Can we just skip ahead to the part where all the LLMs consolidate into one or two major players?
Honestly, there are just too many language models right now. For the average user, it’s overwhelming and confusing trying to figure out which one to use. Every other week, it seems like a new model launches—each claiming to be better, faster, stronger than the last. The pace of innovation is exciting—but also exhausting. It’s hard to keep up, and even harder to decide where to invest your time and money. And with most models requiring a subscription, choosing becomes not just a decision—but a financial commitment.
Right now, major players include ChatGPT, Gemini, Claude, MetaAI, Mistral, DeepSeek, Grok… and that’s just the top layer. As of early 2025, there are over 75+ commercially available LLMs, with dozens more in development. The market is flooded. They’re all in a constant race, leapfrogging each other with new features or performance boosts. And yet, functionally, they’re mostly doing the same things. Sure, some have niche strengths—but the core capabilities are increasingly converging.
Why Fragmentation Hurts AI Tool Adoption
This fragmentation creates real challenges—especially for everyday users. The biggest is user paralysis: too many options with too much overlap. People don’t know which one best fits their needs, and often end up cycling through several—or none at all. And from an organizational standpoint, it’s even worse. Teams spend months evaluating tools, often juggling multiple subscriptions, or wasting time in analysis mode rather than execution.
And then there’s the lack of standardization. With so many players building in silos, there are no shared UX conventions, formatting norms, or reliable API compatibility. Adopting one model over another often means adapting to a whole new workflow. If platforms aligned around common standards, adoption would be smoother and user onboarding far less painful.
We’ve Seen This Movie Before
Now, I get it—today’s tech and economic landscape is more complex than past examples. But we’ve seen this pattern before. Technologies consolidate over time. VHS beat Betamax. Blu-ray beat HD DVD. AOL, MySpace. Remember how many smartphone makers there were in the beginning? Inevitably, winners emerge, and things get simpler.
But these comparisons, while tempting, may oversimplify what’s going on.
Let’s look at a more relatable example. Take the early days of the internet: in the late ’90s and early 2000s, the Dot Com era was booming, there were dozens of search engines—AltaVista, Excite, Lycos, Yahoo, Ask Jeeves, WebCrawler. Then Google entered the scene. It didn’t just win because it had a silly name; it won because it was objectively better—more relevant results, cleaner UI, faster indexing. As Google rose, the industry consolidated. Smaller engines were acquired or faded out, and innovation actually sped up. Fewer players didn’t mean stagnation—it meant focus. The result? Better algorithms, better user experiences, and a more stable landscape for everyone.
Why LLM Consolidation Makes Sense
That’s the vision I have for LLMs. A market where one or two models become the industry standard, where the rest either specialize deeply, get absorbed, or phase out. Imagine not having to check if Gemini is better for fiction, ChatGPT for images, Claude for code, or Grok for news. You’d just pick your go-to and get to work. That kind of simplicity could unlock a whole new wave of productivity.
But It’s Not That Simple
But let’s be honest— as much as I’d like to wave my hand and make it so, that model may not be the perfect fit for AI today.
For one, LLMs aren’t just consumer-facing tools. They’re infrastructure—more akin to operating systems or cloud platforms than search engines. We live in a world where Windows, macOS, and Linux coexist because different users need different things. The same might always be true for LLMs.
Then there’s the open-source movement. Projects like MetaAI, Mistral, and DeepSeek are thriving—not despite their openness, but because of it. For developers, researchers, and smaller enterprises, open models offer something proprietary systems can’t: control, auditability, and adaptability. Even if the big players consolidate, open-source will likely remain a critical check on their dominance—and a spark for ongoing innovation.
The Role of Big Tech and Big Money
But the biggest barrier to consolidation? Money. Every major model is backed by a tech giant with deep, seemingly endless pockets. Google has Gemini. Amazon supports Claude. Microsoft backs OpenAI. Grok is Elon Musk’s project. These companies can afford to burn through billions in pursuit of AI dominance and then turn around and raise even more money from investors looking to back a winner. And they’re not going to walk away quietly. Until these projects become financial liabilities—or unless regulators intervene—we’re not likely to see real consolidation anytime soon.
Which brings us to an important point: regulation. In today’s climate, every merger, acquisition, or major partnership faces intense scrutiny. Antitrust regulators around the world—especially in the U.S. and EU—are increasingly wary of concentration in tech. Even if consolidation makes sense from a user-experience perspective, it may not be legally or politically viable in the short term. In fact, it could be actively discouraged in the name of “competition.”
And even if it were possible, there are ethical risks we shouldn’t and can’t ignore. A world where one or two LLMs dominate might be simpler, but it also introduces real dangers: entrenched bias, lack of transparency, limited oversight. Who sets the guardrails? Who decides what’s “true” or “appropriate” for billions of users? Centralizing that power in a few hands might make things easier—but it also makes the stakes much higher.
Beware the AI Monoculture
We also have to consider the risk of creating an AI monoculture—a fragile ecosystem where everything is built on one or two foundational models. If those models have vulnerabilities, systemic biases, or are controlled by bad actors, the fallout could be massive. Diversity in the ecosystem—annoying as it is right now—provides a form of resilience. It’s insurance against failure, stagnation, or abuse.
So Where Does That Leave Us?
For now, I think my dream of a simpler, more unified AI landscape will have to wait. In a perfect world, we’d find a middle ground: a few strong models that set shared standards, while a healthy open-source community continues to thrive on the edges. A world with clarity and consistency, without killing innovation.
So yeah, I still think consolidation is inevitable—and beneficial. But it’s got to be done right. Not just fewer choices, but better ones. Not just efficiency, but ethics. Not just simplicity, but security.
And as much as I’d like that world to exist today, the reality is… we’re not quite there yet.
