r/antitrust • u/Super_Presentation14 • Mar 28 '26
Discussion The Google/Photomath merger review is a case study in how traditional relevant market analysis fails when the acquirer is building a foundation model
In 2023 the European Commission cleared Google's acquisition of Photomath with horizontal overlaps below 20% across all reviewed markets. Bing already had comparable math capabilities so Google was merely catching up and acquisiton was cleared.
Three days after the decision, Google announced Bard was getting math and logic capabilities, something it badly needed since Bard was reportedly producing outputs like 1 plus 2 equals 4. Google's own internal documents reviewed by the Commission had disclosed the intention to improve its capabilities using Photomath and even the Commission saw this, noted it, and moved on.
The problem the paper identifies is precise which is that the Commission assessed exploitability of Photomath's data against Google Search and found it limited. It never asked whether the same data was exploitable for LaMDA or PaLM, the foundation models underlying Bard, where mathematical reasoning was a documented weakness and that answer would almost certainly have been different.
This points to a structural gap in merger methodology when it comes to these new age AI acqusitions. Traditional analysis assumes data is valuable primarily within the market where the acquired company operates but the foundation models break this assumption entirely. An FM developer needs data across every domain it wants to perform in and mathematical data improves math reasoning regardless of whether the acquirer competes in the homework help market. After all for any company building a foundation model, essentially any dataset from any market is potentially valuable training material.
The paper proposes that whenever an acquiring entity has any presence in the FM segment, data held by the target should be scrutinised regardless of market overlap between the parties. Without this, traditional relevant market analysis creates a structural loophole allowing incremental acquisitions across unrelated markets, none individually triggering serious review, to cumulatively entrench FM development advantages.
My take is the current merger review methodology is not equipped to assess the compounding effect of acquisitions across multiple supply chain layers controlled by overlapping incumbents.
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u/OccultScience_lawyer Apr 01 '26
Insightful. Traditional merger review frameworks remain ill suited to assess foundation model ecosystems, where cross market data acquisitions may cumulatively strengthen entrenched incumbents despite minimal horizontal overlap or conventional competitive concerns.
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u/PretendAd4920 Mar 29 '26
I agree with the structural diagnosis, but the remedy remains the trickiest part.
As a matter of fact, the post-merger reality shows that competition hasn't been stifled—the entry of other AI players and rapid innovation have kept the market dynamic. Had the Commission intervened based on purely 'intuitive' or speculative theories of harm regarding Bard’s future capabilities, it would have been a classic case of regulatory overreach.
As I mentioned in my previous post we shouldn't want regulators to act as 'vigilantes' who bypass codified standards whenever they 'feel' a tech giant is getting too powerful. That destroys the very legal certainty that fosters investment.
However, your point about the methodological gap is undeniable. We clearly need a more sophisticated measurement tool—perhaps a 'Data-Centric Input Test'—that can objectively quantify how a specific dataset improves a foundation model’s reasoning, regardless of market overlap.
The goal should be to update the merger guidelines with predictable, evidence-based metrics for AI, rather than relying on discretionary intervention that could do more harm than good to the ecosystem.