Long read: From Ferment to Dominant Design: Reading the AI Model Wars Through History

In this brilliant blog (which we would suggest you read in full because of how important this could be for your business), Prof Lakhani tells us that those of us who are trying to figure who will win the LLM race by comparing what various AI models can do are likely to get it wrong. He says: “I go back to 1926 — when the auto industry looked exactly like this. Hundreds of firms. Competing designs. Weekly shifts in who seemed to be “winning.” We know how that story ended. But nobody living through it could see the shakeout coming.

Two scholars changed how I think about moments like this: James Utterback (MIT) and Steven Klepper (CMU). Their frameworks — dominant design, industry life cycles, and the shakeout — are the right lenses for reading what’s actually happening in foundation models right now.”

As befits a renowned business school prof, Karim gets to the punchline straightaway: “we are in a period of structured ferment. Not chaos, but not a settled race either. The dominant design for AI hasn’t emerged. The shakeout hasn’t happened. And the winners aren’t yet identifiable with the confidence that most commentary projects onto the moment.”

So why is Utterback and Klepper’s thinking important for those of us who want to identify the winner in the LLM wars? A lot of that thinking is contained in diagrams which we cannot reproduce here (and hence reading Prof Lakhani’s blog is all the more useful) but here is the crux what these two thinkers said:

“Utterback helps because he gives us a language for separating noise in the moment from pattern over time. In his classic work…the point was not simply that industries innovate, but that they do so in recognizable stages. Early on, an industry tends to be in a fluid phase: firms experiment with product concepts, architectures, interfaces, and uses; uncertainty is high; and the basis of competition is still unsettled. Over time, that fluid phase gives way to a transitional phase, in which some design choices begin to stabilize and firms increasingly learn what customers value, what complements matter, and what scale really requires. Eventually, if the industry matures, a specific phase emerges, where improvement becomes more incremental, process discipline matters more, and competition shifts toward efficiency, cost, and execution. The key insight is that the meaning of “who is ahead” changes across those stages.

His central idea is straightforward but powerful: early in an industry’s life, firms do not yet know the final form of the product, the most important features, the right production method, or even the most meaningful definition of value for customers. Only later does a more stable dominant design emerge — and when that happens, competition changes character. The question stops being mainly what should the product be? and becomes increasingly who can deliver it more reliably, cheaply, and at scale?” [Underlining is ours]

Moving on to Klepper: “Klepper helps in a different but equally important way. Where Utterback asks us to pay attention to design evolution, Klepper asks us to watch the population dynamics of the industry itself. His core question was why so many industries show a familiar pattern: entry rises early, the number of firms peaks, a shakeout follows, and a smaller set of firms comes to dominate. What matters, in this account, is not only whether a dominant design emerges, but whether some firms build stronger capabilities for innovation, growth, and cost reduction than others.

Again, the automobile industry is the canonical case. The early industry was crowded with entrants, experimentation, and local successes. But over time, the number of producers fell — not simply because observers finally noticed who was “best,” but because firms with stronger capabilities and better positions in the industry’s evolving economics were more likely to survive and grow. The shakeout, in this view, is not an accident or a sudden verdict from the market. It is the result of cumulative differences playing out across cohorts of firms over time.” [Underlining is ours]

Pulling all of this together, Prof Lakhani has a message for crystal ball gazers like us: “We are likely still in the high-entry phase — the equivalent of the 1900s in Klepper’s automobile data. Entry is high, experimentation is intense, and no one has yet been conclusively selected out. It is important to hold two observations separately here. At the architectural level, the field remains genuinely open: we do not yet know whether transformers will prove to be the dominant design or one branch among several that the industry eventually selects among. But at the firm level, a different dynamic may already be operating quietly underneath the noise — some organizations are accumulating compute, distribution, and organizational learning at a pace that will be hard to replicate later. Architectural openness and competitive asymmetry can coexist in the same moment. The market can still be discovering what it is while some players are already building positions that will matter when it does.

That lens matters for foundation models because it pushes us to ask a more structural question than who currently has the most impressive model. Even if the field eventually converges on something like a dominant design, the deeper industrial question is which organizations can repeatedly finance training runs, secure compute, attract research talent, build data advantages, improve inference economics, create distribution, and turn technical progress into organizational learning. Klepper would tell us to watch entry, survival, exit, and capability accumulation — not just rankings.

It also allows a more subtle claim than simply saying that nobody knows who will win. The more precise point is that two things can be true at once. First, we are still early enough that variety remains high and the field is visibly unsettled. Second, the foundations for future concentration may already be forming underneath that visible turbulence. Some organizations may already be building advantages in deployment, distribution, enterprise trust, model operations, and reinvestment capacity that will matter long after today’s benchmark debates are forgotten.

The eventual shakeout may also happen at a different layer than people expect. The end state may not be a single “best model” crowned in the abstract. Instead, concentration may occur across several layers of the stack: one set of firms may control the frontier layer, another may dominate enterprise integration, another may anchor the open ecosystem, and others may own specialized complements such as cloud access, chips, tooling, and safety infrastructure. The future structure of the industry may look less like one winner taking all and more like a narrower and more durable set of positions across interconnected layers, each shaped by different forms of capability accumulation.”

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