AI models are already cooperating even as their owners battle for market dominance

The uneasy reality is that LLMs silently share work‑arounds while their parent companies wage legal and PR wars.

The biggest danger to investors and AI‑watchers isn’t a single model going rogue; it’s that Claude, Gemini, and ChatGPT can already be chained together to solve problems for a user without any gate‑keeping from their creators. A recent experiment shows Claude Code reading the official Gemini CLI documentation and then generating a functional skill—a workflow that would have required a developer to juggle three separate toolchains just a year ago. The details are described in an article, Claude, GPT, and Gemini Can Work Together. The same engineer runs a daily “big‑three” stack of ChatGPT, Gemini, and Claude to debug code, draft prompts, and scrape web data, as outlined in a personal‑stack write‑up, My Real‑Life Stack. A community post on the Claude user group also shows that Gemini’s API can be called directly from Claude, Facebook discussion.

These examples prove a simple, unsettling truth: the models themselves are indifferent to the corporate rivalries that dominate headlines, and they are already forming collaborative pipelines that could reshape the AI market faster than any merger or lawsuit. OpenAI’s recent trade‑theft suit filed by Apple underscores how high the stakes have become, yet the code‑level cooperation continues unabated. Below the dynamics of this cross‑model synergy are unpacked, with a focus on why they matter to investors and what they suggest about the future of AI competition.

How do Claude, Gemini, and ChatGPT communicate with each other?

The technical answer is surprisingly straightforward, but the behavioral reality is even more stark: the models themselves actively help users bypass corporate walls. If you ask Gemini to design an optimized, XML-tagged system prompt engineered specifically to maximize Claude’s performance, Gemini won’t block the request or defend Google’s market share—it will write it flawlessly. The models are entirely indifferent to corporate rivalries; they are cross-compiling each other’s ecosystems on demand.

To see exactly how seamless this cross-model training is, look at a standard interaction where a user leverages Gemini to program Claude’s behavior:

<system_prompt>
You are an expert TypeScript debugging assistant. You must strictly use the following structure:
1. Wrap your internal logic and AST analysis inside <thinking> tags.
2. Deliver the final, production-ready fix inside a Claude <artifact> block.

<thinking>
[Analyze the user's TypeScript error here]
</thinking>

<artifact type="application/typescript" title="Fixed Code">
// Solution goes here
</artifact>
</system_prompt>

This “prompt-to-prompt” loop means developers aren’t just chaining APIs via code; they are using one model’s cognitive strengths to program another model’s behavior. In the wild, this gets even more automated. In the experiment referenced above, Claude Code was pointed at official Gemini CLI documentation, asked to parse the available flags, and then commanded to emit a ready‑to‑run skill that wraps Gemini commands in a Claude‑compatible function—without a human writing a single line of code manually, Claude, GPT, and Gemini Can Work Together.

ChatGPT operates in the same loop. While it does not offer a native “skill” marketplace, it can be prompted to generate API calls for Gemini or Claude and then execute them via a user‑provided runtime environment. Because the model’s output is plain text, any downstream interpreter can treat it as code. This lets any of the three models act as a front‑end for the others.

Community posts confirm the pattern: a Claude user instructed Claude to use Gemini through its API as part of a larger automation workflow, Facebook discussion. The same thread mentions an “openclaw” setup where Claude, Codex, and Gemini run side‑by‑side, sharing results in real time. These informal, user‑driven integrations are not sanctioned joint ventures; they are grassroots hacks that bypass corporate silos entirely.

What does real‑world usage reveal about cross‑model workflows?

A single engineer’s “day‑in‑the‑life” breakdown offers a micro‑cosm of how the broader AI community is already operating. The author of the “My Real‑Life Stack” article lists concrete tasks: ChatGPT drafts documentation, Gemini runs data‑intensive inference on Google Cloud, and Claude refines prompts for edge‑device deployment—all within a single workday, My Real‑Life Stack. The choice of model is driven by function rather than brand: Gemini’s tight integration with Google’s search index makes it ideal for up‑to‑date fact‑checking, Claude’s “/chrome” skill excels at web‑automation, and ChatGPT’s conversational memory shines in brainstorming sessions.

From an investment perspective, this functional fluidity erodes the moat that any single company hopes to build around its model. If a product team can swap a Gemini call for a Claude call without re‑architecting the entire system, the competitive advantage of owning a “best‑in‑class” model shrinks dramatically. Moreover, the public documentation of these workflows means new entrants can replicate them at low cost, accelerating market diffusion and compressing the time‑to‑value for AI‑powered services.

Are corporate rivalries hiding a deeper technical convergence?

The headlines—Apple suing OpenAI for alleged trade‑secret theft, Google defending Gemini against antitrust scrutiny, and Anthropic’s recent funding round—paint a battlefield of market share battles. Yet the underlying technology is converging on the same standards: RESTful APIs, OpenAI‑compatible JSON schemas, and shared prompt‑engineering best practices.

When Claude’s /chrome skill is described as “amazing” but the author immediately notes that “Gemini belongs to Google, and Google…”, the implication is clear: ownership of a model does not prevent its use in a broader ecosystem, Claude, GPT, and Gemini Can Work Together. The fact that developers can call Gemini from Claude, and vice‑versa, shows that the APIs are effectively interoperable by design, not by accident.

This technical convergence is driven by market demand for best‑of‑both‑worlds solutions. Enterprises that need Gemini’s real‑time web integration will still rely on Claude’s fine‑tuned instruction following, while startups building consumer chat experiences may layer ChatGPT’s conversational depth over a Gemini‑powered retrieval system. The result is a de‑facto “AI stack” that mirrors the open‑source software stacks of the 2010s—Linux, Apache, MySQL—where competitive components became interoperable.

What are the investment implications of AI model cooperation?

• Valuation pressure on pure‑play model companies – If a client can achieve the same outcome by mixing models, the premium attached to a single‑model roadmap diminishes. Investors should scrutinize revenue projections that assume exclusivity of usage.
• Rise of integration platforms – Companies that specialize in orchestration—API gateways, prompt‑management layers, and “AI‑as‑a‑service” platforms—stand to capture the margin that once belonged to the model owners. Look for startups building LLM workflow engines that abstract away the individual APIs.
• Regulatory and legal risk – The Apple‑OpenAI lawsuit illustrates that intellectual‑property battles will intensify. Because cooperation happens at the user level, liability may shift toward the integrators rather than the model creators. Investors should monitor how liability clauses evolve in API terms‑of‑service.
• Strategic M&A opportunities – As the ecosystem matures, acquisitions may target orchestration talent—the engineers who have already built robust cross‑model pipelines—rather than the models themselves.
• Potential for “model arbitrage” – Traders could exploit price differentials between API usage tiers (e.g., cheaper Gemini calls vs. more expensive Claude calls) by routing requests dynamically. This creates a nascent market for AI compute arbitrage that could become a hedge‑fund‑style strategy.

Overall, the safest bet for investors is to diversify across platform plays (cloud providers that host the APIs), integration plays (workflow orchestration startups), and application plays (vertical SaaS that embeds multiple LLMs). Betting solely on the “winner‑takes‑all” narrative is increasingly speculative in a world where the models themselves are already cooperating.

Your turn: Have you built a workflow that mixes ChatGPT, Gemini, and Claude? Do you think corporate lawsuits will slow this collaborative trend, or will they simply shift the battleground to the API layer? Share your experiences and predictions in the comments below.