The AI Frontiers article (reproduced beneath) builds on a earlier Asimov Addendum article written by Tim O’Reilly, entitled: “Disclosures. I don’t assume that phrase means what you assume it means.” I (Ilan) assume it’s vital to first very briefly undergo components of Tim’s authentic piece to assist recap why we—at the AI Disclosures Venture—care about protocols in AI markets:
On the AI Disclosures Venture, we’re more and more coming to see disclosures by way of the lens of networking protocols and requirements. Each networking protocol can be considered a system of disclosures. However these disclosures are excess of only a warning label, or a mandated set of experiences. They’re a type of structured communication that permits impartial, decentralized motion.
Tim then notes why this issues for AI’s “market construction”:
The race for first mover benefit by the massive centralized AI suppliers like OpenAI and their enterprise mannequin of offering AI entry by way of metered API subscriptions suggests a hub and spoke railroad design, whereas a world of open weight AI fashions related by new modes of standardized communication might look extra like a street system, or at present’s World Broad Net.
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If we would like a world the place everybody, not simply AI mannequin builders and people constructing on prime of their centralized networks, is ready to innovate and to supply their work to others with out paying a tax to entry centralized networks, we’d like a system of disclosures that permits interoperability and discovery.
On this strategy, protocols, as a sort of disclosure, can architect more healthy AI markets, not after issues are already too far gone, however by way of it working as foundational “guidelines of the street… that allow interoperability”:
Briefly, we have to cease pondering of disclosures as some sort of mandated transparency that acts as an inhibition to innovation. As an alternative, we must always perceive them as an enabler. The extra management rests with techniques whose possession is restricted, and whose conduct is self and opaque, the extra permission is required to innovate. The extra we’ve got constructed “the rule of regulation” (i.e. requirements) into our techniques, the extra distributed innovation can flourish.
Now, on to the primary course!
Protocols and Energy
As AI fashions turn out to be commodities, tech giants are racing to lock in customers by hoarding their information. Open protocols, backed by open APIs, can facilitate broad information sharing and more healthy AI markets.
Initially revealed in AI Frontiers: https://ai-frontiers.org/articles/open-protocols-prevent-ai-monopolies
Can we head off AI monopolies earlier than they harden? As AI fashions turn out to be commoditized, incumbent Huge Tech platforms are racing to rebuild their moats on the utility layer, round context: the sticky user- and project-level information that makes AI purposes genuinely helpful. With the fitting context-aware AI purposes, every further user-chatbot dialog, file add, or coding interplay improves outcomes; higher outcomes appeal to extra customers; and extra customers imply extra information. This context flywheel—a wealthy, structured user- and project-data layer—can drive up switching prices, making a lock-in impact when amassed information is trapped throughout the platform.
Protocols forestall lock-in. We argue that open protocols—exemplified by Anthropic’s Mannequin Context Protocol (MCP)—function a robust rulebook, serving to to maintain API-exposed context fluid and to stop Huge Tech from utilizing information lock-in to increase their monopoly energy. Nonetheless, as an API wrapper, MCP can entry solely what a selected service (comparable to GitHub or Slack) occurs to show by way of its API.
To totally allow open, wholesome, and aggressive AI markets, we’d like complementary measures that guarantee protocols can entry the complete spectrum of consumer context, together with by way of:
- Assured entry, for licensed builders, to user-owned information, by way of open APIs at main platforms.
- Transportable reminiscence that separates a consumer’s agentic reminiscence from particular purposes.
- Knowledge utilization guardrails governing how AI companies can leverage consumer information.
Drawing on the instance of open-banking rules, we present that safety and information requirements are required for any of those proposals to be realized.
Architecting an open, interoperable AI stack by way of the protocol layer is about supporting broad worth creation reasonably than worth seize by a couple of corporations. Coverage efforts comparable to the EU’s Basic-Function AI Code of Observe do matter; however, finally, it’s software program structure that almost all instantly and decisively shapes market outcomes.
Protocols—the shared requirements that permit completely different techniques talk with each other—operate as a deeper de facto regulation, enabling impartial, decentralized, and safe motion in digital markets.
From Commoditized Fashions to Context-Wealthy Purposes
From fashions to companies. In a fevered race to blitzscale its option to AI dominance, OpenAI took an early lead. ChatGPT grew to become the fastest-growing utility in historical past, and it was simple to imagine that the following step was to show it right into a platform. OpenAI tried to turn out to be a developer platform, first with plugins after which with its GPT Retailer.
