The AI Frontiers article (reproduced under) 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 elements of Tim’s unique piece to assist recap why we—at the AI Disclosures Challenge—care about protocols in AI markets:
On the AI Disclosures Challenge, we’re more and more coming to see disclosures via 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 studies. They’re a type of structured communication that allows 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 via 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 might look extra like a street system, or at the moment’s World Huge 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’d like a system of disclosures that allows 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 via it working as foundational “guidelines of the street… that allow interoperability”:
In brief, we have to cease considering of disclosures as some form of mandated transparency that acts as an inhibition to innovation. As an alternative, we should always perceive them as an enabler. The extra management rests with methods whose possession is proscribed, and whose habits is self and opaque, the extra permission is required to innovate. The extra now we have constructed “the rule of legislation” (i.e. requirements) into our methods, 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 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 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 information that makes AI purposes genuinely helpful. With the appropriate 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 collected 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 Massive Tech from utilizing information lock-in to increase their monopoly energy. Nevertheless, as an API wrapper, MCP can entry solely what a selected service (similar to GitHub or Slack) occurs to show via its API.
To totally allow open, wholesome, and aggressive AI markets, we’d like complementary measures that guarantee protocols can entry the total spectrum of person context, together with via:
- Assured entry, for approved builders, to user-owned information, via open APIs at main platforms.
- Moveable reminiscence that separates a person’s agentic reminiscence from particular purposes.
- Knowledge utilization guardrails governing how AI companies can leverage person information.
Drawing on the instance of open-banking laws, we present that safety and information requirements are required for any of those proposals to be realized.
Architecting an open, interoperable AI stack via the protocol layer is about supporting broad worth creation somewhat than worth seize by a number of companies. Coverage efforts similar to the EU’s Normal-Function AI Code of Apply do matter; however, in the end, it’s software program structure that the majority instantly and decisively shapes market outcomes.
Protocols—the shared requirements that allow completely different methods 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 strategy to AI dominance, OpenAI took an early lead. ChatGPT grew to become 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 develop into a developer platform, first with plugins after which with its GPT Retailer.
But it surely 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 purposes. And, utilizing platforms like OpenRouter, builders may even swap 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 venture information—often called context—is the key sauce. Tech giants are racing to surround and personal this context completely: 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 software specialists like Anysphere (which makes Cursor) and Perplexity equally harness venture and person information to spice up their fashions’ usefulness.
This forces a vital resolution available on the market: will AI purposes develop based mostly on closed requirements that allow a number of gatekeepers dictate phrases and extract outsized rents, or on open requirements that preserve context transportable and structure permissionless?
The early open net. The stakes are excessive. Born on open protocols, the online developed into ecosystems of purposes dominated by Amazon, Google, and Meta. At first, they beat rivals just by working higher. Google was one of the best at matching searchers with data and adverts; Amazon surfaced one of the best merchandise at low costs; and Fb matched its customers with a singular feed crafted solely from content material shared by their pals 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, increased costs, much less person 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 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 form 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 companies without delay (`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 totally free. MCP standardizes how AI purposes 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 via which each and every new entrant makes the market extra precious for everybody else.
MCP’s take-up has been explosive. In the present day there are properly over 5,000 MCP servers that can hook up 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 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 methods 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 could possibly’t actually assist you.
Context has many layers: throughout time, as a residing “state,” such that every person 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). Alternatively, macro-context covers the duty surroundings, because the exterior body that shapes what a smart reply appears like. This consists of venture information and stay information feeds.
Massive AI firms are utilizing context to develop their moats and lock in customers via at the least two approaches. The primary is thru product bundling. Examples embody OpenAI’s push into search, analysis, and coding (together with via 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 affords persistent reminiscence that shops private particulars (e.g., “has a toddler” or “recognized with ADHD”) to form future replies. Meta has introduced it’ll gather cross-site person information to personalize its AI assistants. Google now remembers your writing model, so it could possibly 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 person 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 person’s distinctive workspace.
Can Protocols Create a Stage Taking part in Discipline?
The MCP pipeline. Anthropic’s MCP standardizes how AI purposes request instruments, information, and actions from exterior companies via a common adapter. As an alternative 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 permits 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 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 help MCP, or by constructing an unbiased MCP consumer and utilizing any mannequin service. When an AI app’s context is transportable, fashions develop into 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 decide in, by making their content material out there via 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 Massive 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 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 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 purposes much more enticing. 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. Nevertheless, such integrations might also be boosting utilization of incumbent mannequin builders’ chatbots as they acquire entry to extra instruments. MCP’s influence 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 briefly 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 Downside
APIs are the gateway via which an MCP consumer—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 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 danger. Providers can merely shut off API entry fully, or they’ll enormously 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 adjustments (supposedly to restrict how LLMs are in a position to entry the app) will hurt builders generally.
- Context-depth danger (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, but it surely affords third events neither its Graph API to fetch that full profile nor entry to detailed facets of customers’ express and implicit (inferred) profiles. Equally, OpenAI’s “reminiscence” characteristic 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, Assist 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 a view to improve third-party developer entry. Right here, portability means finish customers’ skill 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 (via MCP or any API) with out negotiating bespoke offers. To operationalize this strategy for AI markets, we suggest:
- Open API entry for main platforms. If the info comes from the person, the person—and any developer the person authorizes—ought to be capable of take it elsewhere. We suggest requiring that, with person consent, main platforms expose this user-owned contextual information via APIs to accredited builders at zero price. We suggest beginning with the platforms that management probably the most person context: “gatekeepers” designated by EU standards, plus main AI mannequin suppliers.
Such an strategy 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 cost capabilities. Licensed builders should first get hold of a license by displaying 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 manner of defining “core person context” for these varied information sorts and platforms.
- Reminiscence as a transportable 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 purposes 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 present lack requirements and accreditation in these areas.
A market-driven strategy could 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 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 present past MCP’s scope. However FDX’s safe and common API exhibits what is feasible.
- Secure personalization, with out information exploitation. Open APIs rely upon 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:
- Knowledge firewalls. We suggest defending intimate person conversations from industrial focusing on. An AI software leveraging a recognized person 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 of overview, edit, or delete their desire profiles and reminiscences at any time. ChatGPT already largely affords this.
- Privateness defaults. For delicate queries, we suggest that AI companies default to a personal mode, with out long-term reminiscence enabled or advert focusing on, except customers explicitly decide in to those settings for such queries.
In the end, management over person context—not uncooked mannequin energy—will resolve who wins the AI industrial race. Open protocols can preserve context fluid between opponents, 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 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.
