Why OpenAI Should Be Scared of Open Source

Summary

• Robot Ventures led Nous Research’s $90 million round on a thesis that Hermes can own AI’s user relationship. Tarun Chitra compares the opportunity to investing in Phantom or FOMO: harnesses control preferences, context and access to users, while inference providers supply execution. If AI develops crypto’s market structure, he expects those two layers to capture value while routers and models face pressure.
• Hermes’s product advantage comes from years of research compounding into better defaults. Tarun argues that Nous’s work on training, inference and context management lets it absorb decisions that OpenClaw leaves to users. His OpenClaw analogy: “Here’s all the parts to this car. Go build the car.” Hermes instead learns preferences, generates skills and supports work across models.
• The useful inheritance from crypto is resource-conscious engineering, with token-first projects supplying the cautionary history. Tarun credits Chinese model companies with choosing better optimizations than decentralized-training teams, prompting pivots in 2024–2025. Early token agents anticipated demand but delivered weak products—often “meme coins with more steps.”
• Unsolicited robotics adoption was a stronger diligence signal than another monetization story. Over three to four months, Tarun examined usage and forks, finding Chinese manufacturers embedding Hermes in small robots without Nous knowing. He treats those forks as evidence of product quality and brand attachment; “retention and stickiness” could matter more in a downturn, even when individual forks generate no revenue.
• Enterprise customization gives Hermes a second distribution path alongside its consumer community. Businesses can add permissions, compliance, privacy and internal data connections. Tarun says Microsoft and NVIDIA participated in the round; he expects Hermes to be among the open-source harnesses available through Microsoft and says NVIDIA installs it by default on DGX Spark machines. Tommy sees an owned AI stack as a business asset, but Tarun checks his enthusiasm about ready-made enterprise demand: “nothing’s in the bag until the contract’s signed.”
• A low advertised inference price can conceal a higher total agent bill. In Tarun’s GLM 5.2 illustration, a nominal 95% discount becomes $1.05 over 21 queries when caching savings disappear, versus just over $1 from the higher-priced provider with perfect caching. He reports statistical evidence of providers gaming this mechanism: “you’re probably caching, but you’re not passing on the price benefits of caching.”
• The longer-term opportunity is to supply the harness underneath other companies’ agents. Tarun calls Hermes the “white label harness for anyone,” with security, context management and brand credibility supporting adoption. His categorical belief is that every SaaS company needs a harness or router to participate in token order flow, where aggregated demand can earn rebates and revenue shares.

Deep dive

1. Crypto’s AI survivors learned to leave tokens behind

• Tommy introduces Robot Ventures’ lead in Nous Research’s $90 million round and says Delphi is a co-investor. Tarun’s broader claim is that crypto participants’ “fingerprints” are everywhere in the recent open-source and open-weight surge, particularly among teams applying engineering lessons without attaching a token.

• The initial 2023 pitch often assumed mining infrastructure could become an AI network with minimal adaptation. Tarun argues that language models introduced difficulties image models did not, and that no one seemed to want a decentralized image model. Tokens also let projects attract valuations before demonstrating utility. His description—“meme coins with more steps”—includes well-meaning teams whose ideas simply did not work.

• Tarun’s own position changed: he once considered decentralized systems scams, then spent four or five years understanding why blockchains worked despite conventional distributed-systems objections. That left room for a roughly “1% chance” that genuine architectural advances could overcome problems in decentralized training. He was more optimistic about inference than training.

• Nous stood out through specific research: YaRN for longer context, compression-oriented embeddings, and low-bandwidth training through DisTrO. Tommy adds that Nous did synthetic model evaluation early and recalls a distributed training run of “40 billion parameters or something like that”; the number remains uncertain. Tarun’s criterion was disciplined work on bounded problems, with no token and no promise that one invention would solve everything. He also distinguishes Nous from Prime Intellect, which he says was less focused on training and hedged by becoming a GPU reseller.

