The AI Frontiers article (reproduced under) builds on a earlier Asimov Addendum article written by Tim O’Reilly, entitled: “Disclosures. I don’t suppose that phrase means what you suppose it means.” I (Ilan) suppose it’s necessary to first very briefly undergo elements of Tim’s authentic piece to assist recap why we—at the AI Disclosures Undertaking—care about protocols in AI markets:
On the AI Disclosures Undertaking, 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 excess of only a warning label, or a mandated set of reviews. They’re a type of structured communication that allows impartial, decentralized motion.
Tim then notes why this issues for AI’s “market construction”:
The race for first mover benefit by the big 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 linked by new modes of standardized communication may look extra like a street system, or immediately’s World Broad Net.
…
If we would like a world the place everybody, not simply AI mannequin builders and people constructing on high 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 want a system of disclosures that allows interoperability and discovery.
On this method, protocols, as a sort 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 street… that allow interoperability”:
In brief, we have to cease pondering of disclosures as some type of mandated transparency that acts as an inhibition to innovation. As a substitute, we should always perceive them as an enabler. The extra management rests with programs whose possession is restricted, 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 programs, the extra distributed innovation can flourish.
Now, on to the primary course!
Protocols and Energy
As AI fashions develop into commodities, tech giants are racing to lock in customers by hoarding their knowledge. Open protocols, backed by open APIs, can facilitate broad knowledge sharing and more healthy AI markets.
Initially printed 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 develop into commoditized, incumbent Massive Tech platforms are racing to rebuild their moats on the software layer, round context: the sticky user- and project-level knowledge that makes AI functions genuinely helpful. With the best context-aware AI functions, every extra user-chatbot dialog, file add, or coding interplay improves outcomes; higher outcomes entice extra customers; and extra customers imply extra knowledge. This context flywheel—a wealthy, structured user- and project-data layer—can drive up switching prices, making a lock-in impact when gathered knowledge is trapped throughout the platform.
Protocols stop 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 Massive Tech from utilizing knowledge lock-in to increase their monopoly energy. Nonetheless, as an API wrapper, MCP can entry solely what a specific service (equivalent to GitHub or Slack) occurs to show by its API.
To totally allow open, wholesome, and aggressive AI markets, we want complementary measures that guarantee protocols can entry the complete spectrum of consumer context, together with by:
- Assured entry, for licensed builders, to user-owned knowledge, by open APIs at main platforms.
- Transportable reminiscence that separates a consumer’s agentic reminiscence from particular functions.
- Knowledge utilization guardrails governing how AI providers can leverage consumer knowledge.
Drawing on the instance of open-banking rules, we present that safety and knowledge 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 somewhat than worth seize by a number of companies. Coverage efforts equivalent to the EU’s Basic-Function AI Code of Follow 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 programs talk with each other—perform as a deeper de facto legislation, enabling impartial, decentralized, and safe motion in digital markets.
From Commoditized Fashions to Context-Wealthy Functions
From fashions to providers. In a fevered race to blitzscale its method to AI dominance, OpenAI took an early lead. ChatGPT grew to become the fastest-growing software in historical past, and it was straightforward to imagine that the subsequent step was to show it right into a platform. OpenAI tried to develop into a developer platform, first with plugins after which with its GPT Retailer.
Nevertheless it hasn’t all gone based on 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 consumer knowledge. Entrance-end customers can now simply select their most popular 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 appliance layer, the place proprietary consumer and mission knowledge—referred to as context—is the key sauce. Tech giants are racing to surround and personal this context completely: 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 mission and consumer knowledge to spice up their fashions’ usefulness.
This forces a vital choice available on the market: will AI functions develop based mostly on closed requirements that allow a number 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 developed into ecosystems of functions dominated by Amazon, Google, and Meta. At first, they beat rivals just by working higher. Google was the perfect at matching searchers with data and advertisements; Amazon surfaced the perfect 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 profitable 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 geared toward preserving dominance and squeezing customers and builders. Cory Doctorow frames this course of vividly as “enshittification.” AI’s enshittification may contain weaker security guardrails, greater costs, much less consumer privateness, and lower-quality data or agentic help. In brief, 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 mentioned 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 all the time 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 providers 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 free of charge. MCP standardizes how AI functions request data and actions from exterior providers, thereby facilitating equitable developer entry to exterior instruments and knowledge context. That is how networked markets develop: by enabling an structure of participation by which each and every new entrant makes the market extra worthwhile for everybody else.
MCP’s take-up has been explosive. At present there are effectively over 5,000 MCP servers that can connect with the a whole bunch 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 identical incumbents are already pushing again.
How Consumer Context Is Powering a New Period of Tech Monopolies—and Competitors
Context creates worth. AI programs thrive on context: the consumer knowledge that lets an AI system tailor its habits to customers, their requests, and the duties at hand. When correctly mined, this consumer knowledge permits for customized 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 will probably’t actually enable you 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 mission recordsdata and reside knowledge feeds.
