Has market leadership in the frontier artificial intelligence model market already begun to take shape? And are calls to slow the development of the latest frontier models going to help solidify that market pattern? It seems possible.
Consider the advantages of a slowing of frontier model releases for existing leaders. If new release pace slows to “a new model every 18 months to 24 months,” then the capital intensity of the business might well slow from the breakneck pace of “a new model every six months to 12 months.”
The caveat, of course, is that all the leading contenders need to agree to respect the new pattern. But such agreements can temper the competitive-investment dilemma where each leading firm has to respond if a major competitor ups investment, even if all the leading models are relatively comparable at each stage of development, as suggested by Stanford's Human-Centered Artificial Intelligence institute.
Savings would develop as model developers spent less on training costs, for example, which might also drive less spending on graphics processing units and data center facilities. That, in turn, would help developers reach revenue targets more easily.
On the other hand, such slowing could also enhance the value of existing high-performance computing assets. If capability scaling slows, the economic life of existing infrastructure increases. In other words, older GPUs should have extended economic life and value, for example.
Longer frontier model cycles also should mean the useful economic life of each existing model lengthens, allowing the existing models to remain near the performance frontier for a longer period, enhancing the value of each model.
And all of that might actually help leaders remain leaders, while making it harder for upstarts to break into leadership, as costs related to governance and testing will increase.
As seen in other industries, large compliance costs raise the costs of doing business, and tend to favor larger firms better able to bear the costs of such compliance.
Also, if the frontier model releases slow their pace, while more money and effort has to be spent on safety and testing, there is less opportunity for an upstart to move faster and take share, assuming the upstarts have less capital to commit, compared to the model leaders.
At the same time, the market leaders retain a lead in monetizing their products, for a longer time, at greater scale, while upstarts are disadvantaged, in that regard.
In fact, most capital-intensive industries tend to have a small number of leaders. Perhaps the only surprise for the capital-intensive frontier model business is that we might be seeing market structure stabilization already.
In addition, in part because of network effects, software industries also tend to have a small number of leaders. The rule is so common as to have a name: “The rule of three.”
“A stable competitive market never has more than three significant competitors, the largest of which has no more than four times the market share of the smallest,” BCG founder Bruce Henderson said in 1976.
And such structures have profitability implications as well. “Costs characteristically decline by 20 percent to 30 percent in real terms each time accumulated experience doubles," Henderson posited in 1968.
In most markets, argue Bain consultants, two firms have 80 percent of the profit. In other words, market share also often is a proxy for profitability. “On average, 80 percent of the economic profit pool was concentrated in the hands of just one or two players in each market,” say Bain and Company consultants. In other words, it really matters if a firm is number three in any market.
In mature, competitive markets where there are barriers to entry; economies of scale or are capital intensive, three companies tend to dominate, with the largest player holding 40-50 percent of the market, the second-largest holding 20-30 percent, and the third-largest holding 10-20 percent market share, the rule suggests.
That virtually perfectly corresponds to a market share pattern of 4:2:1, as the number-three provider tends to have less than 10 percent share, in that pattern. Of course, real life does not always confirm to our predictions, but the theory is often quite useful.
The “rule of three” seems to explain outcomes in capital intensive industries. But network effects might explain outcomes in software markets that are not so capital intensive, but are highly reliant on scale effects.
AI is interesting because it contains both forces simultaneously. Data center, cloud computing as a service and frontier AI models are the most capital intensive, and most likely to take a “Rule of Three” shape.
On the other hand, graphics processing units and agentic AI might tend to take a “winner takes most” structure. Vertical applications might not fit either pattern.