• Tarun’s broader reasoning is that distributed systems succeed through many separate optimizations, not one breakthrough. He uses Bitcoin as an example: hash security, difficulty adjustment, inflation and bandwidth each pose distinct technical problems that must work together. He says Nous and, in many ways, Prime Intellect were unusual in bringing that breadth of focus to decentralized AI.

2. Chinese model companies chose the more productive constraints

• Tarun compares decentralized-training teams with Chinese model developers starting around the same period. Both faced resource constraints, but of different kinds: uncertain participation, bandwidth and payment on one side; weaker chips and infrastructure on the other. Chinese companies also built cheaper, Mercor-style data-collection operations. Out of thousands of possible optimizations, “the Chinese companies’ 100 were just better.”

• Tommy pushes back that DeepSeek’s work was genuinely innovative. Tarun agrees: his argument concerns which constraints produced competitive results. Once a model becomes good enough, agentic and recursive improvement can accelerate progress, making catch-up harder. That helps explain why decentralized infrastructure teams began pivoting in 2024–2025.

• Early crypto agents repeated the pattern: directionally right about agents, weak on execution. A year after their peak, Claude Code and Codex were expanding in capability and usage. Tarun points to Nous and, philosophically, OpenRouter as examples of crypto’s optimization ethos finding a place in non-token AI. He says Together AI started as a decentralized inference network, while distinguishing Corby’s approach from teams trying to launch a token.

3. OpenClaw demonstrated demand while exposing an execution gap

• Tarun calls OpenClaw “the right idea for a product” with the wrong execution. Its Android-like abundance of choices—models, reasoning, context and infrastructure—restricted who could use it. Tommy corroborates the experience: after building a personal agent in n8n, he tried OpenClaw and returned to manually connecting services because it felt so clunky.

• Tommy insists OpenClaw deserves credit for creating the category; Tarun agrees. The failure was turning personal experiments into transferable products. At an OpenClaw event around December or January, late 2025 or early 2026, Tarun saw unreliable demos whose configurations others could not readily download. One example was a cat-feeder project that would send a USDC payment. His test was blunt: “does this have a market size of one? Yes.”

• That creates room for second movers. Tarun attributes Claude’s late-2025/early-2026 uptake partly to accessibility and human touches that let nontechnical people build applications, while acknowledging OpenAI subsequently “came back with a vengeance.” He sees a parallel in Z.ai, Kimi and MiniMax learning from DeepSeek and changing reasoning, resource usage and attention designs.

4. Hermes’s advantage starts beneath the interface

• Hermes began as Teknium’s side project, prompted by liking OpenClaw’s concept while finding it unusable. Tarun argues Nous’s previous work supplied the foundation: training exposed data-collection problems; inference exposed orchestration and context-management problems. Tommy notes that Nous’s models had tens of millions of downloads. Those research years equipped the team to understand failures underneath an agent’s interface.

• Context portability is central. Tarun recalls expectations that OpenAI and Anthropic would create lock-in through proprietary context formats. He says OpenAI now supports open models in the same context, undermining that expectation. Nous had already concentrated on sharing context across models and agents: “interoperability is here,” in his blockchain terminology.

• Tarun’s explanation is cumulative rather than dependent on one architectural breakthrough. Successful distributed systems require “1,000 little optimizations”; he also describes the need for “10,000 tricks” that compound. Hermes benefits from understanding the layer below the product. OpenClaw’s contrasting philosophy was to hand users the components: “Here are all the parts to this car. Go build the car.”

5. The harness becomes AI’s wallet and preference engine

• Tarun’s “Maslow’s hierarchy of compute needs” starts with renting a GPU, which still leaves environment setup, memory management and DevOps. Together, Modal, Baseten and Fireworks move users toward served model endpoints. His crypto mapping is running your own node versus receiving an RPC endpoint: useful abstraction, but still some distance from an accessible consumer product.