But it surely hasn’t all gone in accordance with plan. OpenAI’s fashions don’t appear so particular anymore. Open-source fashions like Kimi K2 (by Moonshot AI) have aggressive capabilities and are free to make use of. Sensing the turning tide, application-specific firms like Perplexity struck gold by taking off-the-shelf fashions from a number of suppliers, scaffolding them for particular makes use of, and charging for premium entry whereas avoiding vendor lock-in. Cursor, an AI‑first code editor, went from $0 to over $100 million ARR in 18 months, proof that context‑pushed retrieval-augmented technology (RAG), with a local AI design, can beat incumbents sitting on extra consumer information. Entrance-end customers can now simply select their most popular mannequin inside these purposes. And, utilizing platforms like OpenRouter, builders may even change fashions dynamically in response to pricing or options.
Context rising. As basis fashions commoditize, competitors is shifting up the stack, to the applying layer, the place proprietary consumer and challenge information—generally known as context—is the key sauce. Tech giants are racing to surround and personal this context solely: dialog histories, reminiscence shops, workspaces, codebases, paperwork, and the rest that helps their brokers predict and help higher. OpenAI, Google, and different mannequin distributors lean on chatbot interplay logs as sources of persistent reminiscence, whereas utility specialists like Anysphere (which makes Cursor) and Perplexity equally harness challenge and consumer information to spice up their fashions’ usefulness.
This forces a vital choice in the marketplace: will AI purposes develop based mostly on closed requirements that permit a couple of gatekeepers dictate phrases and extract outsized rents, or on open requirements that hold context transportable and structure permissionless?
The early open net. The stakes are excessive. Born on open protocols, the net advanced into ecosystems of purposes dominated by Amazon, Google, and Meta. At first, they beat rivals just by working higher. Google was the most effective at matching searchers with info and adverts; Amazon surfaced the most effective merchandise at low costs; and Fb matched its customers with a novel feed crafted solely from content material shared by their buddies and folks they selected to observe.
From innovation to extraction. However success conferred sturdy energy that was abused. As development slowed, the successful firms shifted from creating worth to extracting it. In our previous work, we described this course of utilizing the language of financial rents: winners first acquire “Schumpeterian rents” for innovation, however, as soon as markets mature, these flip into extractive rents geared toward preserving dominance and squeezing customers and builders. Cory Doctorow frames this course of vividly as “enshittification.” AI’s enshittification might contain weaker security guardrails, larger costs, much less consumer privateness, and lower-quality info or agentic help. Briefly, when industrial incentives go unchecked, fashions get tuned to serve suppliers’ pursuits over these of customers.
Makes an attempt by OpenAI to construct a platform by locking in builders and customers resemble Fb’s failed try and construct a platform. However, as Invoice Gates is stated to have commented: “This isn’t a platform. A platform is when the financial worth of all people that makes use of it, exceeds the worth of the corporate that creates it. Then it’s a platform.” That sort of platform is sort of at all times enabled by open requirements. Against this, when an organization blocks others from linking appropriate merchandise to its ecosystem, it incentivizes prospects to make use of a number of companies directly (`multi-homing’), and invitations additional scrutiny from regulators.
The promise of protocols. Anthropic has taken a special route, creating MCP as an open protocol, a shared algorithm that anybody can use without cost. MCP standardizes how AI purposes request info and actions from exterior companies, thereby facilitating equitable developer entry to exterior instruments and information context. That is how networked markets develop: by enabling an structure of participation by way of which each and every new entrant makes the market extra invaluable for everybody else.
MCP’s take-up has been explosive. In the present day there are nicely over 5,000 MCP servers that can hook up with the a whole lot of AI apps which have built-in MCP. Confronted with fast adoption by third-party builders, AI mannequin builders like OpenAI and Google have introduced that they too will assist MCP. However these similar incumbents are already pushing again.
How Consumer Context Is Powering a New Period of Tech Monopolies—and Competitors
Context creates worth. AI techniques thrive on context: the consumer information that lets an AI system tailor its conduct to customers, their requests, and the duties at hand. When correctly mined, this consumer information permits for customized and environment friendly predictions. Consider a context-free, factory-settings AI mannequin as a borrowed cellphone: the {hardware} is highly effective, however, with out your contacts, messages, location, and logins, it will probably’t actually provide help to.
Context has many layers: throughout time, as a residing “state,” such that every consumer immediate builds on what got here earlier than; and throughout individuals, as a multi-user setting (say, in a Slack thread or collaborative doc). We emphasize two layers: micro-context captures whom the system helps proper now (related to their preferences, language, and present question). However, macro-context covers the duty setting, because the exterior body that shapes what a smart reply seems to be like. This consists of challenge information and stay information feeds.
