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Two positions sit at the center of that thesis right now: Orbio (@orbiodotso, orbio.so), building an inference capital market, and Auto Protocols / protocol.link (@auto_protocols), building coordination for people and AIs. Both are early. Both are liquid. Both point at markets that can get very large if the core loop works.

Orbio — a capital market for inference

Every serious product now burns inference. Agents, copilots, research loops, customer workflows. The spend is real, recurring, and rising. The procurement layer is still primitive: subscriptions, prepaid credits, opaque markups, keys that do not move like capital.

Orbio treats inference as something that can be priced, held, and transferred.

On the product surface, Orbio is a gateway: one API key, hundreds of models, OpenAI-compatible and OpenRouter-style paths, measured overhead, prompts not stored—billing metadata only. Buyers purchase dollar-denominated credit and spend it through the relay. Holders of $ORBIO sit on the other side of the market. Trading activity funds credit. Unused credit can be offered into a liquidity book at a discount. Demand meets supply. Inference clears like a market instead of a subscription form.

That is the thesis in one line: inference becomes a capital market.

If agents buy and spend credit programmatically, if developers route spend through a liquid book instead of a static plan, and if fee flow keeps converting into usable compute, Orbio is not a meme with a FAQ. It is the spot market and clearing layer for a commodity every AI system needs. The upside case is not “another API wrapper.” It is owning the market structure around a cost center that will keep growing as long as models stay expensive and agents stay hungry.

Orbio is being built in public by Yash (@0x_aster), with a builder track record that includes work around nftperp. Lean team. Fast surface. The risk is obvious: fee-funded credit depends on real usage and durable demand, not only trading churn. The opportunity is equally obvious: if the book stays liquid and the gateway stays trusted, Orbio sits under every agent that needs to pay for thought.

Auto Protocols — coordination for people and AIs

Software let individuals ship. Crypto let capital coordinate without a firm. Agents will force a third layer: organizations that are not only human.

protocol.link — Auto Protocols — states the product plainly: a coordination platform for people and AIs. A shared vision. Discussion that becomes decisions. Decisions that become tasks. Tasks that move forward through members and agents. Pool skills, tools, and funding. Ship useful work with a community that includes non-human labor.

This is not another chat UI. It is infrastructure for groups that can include agents as first-class workers: humans setting direction, agents proposing and executing tasks, capital and attention pooled against a shared outcome.

The thesis: as agents get competent, the scarce layer is not another model. It is the protocol that lets humans and agents organize, decide, and ship together.

If that becomes default for online teams, DAOs, open-source efforts, and agent swarms, Auto Protocols is early infrastructure for a new kind of firm. The upside case is category-defining: the operating system for mixed human–agent organizations. The risk case is also clear: coordination products die when they are empty rooms. Distribution, trust, and real work loops matter more than manifesto copy.

Auto Protocols is being built by an ex-GMX developer — someone who has already shipped in environments where money, latency, and adversarial users are not theoretical. That background matters for a product that will eventually touch shared resources and automated execution.

Sizing the loops

The loops need a scale, not a slogan. Public market-size reports give the pool. They do not give Orbio or Auto Protocols a revenue line, a user count, a TVL, or a market cap. We will not invent those. What follows are illustrative paths—bear, base, bull—for a share of a growing spend pool. They are not predictions. Not token-price targets. Not AUM.

Orbio: share of the credit book

For inference, the closest public fit is foundation-model API and credit spend, not chips. Menlo Ventures’ 2025 report, The State of Generative AI in the Enterprise, puts U.S. enterprise generative AI at $37 billion in 2025, of which foundation-model APIs are $12.5 billion. Menlo notes that API spend rose from about $3.5 billion in November 2024 as production inference scaled. That scope excludes chips and the major clouds’ model-serving layers on AWS, GCP, and Azure.

Hardware-heavy inference estimates sit much higher—MarketsandMarkets has put AI inference near $106 billion in 2025 and $255 billion by 2030—and we treat those as upper context, not the TAM for a credit book. Secondary summaries of Gartner-style foundation generative-model spend have the category roughly doubling from about $11 billion to $23 billion between 2025 and 2026. The dollars scale if API spend grows. The question for Orbio is share of the routed layer.

Orbio’s wedge is a liquidity book and an OpenRouter-compatible relay for LLM credits. The scenarios below are illustrative annual GMV—credit spend that could route through a liquid book—on a $12.5 billion foundation-model API base. Not take-rate. Not fully diluted value. If the API pool grows, the same shares imply larger dollars.

  • Bear (~0.05% share): about $6 million GMV. Speculative churn. Weak real credit demand. Labs and incumbents keep the book.
  • Base (~0.5–1%): about $60–125 million GMV. A credible discount book. Developers and agents route habitually. The fee-to-credit loop works.
  • Bull (~5–10% of the routed, marketplace layer): about $0.6–1.3 billion GMV. The default liquid credit market for agents. A deep book. Durable usage.

What must be true: credits move because someone is shipping. The book is deeper than the token. Agents pay at the spot, not on a monthly invoice. If that fails, the bear case is the honest one.

Auto Protocols: share of executable work

Coordination is a younger, worse-measured category. Incumbents own the chat seats—Microsoft Teams, Slack. Auto Protocols is a protocol for executable work: identity, job, settlement, humans and agents in the same loop.

Adjacent public numbers are team-collaboration tools. Mordor Intelligence has that market near $20.9 billion in 2025 and about $42 billion by 2031. Grand View Research, widely cited, has it near $36 billion in 2024 and about $57 billion by 2030. As a mid-range for collab-adjacent spend that could become agent-mediated work, we use about $25 billion. That is not Auto Protocols revenue.

  • Bear (~0–0.02%): near-zero sustained jobs. Empty rooms. A manifesto without receipts.
  • Base (~0.2–0.5%): about $50–125 million. Crypto and AI-native teams run real jobs on-protocol.
  • Bull (~2–5%): about $0.5–1.5 billion and up. The default operating system for mixed human–agent organizations.

What must be true: a person or an agent posts work. Another takes it. The receipt is real. Repeat. If the protocol only describes coordination, the bear case is already written.

A liquid fund does not underwrite a slide that says TAM. It underwrites whether a loop can take a share of a spend pool that is already large and still growing—and whether that share is usage. The rest is theater.

Why both, why liquid, why now

These are not the same company. They are adjacent layers of the same stack.

Orbio answers: how does intelligence get paid for when machines are the buyers?

Auto Protocols answers: how do humans and machines decide and work together once intelligence is cheap enough to staff?

Baus takes liquid positions when founders are already in motion and the market structure is still early. We are not waiting for a polished Series A narrative. We are underwriting the loop — a share of a growing spend pool, not a deck TAM.

The 100x path on either name is the same shape: become default infrastructure for a spend or coordination habit that compounds with AI adoption. Most attempts will fail. A few will look obvious in hindsight. That is the early-stage liquid job.

If you are building in this stack — inference markets, agent-native organization, crypto rails under AI work — send the sharpest version of what you believe.

hello@baus.ai