Regardless of intense AI arms race, we’re in for a multi-modal future

Each week — typically every single day—a brand new state-of-the-art AI mannequin is born to the world. As we transfer into 2025, the tempo at which new fashions are being launched is dizzying, if not exhausting. The curve of the rollercoaster is continuous to develop exponentially, and fatigue and marvel have change into fixed companions. Every launch highlights why this specific mannequin is best than all others, with limitless collections of benchmarks and bar charts filling our feeds as we scramble to maintain up.

Eighteen months in the past, the overwhelming majority of builders and companies have been utilizing a single AI mannequin. Immediately, the other is true. It’s uncommon to discover a enterprise of serious scale that’s confining itself to the capabilities of a single mannequin. Firms are cautious of vendor lock-in, significantly for a know-how which has shortly change into a core a part of each long-term company technique and short-term bottom-line income. It’s more and more dangerous for groups to place all their bets on a single giant language mannequin (LLM).

However regardless of this fragmentation, many mannequin suppliers nonetheless champion the view that AI might be a winner-takes-all market. They declare that the experience and compute required to coach best-in-class fashions is scarce, defensible and self-reinforcing. From their perspective, the hype bubble for constructing AI fashions will ultimately collapse, abandoning a single, large synthetic common intelligence (AGI) mannequin that might be used for something and the whole lot. To solely personal such a mannequin would imply to be probably the most highly effective firm on this planet. The scale of this prize has kicked off an arms race for increasingly GPUs, with a brand new zero added to the variety of coaching parameters each few months. 

We imagine this view is mistaken. There might be no single mannequin that may rule the universe, neither subsequent yr nor subsequent decade. As a substitute, the way forward for AI might be multi-model. 

Language fashions are fuzzy commodities 

The Oxford Dictionary of Economics defines a commodity as a “standardized good which is bought and sold at scale and whose units are interchangeable.” Language fashions are commodities in two necessary senses: 

1. The fashions themselves have gotten extra interchangeable on a wider set of duties; 
2. The analysis experience required to supply these fashions is turning into extra distributed and accessible, with frontier labs barely outpacing one another and unbiased researchers within the open-source group nipping at their heels. 

However whereas language fashions are commoditizing, they’re doing so inconsistently. There’s a giant core of capabilities for which any mannequin, from GPT-4 all the best way all the way down to Mistral Small, is completely suited to deal with. On the identical time, as we transfer in direction of the margins and edge instances, we see larger and larger differentiation, with some mannequin suppliers explicitly specializing in code technology, reasoning, retrieval-augmented technology (RAG) or math. This results in limitless handwringing, reddit-searching, analysis and fine-tuning to seek out the suitable mannequin for every job. 

And so whereas language fashions are commodities, they’re extra precisely described as fuzzy commodities. For a lot of use instances, AI fashions might be almost interchangeable, with metrics like value and latency figuring out which mannequin to make use of. However on the fringe of capabilities, the other will occur: Fashions will proceed to specialize, turning into increasingly differentiated. For example, Deepseek-V2.5 is stronger than GPT-4o on coding in C#, regardless of being a fraction of the dimensions and 50 instances cheaper. 

Each of those dynamics — commoditization and specialization — uproot the thesis {that a} single mannequin might be best-suited to deal with each potential use case. Slightly, they level in direction of a progressively fragmented panorama for AI. 

Multi-modal orchestration and routing

There’s an apt analogy for the market dynamics of language fashions: The human mind. The construction of our brains has remained unchanged for 100,000 years, and brains are much more related than they’re dissimilar. For the overwhelming majority of our time on Earth, most individuals realized the identical issues and had related capabilities. 

However then one thing modified. We developed the power to speak in language — first in speech, then in writing. Communication protocols facilitate networks, and as people started to community with one another, we additionally started to specialize to larger and larger levels. We turned free of the burden of needing to be generalists throughout all domains, to be self-sufficient islands. Paradoxically, the collective riches of specialization have additionally meant that the common human at the moment is a far stronger generalist than any of our ancestors. 

On a sufficiently large sufficient enter area, the universe at all times tends in direction of specialization. That is true all the best way from molecular chemistry, to biology, to human society. Given ample selection, distributed methods will at all times be extra computationally environment friendly than monoliths. We imagine the identical might be true of AI. The extra we will leverage the strengths of a number of fashions as a substitute of counting on only one, the extra these fashions can specialize, increasing the frontier for capabilities. 

An more and more necessary sample for leveraging the strengths of numerous fashions is routing — dynamically sending queries to the best-suited mannequin, whereas additionally leveraging cheaper, sooner fashions when doing so doesn’t degrade high quality. Routing permits us to make the most of all the advantages of specialization — greater accuracy with decrease prices and latency — with out giving up any of the robustness of generalization.

A easy demonstration of the facility of routing will be seen in the truth that many of the world’s high fashions are themselves routers: They’re constructed utilizing Combination of Skilled architectures that route every next-token technology to a couple dozen skilled sub-models. If it’s true that LLMs are exponentially proliferating fuzzy commodities, then routing should change into a necessary a part of each AI stack. 

There’s a view that LLMs will plateau as they attain human intelligence — that as we totally saturate capabilities, we are going to coalesce round a single common mannequin in the identical approach that we have now coalesced round AWS, or the iPhone. Neither of these platforms (or their opponents) have 10X’d their capabilities up to now couple years — so we would as effectively get snug of their ecosystems. We imagine, nonetheless, that AI is not going to cease at human-level intelligence; it’ll keep it up far previous any limits we would even think about. Because it does so, it’ll change into more and more fragmented and specialised, simply as some other pure system would. 

We can’t overstate how a lot AI mannequin fragmentation is an excellent factor. Fragmented markets are environment friendly markets: They provide energy to consumers, maximize innovation and reduce prices. And to the extent that we will leverage networks of smaller, extra specialised fashions somewhat than ship the whole lot via the internals of a single large mannequin, we transfer in direction of a a lot safer, extra interpretable and extra steerable future for AI. 

The best innovations don’t have any house owners. Ben Franklin’s heirs don’t personal electrical energy. Turing’s property doesn’t personal all computer systems. AI is undoubtedly certainly one of humanity’s biggest innovations; we imagine its future might be — and must be — multi-model. 

Zack Kass is the previous head of go-to-market at OpenAI.

Tomás Hernando Kofman is the co-Founder and CEO of Not Diamond.