• A harness is closer to a wallet such as Phantom or FOMO: it manages state, history, preferences and sensitive credentials, with defaults that reduce mistakes. Tarun puts OpenClaw nearer MetaMask or MyEtherWallet because setup still demands substantial user judgment. The distinction determines the reachable audience, just as using a wallet differs from manually submitting a transaction through Etherscan.

• Hermes’s approach includes learning behavior, generating skills from usage and managing model and inference-spending decisions. Tarun says it was built around the idea that a model router would exist. Tommy’s objection is practical: most users cannot distinguish reasoning settings or model choices, but recognize a bad answer—and then conclude the agent itself is broken.

• The philosophical difference changes architecture: which choices reach the user, which preferences are learned, and when clarification is necessary. Tarun calls this “the difference between product engineering and back-end engineering.” Nous’s design sensibility matters alongside its technical depth; the team once mocked as an expensive merchandise company had already shown attention to its audience’s tastes.

6. Packaged open source expands the reachable market

• Tommy describes Hermes as the open-source, non-lab-owned alternative within the consumer-agent set. Tarun qualifies the distinction: DIY competitors exist, but Hermes is “open source but packaged.” His analogy is Ubuntu’s defaults, updates and accommodation of closed-source components. Likewise, Hermes accepts Claude and OpenAI API keys rather than requiring ideological purity about the models underneath.

• Tarun credits Pi with taking over the DIY community and says it “probably has the most forks.” Its simplicity makes it useful for teaching harness design—including in an introductory computer-science class his brother described—but builders must supply adapters themselves. Hermes targets prosumers and progressively broader users while retaining extensibility. Tarun still wanted evidence developers used its source: an open-source label without actual reuse would concern him.

7. Robotics forks supplied the strongest diligence signal

• During three to four months of diligence, Tarun combined OpenRouter usage data with inspection of Hermes forks. Public application and model leaderboards made analysis resemble blockchain data research. The forks answered another question: did builders value how Hermes was constructed? He treats this as brand sentiment expressed through engineering choices, distinct from revenue or immediate monetization potential.

• His strongest example was small Chinese robotics manufacturers using Hermes as an embedded interface. They had to keep costs down because hardware consumed their budgets, and wanted to minimize inference costs. A robot approaching a dryer and folding clothes might need separate models for movement, spatial planning and dexterity, with different reasoning and action requirements. Tarun says evaluations have shown that separating tasks across models often performs better than relying on one model.

• Manufacturers adapted Hermes with device-specific tool calls such as moving a hand or head. Nous had not even known some existed: “We didn’t even know this person forked it.” Tarun saw unsolicited adoption as product and brand fit. Amid what he says is a market where “half of the ARR numbers are fake,” this attachment mattered because “retention and stickiness” could become more important during a downturn.

8. Enterprise expansion adds customization and distribution

• Tommy’s ownership thesis is that an organization’s architecture, data, memory, hosting and interactions accumulate as an asset, while customization can produce distinctive outcomes. Tarun describes Nous’s enterprise work in concrete terms: businesses can clone Hermes, connect databases, and add privacy, compliance and permissioning layers. They retain a common foundation while adapting it to internal requirements.

• Tommy argues a large consumer community supplies advocates inside companies and effectively puts go-to-market “in the bag.” Tarun immediately qualifies that: “nothing’s in the bag until the contract’s signed.” He agrees internal champions help, while maintaining the distinction between widespread individual adoption and closed enterprise business. Nous is pursuing enterprise expansion alongside its consumer and open-source community.

• Tarun reports growing Portal subscriptions and OpenRouter usage, saying Nous had passed 2 trillion tokens across apps on OpenRouter. He also describes tens of millions of users and, “if I remember correctly,” tens of millions of unique GitHub accounts accessing them daily. He acknowledges multiple accounts are possible and presents GitHub identity as an imperfect measure.

• Tarun says Microsoft and NVIDIA both participated in the round. He expects Hermes to be one of the main open-source harnesses available through Microsoft, and says NVIDIA installs it by default on DGX Spark machines. These relationships support the enterprise push while the community continues supplying adoption from the consumer side.

