The AI Labs Have All Started Building the Same Thing

Something's changed in AI development over the past six months. The labs are still making announcements, but they're not about smarter models anymore. They're about better products.

Look at what the major AI labs have been shipping lately, and you'll see they're all building products that wrap their existing models in slick interfaces instead of shipping dramatically smarter models. OpenAI just launched Agent Mode, basically a fancy chatbot interface that can maintain conversation context and call different tools. The company explicitly declared GPT-4.5 "Orion" as their "last non-chain-of-thought model" after retiring it in just 4.5 months—the shortest-lived model in their history. Anthropic rolled out enhanced Artifacts that let you build interactive widgets and apps directly in chat, while launching 12 major product improvements compared to just 2 foundational model releases. XAI shipped Research Mode and Companion Mode, which are essentially specialized chat interfaces for different use cases.

The pattern is undeniable: Anthropic's product releases now occur nearly every month while foundational model improvements remain selective. Google achieved breakthrough research milestones but simultaneously launched consumer products at unprecedented velocity, with Sundar Pichai calling 2025 "critical" for moving faster on product deployment rather than model development.

Yes, OpenAI's reasoning models saw benchmarks skyrocket, but those are really more product than core model innovation. They're essentially the same underlying architecture with extra thinking steps baked in, packaged as a premium feature. Even Google's "Deep Think" version of Gemini that achieved gold-medal performance on mathematical olympiad problems represents inference-time scaling, not foundational capability improvements.

These aren't breakthrough AI capabilities, but they do represent sophisticated applications that showcase what current models can do when properly integrated into polished interfaces. The fact that billion-dollar research teams are spending their time on conversation modes and interactive interfaces tells you everything about where the real innovation has stalled. When training costs for frontier models now exceed $100 million with projections reaching $10 billion for future models, and OpenAI's latest model reportedly shows improvements "so slight as to be almost negligible," the economics force this pivot. When you stop hearing about dramatic model improvements and start hearing about "enhanced user experiences" and "seamless workflows," that's usually code for "we've hit a wall on the underlying technology."

The dirty secret is that GPT-4, Claude 4, and Grok 4 all perform within statistical noise of each other on most real tasks. Multiple industry sources confirm that AI scaling laws are showing diminishing returns, with models facing a fundamental data constraint—they will exhaust available public text data between 2026 and 2032. Ask them the same complex question and you'll get three different answers that are all roughly equally good and equally flawed. Sure, one might be slightly better at coding while another excels at creative writing, but the fundamental capabilities have converged. As one A16Z report noted, "for most tasks, all the models perform well enough now—so pricing has become a much more important factor." So instead of pushing for the next major breakthrough, every lab has pivoted to the same playbook: take your existing model, wrap it in different interfaces, add some workflow features, and call it innovation. That's not a coincidence. It's what happens when model development plateaus.

Even Meta's Chief AI Scientist Yann LeCun confirms this trajectory, predicting the current LLM paradigm has a "fairly short" 3-5 year shelf life, stating that "nobody in their right mind would use them anymore" as central AI components. The industry leadership knows where this is heading.

This shift changes everything about AI's trajectory. Sam Altman's declaration that "we are now confident we know how to build AGI as we have traditionally understood it" signals OpenAI believes the fundamental research questions have been largely answered. We're not heading toward some sci-fi future where AI achieves superintelligence or replaces everyone's job. We're heading toward a decade that looks a lot like right now, where AI remains a powerful but bounded tool that gets packaged into increasingly polished products. The transformer architecture has given us most of what it's going to give us, and the real competition has moved from who can build the smartest AI to who can build the best products around the AI we already have.

The market confirms this reality. Enterprise spending patterns show innovation budgets for AI experimentation dropped from 25% of LLM spending to just 7% in 2025. Companies now fund AI through centralized IT budgets, treating it as essential infrastructure rather than experimental technology. Over 90% of enterprises are testing third-party AI applications rather than building their own—they want products, not models.

Companies need to wake up to this reality. Instead of waiting for the next breakthrough model that might never come, the smart move is doubling down on what GPT-4 and Claude can already do. These models are genuinely capable of handling complex workflows, automating real tasks, and solving actual business problems. The paradigms, practices, and techniques for working with them have been figured out over the past two years. The winners won't be those with marginally better models, but those who build the most compelling products, forge the strongest partnerships, and create the most seamless user experiences. Stop waiting for better models and start building around the ones we have.