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 unique 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 the lens of networking protocols and requirements. Each networking protocol can be regarded as a system of disclosures. However these disclosures are way over only a warning label, or a mandated set of stories. They’re a type of structured communication that permits unbiased, 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 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 highway system, or at this time’s World Vast Internet.
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If we wish 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 method, protocols, as a kind of disclosure, can architect more healthy AI markets, not after issues are already too far gone, however by it working as foundational “guidelines of the highway… that allow interoperability”:
Briefly, we have to cease pondering of disclosures as some type of mandated transparency that acts as an inhibition to innovation. As a substitute, we must always perceive them as an enabler. The extra management rests with techniques whose possession is proscribed, and whose habits is self and opaque, the extra permission is required to innovate. The extra we have now constructed “the rule of legislation” (i.e. requirements) into our techniques, the extra distributed innovation can flourish.
Now, on to the primary course!
Protocols and Energy
As AI fashions grow 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 grow to be commoditized, incumbent Large Tech platforms are racing to rebuild their moats on the software layer, round context: the sticky user- and project-level information that makes AI functions genuinely helpful. With the correct context-aware AI functions, 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 gathered information is trapped inside the platform.
Protocols forestall lock-in. We argue that open protocols—exemplified by Anthropic’s Mannequin Context Protocol (MCP)—function a strong rulebook, serving to to maintain API-exposed context fluid and to forestall Large Tech from utilizing information lock-in to increase their monopoly energy. Nevertheless, as an API wrapper, MCP can entry solely what a selected service (equivalent to GitHub or Slack) occurs to reveal by its API.
To completely allow open, wholesome, and aggressive AI markets, we’d like complementary measures that guarantee protocols can entry the complete spectrum of person context, together with by:
- Assured entry, for approved builders, to user-owned information, by open APIs at main platforms.
- Moveable reminiscence that separates a person’s agentic reminiscence from particular functions.
- Information utilization guardrails governing how AI companies can leverage person 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 the protocol layer is about supporting broad worth creation quite than worth seize by just a few corporations. Coverage efforts equivalent to the EU’s Normal-Function AI Code of Apply do matter; however, in the end, it’s software program structure that almost all instantly and decisively shapes market outcomes.
Protocols—the shared requirements that allow completely different techniques talk with each other—operate as a deeper de facto legislation, enabling unbiased, decentralized, and safe motion in digital markets.
From Commoditized Fashions to Context-Wealthy Functions
From fashions to companies. In a fevered race to blitzscale its approach to AI dominance, OpenAI took an early lead. ChatGPT turned the fastest-growing software in historical past, and it was simple to imagine that the subsequent step was to show it right into a platform. OpenAI tried to grow to be a developer platform, first with plugins after which with its GPT Retailer.
However it hasn’t all gone in line 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 era (RAG), with a local AI design, can beat incumbents sitting on extra person information. Entrance-end customers can now simply select their most well-liked mannequin inside these functions. 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 person and challenge information—often 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 anything 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 software specialists like Anysphere (which makes Cursor) and Perplexity equally harness challenge and person information to spice up their fashions’ usefulness.
This forces an important determination available on the market: will AI functions develop primarily based on closed requirements that allow just a few 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 online advanced into ecosystems of functions dominated by Amazon, Google, and Meta. At first, they beat rivals just by working higher. Google was the most effective at matching searchers with data 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 mates and folks they selected to observe.
From innovation to extraction. However success conferred sturdy energy that was abused. As progress 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 achieve “Schumpeterian rents” for innovation, however, as soon as markets mature, these flip into extractive rents aimed 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, greater costs, much less person privateness, and lower-quality data 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 to 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 type 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 clients 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 unique route, creating MCP as an open protocol, a shared algorithm that anybody can use free of charge. MCP standardizes how AI functions request data 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 which each and every new entrant makes the market extra priceless for everybody else.
MCP’s take-up has been explosive. At this time there are properly over 5,000 MCP servers that can connect with the lots of 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 help MCP. However these identical 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 person information that lets an AI system tailor its habits to customers, their requests, and the duties at hand. When correctly mined, this person information permits for personalised and environment friendly predictions. Consider a context-free, factory-settings AI mannequin as a borrowed telephone: the {hardware} is highly effective, however, with out your contacts, messages, location, and logins, it may well’t actually enable you.
Context has many layers: throughout time, as a dwelling “state,” such that every person immediate builds on what got here earlier than; and throughout folks, 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). Then again, macro-context covers the duty surroundings, because the exterior body that shapes what a wise reply appears like. This consists of challenge recordsdata and dwell information feeds.
Large AI firms are utilizing context to develop their moats and lock in customers by at the very least two approaches. The primary is thru product bundling. Examples embrace OpenAI’s push into search, analysis, and coding (together with by acquisitions); Google’s threading Gemini into Workspace; Microsoft’s embedding Copilot throughout its 365 productiveness apps. Bundling aggregates the information floor and raises switching prices.