Huge AI firms are utilizing context to develop their moats and lock in customers by way of no less than two approaches. The primary is thru product bundling. Examples embody OpenAI’s push into search, analysis, and coding (together with by way of acquisitions); Google’s threading Gemini into Workspace; Microsoft’s embedding Copilot throughout its 365 productiveness apps. Bundling aggregates the info floor and raises switching prices.
The second is thru constructing context as a central product characteristic. OpenAI now presents persistent reminiscence that shops private particulars (e.g., “has a baby” or “recognized with ADHD”) to form future replies. Meta has introduced it can accumulate cross-site consumer information to personalize its AI assistants. Google now remembers your writing fashion, so it will probably tune its AI-generated Gmail replies. By binding the app and its context to the mannequin, firms lock in customers and starve rivals. Such bundling is fertile floor for enshittification.
Importantly, this course of depends on Huge AI firms’ gathering express consumer alerts—their prompts, docs, API calls—and distilling them into an inferred, implicit preferences profile that lets their mannequin ship extra related, environment friendly predictions inside every consumer’s distinctive workspace.
Can Protocols Create a Stage Enjoying Discipline?
The MCP pipeline. Anthropic’s MCP standardizes how AI purposes request instruments, information, and actions from exterior companies by way of a common adapter. As an alternative of customized integrations for every pairing (Cursor → GitHub; Claude → Google Drive), any AI app (every one an MCP shopper) can use any MCP-compatible service (or MCP server), making fashions extra interchangeable. MCP additionally creates an agentic interface that permits an AI agent to determine what to do, based mostly on the language of duties, not endpoints. This reduces the MxN integration tax, permits small corporations to hire reasonably than construct tooling, and weakens vertical exclusives.
As a result of MCP is client-agnostic, any AI app can use any exterior service, which in flip makes switching between fashions far simpler — both by switching between mannequin service suppliers that assist MCP, or by constructing an impartial MCP shopper and utilizing any mannequin service. When an AI app’s context is transportable, fashions turn out to be extra interchangeable.
MCP is the final word unbundler of context: any appropriate AI app can attain any service that exposes an MCP server, permitting an enriched immediate to then be despatched to the mannequin. However companies should nonetheless choose in, by making their content material obtainable by way of APIs.
This shifts the aggressive gravity “up the stack,” away from the mannequin builders and to the applying that develops the successful context flywheel. App-level information portability and governance—together with pricing, permissioning, and any preferential entry into Huge Tech–managed information sources—then turns into the brand new battleground.
Though MCP reduces integration friction, interoperability alone doesn’t guarantee market competitors. We’ve seen this earlier than: open protocols like HTTP (for net looking) and SMTP (for e-mail) enabled permissionless entry of latest purposes, but markets nonetheless tipped. Google is now the dominant e-mail and browser supplier due to its superior merchandise and cross-app integrations.
MCP’s Influence on the AI Market So Far
Incumbents have rushed to insert AI into each legacy product: the quickest go-to-market technique with the shallowest integration. Meta surfaces an assistant in practically each app. This has solely made constructing cleaner, MCP-enabled purposes much more engaging. AI-native instruments like Perplexity supply additional encouragement to builders, displaying that customers will decide a personalized expertise over a retrofitted one (just like the AI-layered Google Search).
Unsurprisingly, the variety of new MCP servers has rocketed, as we famous earlier. Nonetheless, such integrations may be boosting utilization of incumbent mannequin builders’ chatbots as they acquire entry to extra instruments. MCP’s impression has been impeded by its weak safety. MCP servers’ exterior authentication and authorization stay a cussed MxN integration drawback. Furthermore, for repeated manufacturing workflows, code-based frameworks could also be extra environment friendly than an inference‑solely workflow.
Lastly, there are early indicators that AI mannequin builders could resist interoperability extra broadly, regardless of the elevated utilization it generates for them, if it finally ends up reinforcing the context moats for utility builders. Anthropic briefly lower off the coding utility Windsurf’s direct (first-party) entry to its high-performing Claude fashions. Windsurf was rising too fashionable and was set to be acquired by OpenAI, a direct competitor to Anthropic.
