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 necessary to first very briefly undergo components 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 way of 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 studies. 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 big 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 the moment’s World Broad Net.
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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 want a system of disclosures that permits 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 way of it working as foundational “guidelines of the street… that allow interoperability”:
Briefly, we have to cease considering 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 proscribed, and whose conduct is self and opaque, the extra permission is required to innovate. The extra we’ve got constructed “the rule of legislation” (i.e. requirements) into our programs, the extra distributed innovation can flourish.
Now, on to the principle course!
Protocols and Energy
As AI fashions change 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 change into commoditized, incumbent Huge Tech platforms are racing to rebuild their moats on the utility 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 collected 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 Huge 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 reveal by way of its API.
To completely allow open, wholesome, and aggressive AI markets, we want complementary measures that guarantee protocols can entry the total spectrum of consumer context, together with by way of:
- Assured entry, for licensed builders, to user-owned knowledge, by way of open APIs at main platforms.
- Transportable reminiscence that separates a consumer’s agentic reminiscence from particular functions.
- Information utilization guardrails governing how AI companies can leverage consumer knowledge.
Drawing on the instance of open-banking laws, we present that safety and knowledge 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 just a few companies. Coverage efforts equivalent to the EU’s Basic-Goal AI Code of Follow do matter; however, finally, it’s software program structure that the majority instantly and decisively shapes market outcomes.
Protocols—the shared requirements that allow totally different programs talk with each other—operate as a deeper de facto legislation, 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 strategy 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 subsequent step was to show it right into a platform. OpenAI tried to change into a developer platform, first with plugins after which with its GPT Retailer.
Nevertheless it hasn’t all gone in keeping 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 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 undertaking knowledge—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 undertaking 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 just a few gatekeepers dictate phrases and extract outsized rents, or on open requirements that maintain context moveable and structure permissionless?
The early open net. The stakes are excessive. Born on open protocols, the online 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 info and adverts; 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 mates and other people 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 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 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 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 everyone that makes use of it, exceeds the worth of the corporate that creates it. Then it’s a platform.” That type of platform is nearly 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 without delay (`multi-homing’), and invitations further scrutiny from regulators.
The promise of protocols. Anthropic has taken a distinct route, growing MCP as an open protocol, a shared algorithm that anybody can use without cost. MCP standardizes how AI functions request info and actions from exterior companies, thereby facilitating equitable developer entry to exterior instruments and knowledge 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 precious 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 a whole bunch of AI apps which have built-in MCP. Confronted with speedy 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 programs thrive on context: the consumer knowledge that lets an AI system tailor its conduct to customers, their requests, and the duties at hand. When correctly mined, this consumer knowledge 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’t actually allow 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 atmosphere, because the exterior body that shapes what a smart reply seems like. This contains undertaking recordsdata and dwell knowledge feeds.
Huge AI firms are utilizing context to develop their moats and lock in customers by way of a minimum of 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 gives persistent reminiscence that shops private particulars (e.g., “has a baby” or “identified with ADHD”) to form future replies. Meta has introduced it can acquire cross-site consumer knowledge to personalize its AI assistants. Google now remembers your writing model, so it may 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 specific 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 Area?
The MCP pipeline. Anthropic’s MCP standardizes how AI functions request instruments, knowledge, and actions from exterior companies by way of 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 resolve what to do, based mostly on the language of duties, not endpoints. This reduces the MxN integration tax, permits small companies 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 consumer and utilizing any mannequin service. When an AI app’s context is moveable, fashions change 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 companies should nonetheless decide in, by making their content material accessible by way of APIs.
This shifts the aggressive gravity “up the stack,” away from the mannequin builders and to the appliance that develops the successful context flywheel. App-level knowledge portability and governance—together with pricing, permissioning, and any preferential entry into Huge 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 electronic mail) enabled permissionless entry of recent 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 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 enticing. AI-native instruments like Perplexity supply additional encouragement to builders, exhibiting that customers will decide a custom-made 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 affect 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 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 quickly minimize off the coding utility Windsurf’s direct (first-party) entry to its high-performing Claude fashions. Windsurf was rising too widespread 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 consumer—the AI functions—can entry third-party knowledge 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 threat. Providers can merely shut off API entry totally, 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) at the same time as they launch competing merchandise. In the meantime, Slack’s new API adjustments (supposedly to restrict how LLMs are in a position to 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 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” characteristic is confined to ChatGPT. OpenAI doesn’t permit builders to entry a consumer’s “recollections” by way of 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 want an structure that helps user-sanctioned knowledge portability to be able 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 way of 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 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 method might draw inspiration from the EU’s open-banking legislation (particularly, its Second Fee Providers Directive, or PSD2), which holds that banks should present licensed fintechs with free, real-time entry to core account knowledge and cost features. Approved builders should first acquire a license by exhibiting correct safety and knowledge requirements. In contrast to 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 method of defining “core consumer context” for these varied knowledge varieties and platforms.
- Reminiscence as a conveyable service. Customers’ AI “reminiscence” must 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 assist it.
The problem is much less technical than socio-economic. Reminiscence is deeply private and requires safe data-handling, but AI markets at present 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 commonplace for US open banking. Such embedding permits for safe and standardized sharing of economic 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 present past MCP’s scope. However FDX’s safe and common API reveals what is feasible.
- Secure personalization, with out knowledge exploitation. Open APIs rely upon customers’ trusting builders to deal with shared context responsibly. Business-specific knowledge utilization guidelines would additionally weaken incumbents’ benefits whereas creating safer applied sciences. Such utilization guidelines might begin with:
- Information firewalls. We advocate defending intimate consumer conversations from industrial focusing on. An AI utility 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 overview, edit, or delete their desire profiles and recollections at any time. ChatGPT already largely gives this.
- Privateness defaults. For delicate queries, we advocate that AI companies default to a personal mode, with out long-term reminiscence enabled or advert focusing on, until 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 maintain 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 ideas, 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.
