](https://x.com/a16z/status/2044789057566523757)
[^3]: [𝕏 post by @a16z](https://x.com/a16z/status/2044793250125578498)
[^4]: [What It Takes to Turn Academic Research into a Venture-Backed StartupBM S2E9 FULL EDIT](https://www.youtube.com/watch?v=pxAHMiFZSVQ)
[^5]: [The GPT Moment for Robotics Is Here](https://www.youtube.com/watch?v=4EsUaur0nsQ)
[^6]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2044838231397560596)
[^7]: [r/SideProject post by u/lollete5](https://www.reddit.com/r/SideProject/comments/1snrlxf/)
[^8]: [r/SideProject comment by u/Designer_Reaction551](https://www.reddit.com/r/SideProject/comments/1snrlxf/comment/ognvazz/)
[^9]: [r/SideProject comment by u/BalanceSuspicious851](https://www.reddit.com/r/SideProject/comments/1snrlxf/comment/ognrfgj/)
[^10]: [20VC x SaaStr: “I Don’t Buy Dario Anymore”, Mythos Withheld Over Zero-Days, Meta’s Muse Spark Gets Back in the Game, SpaceX at $2T on 108x Revenue, and Why 60% Agents Are the Slow Death Spiral for Public SaaS](https://www.saastr.com/20vc-x-saastr-i-dont-buy-dario-anymore-mythos-withheld-over-zero-days-metas-muse-spark-gets-back-in-the-game-spacex-at-2t-on-108x-revenue-and-why-60-agents-are-the-slow-death-sp)
[^11]: [SpaceX's Financials Leaked: Is it Worth $2TN | Meta Debuts Muse Spark: Are They Back in the AI Race?](https://www.youtube.com/watch?v=UOY8hsBqjJo)
[^12]: [Why DeepMind’s New AI Broke The Internet](https://www.youtube.com/watch?v=Sk9tvyRSCgY)
[^13]: [r/MachineLearning post by u/network-kai](https://www.reddit.com/r/MachineLearning/comments/1sn6b90/)
[^14]: [VC in 2026: 75% of All the Money Is Going to Just 5 VC Funds. And To Just 5 “Startups.”](https://www.saastr.com/vc-in-2026-75-of-all-the-money-is-going-to-just-5-funds-and-just-5-startups)
[^15]: [Unicorn Market Cap 2026: SF is the GenAI Super Cluster](https://blog.eladgil.com/p/unicorn-market-cap-2026-sf-is-the)
[^16]: [𝕏 post by @GavinSBaker](https://x.com/GavinSBaker/status/2044861680015069571)
[^17]: [𝕏 post by @anissagardizy8](https://x.com/anissagardizy8/status/2044949727549268454)
[^18]: [𝕏 post by @AravSrinivas](https://x.com/AravSrinivas/status/2044940850715897857)
[^19]: [The Top 10 Most Popular Sessions at SaaStr AI Annual 2026 \(So Far\). May 12-14 in SF Bay!](https://www.saastr.com/the-top-10-most-popular-sessions-at-saastr-ai-annual-2026-so-far-may-12-14-in-sf-bay)
[^20]: [𝕏 post by @saranormous](https://x.com/saranormous/status/2044776712979071093)
[^21]: [𝕏 post by @saranormous](https://x.com/saranormous/status/2044777371975487987)
[^22]: [𝕏 post by @bpizzacalla](https://x.com/bpizzacalla/status/2044824325145153973)
[^23]: [𝕏 post by @garrytan](https://x.com/garrytan/status/2044826190788362709)
[^24]: [𝕏 post by @businessbarista](https://x.com/businessbarista/status/2044874360280723934)