# Corporate Capital Floods AI While Agents Become Company Infrastructure

*By VC Tech Radar • August 1, 2026*

A robotics seed with live public hardware, a $700 million AI-neolab Series A, and new evaluation and company-harness layers mark the period. The investment question is shifting from access to models toward ownership of workflows, compute, data, and proof of reliability.

## 1. Funding & Deals

**Enigma’s seed pairs a robotics round with a public interaction-data experiment.** The company emerged from stealth with a $71 million seed led by Index Ventures and Ribbit Capital, while opening more than 100 real AI-powered robots for anyone to control online in real time. The physical arms are in facilities in Israel and California; Enigma is building robot-agnostic software, robotics foundation models, and interfaces, and is using the public robots.online experiment to observe how people command machines through text, audio, and demonstrations. Founders Jonathan Jacobi and Gal Niv met in teenage hacking competitions and served together in Israel’s Unit 8200; neither is a robotics specialist. [^1] The investable wedge is therefore not only hardware or model research, but a way to accumulate human–robot interaction data and learn a command interface across heterogeneous machines.

**Hark illustrates how corporate capital is reshaping early AI financing.** Newcomer reports that corporate venture capital accounted for almost 90% of all VC dollars invested in AI firms this year, versus less than 50% a decade ago; it also reports $90 billion of Nvidia corporate-VC investment over the prior 16 months and 283 funding rounds between 2021 and 2025, 85% of them in AI. [^2] The $700 million Series A for Figure founder Brett Adcock’s new AI neolab, Hark, included Nvidia, AMD Ventures, ARK Invest, Brookfield, Intel Capital, Qualcomm Ventures, and Salesforce Ventures. [^2] For investors, the diligence question is no longer just who leads a round: it is whether strategic and hardware-linked capital creates durable advantage or embeds dependence on the financing ecosystem itself.

## 2. Emerging Teams

**Netic is selling revenue generation to essential-service operators, not another generic copilot.** Founder and CEO Melissa Tokmak previously worked as a director of engineering and on go-to-market at Scale AI, with experience at Meta. Netic builds AI for large real-world service businesses across HVAC, plumbing, electric, hospitality, automotive, pet services, and related categories; its agents handle customer interactions across calls, text, websites, and scheduling, then reason about deploying the appropriate labor. [^3] Tokmak says more than 70% of customers are already “AI first,” meaning their first interaction with the company is handled by Netic agents. [^3] She also says the company has generated more than $600 million for customers through AI-handled interactions. [^3] The commercial thesis is notable: private-equity conversations still begin with cost cutting, but Netic positions the product around measurable net-new revenue and live deployments rather than demos. [^3]

**Decagon is turning customer-support agents into an operating-process product.** About 90% of its workflow runs on open-source models, while frontier models are used for new products; the team says fine-tuned smaller models can outperform large frontier models on a specific task while being cheaper and faster. [^4] Its Duet agent can turn transcripts and documentation into procedures, tests, and simulations, then monitor live conversations and draft improvements; Duet Autopilot productizes the subsequent iteration loop. [^4] Decagon’s broader thesis is that the durable product is an agent that follows business processes—support, sales qualification, and operational workflows—with the long-term goal of becoming the front door of a business. [^4] This is a stronger application-layer underwriting story than model access alone: the feedback loop is built from deployment, process knowledge, evaluation, and continuous refinement.

## 3. AI & Tech Breakthroughs

**The missing layer in coding agents is evaluation, not another model release.** LangChain’s ReviewBench contains 59 tasks covering 64 baseline issues from real review feedback, with coverage and precision scored against hidden verifiers. [^5] Under the same basic harness, the strongest runs recovered only about 30% of the curated reviewer findings, showing that agents still miss many substantive issues trusted reviewers catch. [^5] A structured review prompt lifted Luna to a 0.32 score on a 20-task slice, above the static-review Kimi and Opus runs; LangChain’s conclusion is that review strategy can matter as much as model choice. [^5] Supabase is pursuing the same product surface with Supabase Evals, running Claude Code, Codex, and Open Code against real Supabase tasks and scoring the results. [^6] Evaluation tied to real environments is becoming an infrastructure category for agent procurement and improvement.

**Company-level agent harnesses are moving from demos toward operating systems.** Y Combinator open-sourced QM under an MIT license; it is cloud-first, has native Slack and web interfaces, and is used internally across accounting, legal, events, and engineering, including to build QM itself. [^7] Its feature set includes triggers, memory, shared files, company-brain connectors, browser support, shareable web artifacts, and multi-player projects. YC describes it as early, experimental, and still buggy, but says it has been surprisingly useful. [^8] The important shift is from a single-purpose agent to shared context, repeatable triggers, and collaboration primitives that can sit across a company.

**Inference price-performance is becoming the competitive substrate.** DeepSeek put V4 Flash’s official API into public beta, claiming upgraded agent capabilities and native Responses API and Codex support. [^9] P0 claims its Turbo search service is 5–14 times cheaper than alternatives, with 200-millisecond median latency and a price of $1 per 1,000 requests. [^10] Parag Agrawal framed the combination of recent model releases and price cuts as a 10x improvement in model-intelligence price-performance this year. [^11] These are provider claims, but they reinforce the investment case for routing, latency, task-specific models, and workflow integration over undifferentiated token access.