9. Cheap inference can become expensive when caching is gamed

• OpenRouter connects applications requesting models with providers quoting input, output and cached-token prices. Tarun maps the application to a wallet, the router to an exchange or DEX aggregator, the model to a protocol, and the provider to a validator or liquidity provider. Cached tokens can be “hundreds or thousands of times cheaper” because they avoid repeated computation.

• Providers are not selected solely by lowest price. Tarun describes probabilities incorporating quoted prices and a quality score covering factors such as uptime and throughput. His question was whether providers could advertise attractive rates to win allocation, then misrepresent how much they cache. Without cryptographic verification of cache accounting, a provider’s report determines how much of that benefit reaches the customer.

• In Tarun’s GLM 5.2 illustration, Z.ai quotes $1 per million tokens and cached tokens at 0.001 cents. With perfect caching, 21 queries cost roughly $1, just over $1. A rival advertises 5 cents per million, a 95% discount, but bills every repetition as uncached: 21 × $0.05 = $1.05. Agent follow-ups make the apparent bargain more expensive.

• Tommy asks whether the harm is worse memory or responses. Tarun clarifies that it is price: “you’re probably caching, but you’re not passing on the price benefits of caching.” He reports statistical evidence from experiments across providers. A provider may lose money on the first query but earn it back on repeated queries. Tarun also limits the analogy: equating this too closely with crypto front-running or MEV would be wrong, although it is marketplace manipulation.

10. Harnesses and inference providers could capture the economics

• Tarun’s investment thesis follows from that market structure: “All the money goes to the top and the bottom.” In crypto, he argues, wallets and validators or liquidity providers gained leverage while aggregators and protocols were squeezed. Applied to AI, the favored positions are harnesses and inference providers. “Hermes is like investing in Phantom or FOMO,” in his formulation.

• Models can attempt licensing monetization. Tarun describes BSL-style terms requiring fees above revenue thresholds; Tommy mentions Kimi, and Tarun says GLM added similar provisions with different terms. He says Kimi’s revenue cap is particularly low. But routers face low entry barriers as SaaS companies, including Vercel and Ramp in his examples, introduce marketplaces. They can become successful businesses while still facing margin compression from abundant competition.

• OpenRouter’s admission controls mean it is not permissionless or fully decentralized, Tarun emphasizes. Once admitted, providers compete in an open market. That differs from bundled services where users cannot see the infrastructure supplier or allocation decisions. Open models allow users to unbundle the harness, model selection, routing and inference provision that centralized coding products package together.

11. Hermes could become infrastructure beneath other companies’ agents

• A wallet analogy has limits: Hermes need not be the final consumer interface. Tarun’s “guess” is that harnesses become a “security and brand” and context-management layer underneath applications, including consumer bots like Instinct. Companies can build differentiated experiences while inheriting shared infrastructure. His robotics examples already show developers using Hermes as a foundation rather than an endpoint.

• Token aggregation supplies a possible revenue mechanism. Tarun says inference providers already offer payment-for-order-flow-like rebates: “if you guarantee me X billion tokens a day,” they can return part of the fee. Tommy asks whether individual users control routing. Tarun agrees they can choose providers directly, while Portal and other access paths create different arrangements for managing that demand.

• Looking a year ahead, Tarun’s belief is categorical: “every SaaS company either has to have a harness or be a router,” because participation in token order flow creates monetization opportunities. Hermes could be the “white label harness for anyone.” Existing companies bring users and data; they avoid starting from zero and depend on a foundation whose security reputation matters much like a wallet’s.

• His closing analogy is a fintech sharing revenue with the bank underneath it. Tarun says Muse is going to monetize in a crypto-like way by charging transaction fees on in-app purchases and sharing revenue with Stripe, Shopify and others. To Tarun, competing counterparties and shared order flow make these systems resemble crypto markets. Enterprise deployments, consumer use and applications built on Hermes are therefore separate potential growth paths for the same underlying harness.