The second is thru constructing context as a central product function. OpenAI now gives persistent reminiscence that shops private particulars (e.g., “has a baby” or “identified with ADHD”) to form future replies. Meta has introduced it should acquire cross-site person information to personalize its AI assistants. Google now remembers your writing fashion, so it may well 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 Large AI firms’ gathering specific person indicators—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 person’s distinctive workspace.
Can Protocols Create a Degree Taking part in Area?
The MCP pipeline. Anthropic’s MCP standardizes how AI functions request instruments, information, and actions from exterior companies by a common adapter. As a substitute of customized integrations for every pairing (Cursor → GitHub; Claude → Google Drive), any AI app (every one an MCP consumer) can use any MCP-compatible service (or MCP server), making fashions extra interchangeable. MCP additionally creates an agentic interface that enables an AI agent to determine what to do, primarily based on the language of duties, not endpoints. This reduces the MxN integration tax, permits small corporations to lease quite 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 help MCP, or by constructing an unbiased MCP consumer and utilizing any mannequin service. When an AI app’s context is transportable, fashions grow to be extra interchangeable.
MCP is the last 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 out there by 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 Large 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 searching) and SMTP (for electronic mail) enabled permissionless entry of latest functions, but markets nonetheless tipped. Google is now the dominant electronic mail and browser supplier due to its superior merchandise and cross-app integrations.
MCP’s Affect 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 almost each app. This has solely made constructing cleaner, MCP-enabled functions much more engaging. AI-native instruments like Perplexity supply additional encouragement to builders, exhibiting 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. Nevertheless, such integrations can also be boosting utilization of incumbent mannequin builders’ chatbots as they achieve 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 downside. 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 might resist interoperability extra broadly, regardless of the elevated utilization it generates for them, if it finally ends up reinforcing the context moats for software builders. Anthropic quickly lower off the coding software Windsurf’s direct (first-party) entry to its high-performing Claude fashions. Windsurf was rising too in style and was set to be acquired by OpenAI, a direct competitor to Anthropic.
MCP Versus Walled Gardens: The API Gatekeeping Drawback
APIs are the gateway by which an MCP consumer—the AI functions—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 gives 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 danger. Providers can merely shut off API entry totally, or they’ll 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 usually.
- Context-depth danger (the “personalization hole”). Platform APIs expose posts and recordsdata however hardly ever 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, but it surely gives third events neither its Graph API to fetch that full profile nor entry to detailed points of customers’ specific and implicit (inferred) profiles. Equally, OpenAI’s “reminiscence” function is confined to ChatGPT. OpenAI doesn’t permit builders to entry a person’s “reminiscences” by way of an API, even with the person’s prior consent.
To Save AI from Enshittification, Help Protocol-Degree 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 to be able to improve third-party developer entry. Right here, portability means finish customers’ means 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 MCP or any API) with out negotiating bespoke offers. To operationalize this method for AI markets, we suggest:
- Open API entry for main platforms. If the information comes from the person, the person—and any developer the person authorizes—ought to have the ability to take it elsewhere. We suggest requiring that, with person consent, main platforms expose this user-owned contextual information by APIs to accredited builders at zero value. We suggest beginning with the platforms that management essentially the most person context: “gatekeepers” designated by EU standards, plus main AI mannequin suppliers.
Such an method might draw inspiration from the EU’s open-banking legislation (particularly, its Second Cost Providers Directive, or PSD2), which holds that banks should present licensed fintechs with free, real-time entry to core account information and fee features. Approved builders should first receive a license by exhibiting correct safety and information requirements. Not like banking’s standardized information, although, AI context spans code repositories, conversations, behavioral patterns, and preferences. Within the case of AI, markets and regulators would want to give you a means of defining “core person context” for these varied information sorts and platforms.
- Reminiscence as a transportable service. Customers’ AI “reminiscence” ought to be accessible throughout platforms by way of APIs, with market-driven safety requirements embedded within the technical structure. Such MCP servers already exist, even when AI functions don’t help 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 method could 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 monetary 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.
- Protected personalization, with out information exploitation. Open APIs rely on customers’ trusting builders to deal with shared context responsibly. Trade-specific information utilization guidelines would additionally weaken incumbents’ benefits whereas creating safer applied sciences. Such utilization guidelines might begin with:
- Information firewalls. We suggest defending intimate person conversations from industrial focusing on. An AI software leveraging a recognized person choice like “is vegetarian” for restaurant suggestions is helpful; however exploiting therapy-like conversations for manipulative promoting should be prevented.
- Erasure rights. Customers ought to have the ability to evaluation, edit, or delete their choice profiles and reminiscences at any time. ChatGPT already largely gives this.
- Privateness defaults. For delicate queries, we suggest that AI companies default to a non-public mode, with out long-term reminiscence enabled or advert focusing on, except customers explicitly choose in to those settings for such queries.
Finally, management over person 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 information (and instruments) that they’ll securely entry. The selection is ours: design aggressive AI markets round open rules, or settle for a brand new era of platform monopolies.
Due to 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.