MCP Versus Walled Gardens: The API Gatekeeping Downside
APIs are the gateway by way of which an MCP shopper—the AI purposes—can entry third-party information and instruments, thereby breaking down a platform’s “walled backyard” of proprietary companies and datasets. However MCP can liberate context solely when a third-party service presents a sufficiently wealthy API (and retains it open). As a result of platform house owners management these APIs, they’ve an incentive to constrain what MCP can contact, to guard their aggressive edge. This manifests in two methods:
- Entry threat. Providers can merely shut off API entry fully, or they will tremendously degrade entry. Latest API paywalls and shutdowns at Reddit, Twitter, and Meta present how entry can vanish in a single day. Enterprise companies like Salesforce (which owns Slack), Atlassian, and Notion are now limiting API entry by Glean (a context platform) whilst they launch competing merchandise. In the meantime, Slack’s new API modifications (supposedly to restrict how LLMs are capable of entry the app) will hurt builders basically.
- Context-depth threat (the “personalization hole”). Platform APIs expose posts and information however not often the behavioral profiles that energy their very own personalization, leaving newcomers with a chilly‑begin handicap. Meta, for instance, personalizes its personal chatbot with Fb and Instagram historical past, however it presents third events neither its Graph API to fetch that full profile nor entry to detailed points of customers’ express and implicit (inferred) profiles. Equally, OpenAI’s “reminiscence” characteristic is confined to ChatGPT. OpenAI doesn’t permit builders to entry a consumer’s “reminiscences” through an API, even with the consumer’s prior consent.
To Save AI from Enshittification, Help Protocol-Stage Interventions
Bettering protocols for the AI age. To interrupt API gatekeeping in AI markets, we’d like an structure that helps user-sanctioned information portability with the intention to improve third-party developer entry. Right here, portability means finish customers’ capacity to learn and switch their information throughout platforms—or to permit different builders to take action on their behalf. When portability is common, builders can entry the identical context (by way of MCP or any API) with out negotiating bespoke offers. To operationalize this strategy for AI markets, we advocate:
- Open API entry for main platforms. If the info comes from the consumer, the consumer—and any developer the consumer authorizes—ought to be capable to take it elsewhere. We advocate requiring that, with consumer consent, main platforms expose this user-owned contextual information by way of APIs to accredited builders at zero value. We suggest beginning with the platforms that management probably the most consumer context: “gatekeepers” designated by EU standards, plus main AI mannequin suppliers.
Such an strategy might draw inspiration from the EU’s open-banking regulation (particularly, its Second Fee Providers Directive, or PSD2), which holds that banks should present licensed fintechs with free, real-time entry to core account information and fee capabilities. Licensed builders should first get hold of a license by displaying correct safety and information requirements. In contrast to banking’s standardized data, although, AI context spans code repositories, conversations, behavioral patterns, and preferences. Within the case of AI, markets and regulators would want to provide you with a method of defining “core consumer context” for these varied information sorts and platforms.
- Reminiscence as a conveyable service. Customers’ AI “reminiscence” must be accessible throughout platforms through APIs, with market-driven safety requirements embedded within the technical structure. Such MCP servers already exist, even when AI purposes don’t assist it.
The problem is much less technical than socio-economic. Reminiscence is deeply private and requires safe data-handling, but AI markets at the moment lack requirements and accreditation in these areas.
A market-driven strategy can be to embed these safety requirements into technical structure, as is finished with the FDX API normal for US open banking. Such embedding permits for safe and standardized sharing of economic information between banks and third-party builders. Safety necessities like end-to-end encryption, OAuth-controlled entry to client-side keys, and granular topic-by-topic permissions are at the moment past MCP’s scope. However FDX’s safe and common API reveals what is feasible.
- Secure personalization, with out information exploitation. Open APIs rely on customers’ trusting builders to deal with shared context responsibly. Business-specific information utilization guidelines would additionally weaken incumbents’ benefits whereas creating safer applied sciences. Such utilization guidelines might begin with:
- Knowledge firewalls. We advocate defending intimate consumer conversations from industrial concentrating on. An AI utility leveraging a identified consumer desire like “is vegetarian” for restaurant suggestions is helpful; however exploiting therapy-like conversations for manipulative promoting should be prevented.
- Erasure rights. Customers ought to be capable to overview, edit, or delete their desire profiles and reminiscences at any time. ChatGPT already largely presents this.
- Privateness defaults. For delicate queries, we advocate that AI companies default to a personal mode, with out long-term reminiscence enabled or advert concentrating on, except customers explicitly choose in to those settings for such queries.
In the end, management over consumer context—not uncooked mannequin energy—will determine who wins the AI industrial race. Open protocols can hold context fluid between rivals, however they’re solely as efficient as the info (and instruments) that they will securely entry. The selection is ours: design aggressive AI markets round open rules, or settle for a brand new technology of platform monopolies.
Because of Alex Komoroske, Chris Riley, David Soria Parra, Guangya Liu, Benjamin Mathes, and Andrew Trask for studying and/or commenting on this text. Any errors are ours.