Massive AI firms are utilizing context to develop their moats and lock in customers by no less than 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 info floor and raises switching prices.
The second is thru constructing context as a central product function. OpenAI now affords persistent reminiscence that shops private particulars (e.g., “has a toddler” or “identified with ADHD”) to form future replies. Meta has introduced it would accumulate cross-site consumer knowledge 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 Massive AI firms’ gathering express consumer 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 consumer’s distinctive workspace.
Can Protocols Create a Stage Enjoying Subject?
The MCP pipeline. Anthropic’s MCP standardizes how AI functions request instruments, knowledge, and actions from exterior providers by a common adapter. As a substitute of customized integrations for every pairing (Cursor → GitHub; Claude → Google Drive), any AI app (each 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 resolve what to do, based mostly on the language of duties, not endpoints. This reduces the MxN integration tax, permits small companies to lease somewhat 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 consumer and utilizing any mannequin service. When an AI app’s context is transportable, fashions develop into 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 providers should nonetheless decide 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 appliance that develops the profitable context flywheel. App-level knowledge portability and governance—together with pricing, permissioning, and any preferential entry into Massive Tech–managed knowledge 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 recent functions, 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 Impression 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 way more engaging. AI-native instruments like Perplexity supply additional encouragement to builders, displaying that customers will choose 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 achieve entry to extra instruments. MCP’s affect 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 software builders. Anthropic quickly minimize off the coding software Windsurf’s direct (first-party) entry to its high-performing Claude fashions. Windsurf was rising too standard 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 knowledge and instruments, thereby breaking down a platform’s “walled backyard” of proprietary providers and datasets. However MCP can liberate context solely when a third-party service affords a sufficiently wealthy API (and retains it open). As a result of platform homeowners 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. Companies can merely shut off API entry solely, or they’ll enormously degrade entry. Current API paywalls and shutdowns at Reddit, Twitter, and Meta present how entry can vanish in a single day. Enterprise providers like Salesforce (which owns Slack), Atlassian, and Notion are now limiting API entry by Glean (a context platform) at the same time as 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 on the whole.
- Context-depth threat (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 affords third events neither its Graph API to fetch that full profile nor entry to detailed features of customers’ express and implicit (inferred) profiles. Equally, OpenAI’s “reminiscence” function is confined to ChatGPT. OpenAI doesn’t permit builders to entry a consumer’s “recollections” through an API, even with the consumer’s prior consent.
To Save AI from Enshittification, Help Protocol-Stage Interventions
Enhancing protocols for the AI age. To interrupt API gatekeeping in AI markets, we want an structure that helps user-sanctioned knowledge portability as a way to improve third-party developer entry. Right here, portability means finish customers’ potential to learn and switch their knowledge 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 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 knowledge by APIs to accredited builders at zero price. We suggest beginning with the platforms that management essentially the most consumer context: “gatekeepers” designated by EU standards, plus main AI mannequin suppliers.
Such an method may draw inspiration from the EU’s open-banking legislation (particularly, its Second Fee Companies Directive, or PSD2), which holds that banks should present licensed fintechs with free, real-time entry to core account knowledge and cost capabilities. Approved builders should first acquire a license by displaying correct safety and knowledge 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 wish to provide you with a means of defining “core consumer context” for these varied knowledge varieties and platforms.
- Reminiscence as a transportable service. Customers’ AI “reminiscence” ought to be accessible throughout platforms through APIs, with market-driven safety requirements embedded within the technical structure. Such MCP servers already exist, even when AI functions 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 method can be to embed these safety requirements into technical structure, as is completed with the FDX API normal for US open banking. Such embedding permits for safe and standardized sharing of monetary knowledge 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 exhibits what is feasible.
- Protected personalization, with out knowledge exploitation. Open APIs depend upon customers’ trusting builders to deal with shared context responsibly. Trade-specific knowledge utilization guidelines would additionally weaken incumbents’ benefits whereas creating safer applied sciences. Such utilization guidelines may begin with:
- Knowledge firewalls. We advocate defending intimate consumer conversations from industrial concentrating on. An AI software leveraging a recognized consumer desire like “is vegetarian” for restaurant suggestions is helpful; however exploiting therapy-like conversations for manipulative promoting have to be prevented.
- Erasure rights. Customers ought to be capable to assessment, edit, or delete their desire profiles and recollections at any time. ChatGPT already largely affords this.
- Privateness defaults. For delicate queries, we advocate that AI providers default to a non-public mode, with out long-term reminiscence enabled or advert concentrating on, except customers explicitly decide in to those settings for such queries.
In the end, management over consumer context—not uncooked mannequin energy—will resolve 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’ll securely entry. The selection is ours: design aggressive AI markets round open ideas, or settle for a brand new era 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.