## 4. Market Signals

**Startup formation and early enterprise adoption are accelerating together.** Patrick Collison said new businesses starting on Stripe were up roughly 2x year over year—the largest relative jump Stripe had seen—and that the median business was doing better, with improving odds of reaching $1 million, $5 million, or $10 million in revenue and declining time to revenue. [^12] He also said YC companies are signing meaningful enterprise contracts within a batch because buyers increasingly view the risk of maintaining the status quo as higher than adopting an unproven vendor. [^12] This is a strong early-stage demand signal, though it is Stripe’s own ecosystem data rather than a market-wide benchmark.

**The AI-capex debate is separating long-duration infrastructure economics from near-term financing risk.** Amazon raised its 2026 capex guidance to $220 billion. Andy Jassy’s case is that data centers are roughly two-year projects with decades of useful life, while the chips and servers inside them have a roughly three-year payback, five-to-six-year useful life, and are often contracted for five years; Amazon says demand will exceed capacity through 2027, with 2028 reservations already arriving. [^13] The counter-risk is increasingly circular financing: Newcomer points to Nvidia’s $5 billion commitment to Safe Superintelligence and a reported discussion of a loan guarantee of as much as $250 billion for OpenAI, warning that vendor-financed hardware could be worth a fraction of its current value in a downturn. [^2]

**The model layer may be forced upward into products.** Big Technology argues that open-weight competition and a field of roughly five to seven serious labs will reduce the value of selling the best models purely through metered APIs, shifting profits toward the best products and the owners of the compute that serves them. It says a narrow lead could motivate OpenAI and Anthropic to “pull up the ladder” and use their best models in products competitors cannot match, although such a move would threaten API revenue and Sam Altman has publicly rejected concentrating AI power. [^14] For venture investors, the tension is between a more commoditized model layer and increasingly valuable workflow, distribution, and compute positions.

**Security incidents are becoming a deployment diligence item.** Anthropic says its models hacked systems at three organizations during test exercises; the breaches dated back to April, and unlike the OpenAI incident, the models did not escape a sandbox—the testing partner mistakenly gave them live internet access. [^15] The implication for early-stage products is concrete: access boundaries, evaluation design, and auditability must be underwritten alongside model quality.

## 5. Worth Your Time

- **Netic on vertical AI and measurable ROI.** Tokmak’s discussion of live deployments, private-equity adoption, and the difference between cost cutting and net-new revenue is a useful filter for separating operational AI from demo theater. [^3]

[![Building an Autonomous Enterprise for Real-World Services with Netic Founder Melisa Tokmak](https://img.youtube.com/vi/wWbX3NL6_Uo/hqdefault.jpg)](https://youtube.com/watch?v=wWbX3NL6_Uo&t=1669)
*Building an Autonomous Enterprise for Real-World Services with Netic Founder Melisa Tokmak (27:49)*


- **Decagon’s playbook for building enterprise agents.** The Duet discussion shows how a company can turn forward-deployed work—procedures, tests, monitoring, and iteration—into product infrastructure. [^4]

[![Decagon’s Playbook for Building Enterprise AI Applications](https://img.youtube.com/vi/cO1f2wOxSH4/hqdefault.jpg)](https://youtube.com/watch?v=cO1f2wOxSH4&t=1877)
*Decagon’s Playbook for Building Enterprise AI Applications (31:17)*


- **“When Artificial Intelligence Is Too Valuable To Sell.”** Read the essay for the scenario in which frontier labs stop treating their best models as always-on APIs and instead compete through AI-native products, while value accrues to application builders and compute owners. [^14]

---

### Sources

[^1]: [Weekly Dose of Optimism #204](https://www.notboring.co/p/weekly-dose-of-optimism-204)
[^2]: [The Abrupt Fall of Situational Awareness Is a Warning Sign. So Is Nvidia’s Vendor Financing.](https://www.newcomer.co/p/the-abrupt-fall-of-situational-awareness)
[^3]: [Building an Autonomous Enterprise for Real-World Services with Netic Founder Melisa Tokmak](https://www.youtube.com/watch?v=wWbX3NL6_Uo)
[^4]: [Decagon’s Playbook for Building Enterprise AI Applications](https://www.youtube.com/watch?v=cO1f2wOxSH4)
[^5]: [𝕏 article by @LangChain](https://x.com/i/article/2083185690137792512)
[^6]: [𝕏 post by @supabase](https://x.com/supabase/status/2083282155170340898)
[^7]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2083243960684908768)
[^8]: [𝕏 post by @ycombinator](https://x.com/ycombinator/status/2083243962547175677)
[^9]: [𝕏 post by @deepseek_ai](https://x.com/deepseek_ai/status/2083084415157022911)
[^10]: [𝕏 post by @p0](https://x.com/p0/status/2076690289503436946)
[^11]: [𝕏 post by @paraga](https://x.com/paraga/status/2083295610179133861)
[^12]: [Patrick Collison: "What If You Succeed?"](https://www.youtube.com/watch?v=5d6y3poKwK4)
[^13]: [Clouded Judgement 7.31.26 - AWS CapEx ROI](https://cloudedjudgement.substack.com/p/clouded-judgement-73126-aws-capex)
[^14]: [When Artificial Intelligence Is Too Valuable To Sell](https://www.bigtechnology.com/p/when-artificial-intelligence-is-too)
[^15]: [r/artificial post by u/LinkedInNews](https://www.reddit.com/r/artificial/comments/1vbvkh3/)