CMA materials can be found here:

The UK Competition and Markets Authority has published an initial report on its investigation into AI Foundation Models (FMs), which addresses competitive concerns in the FM development phase and their downstream deployment.

Upstream competitive concerns:

The advent of AI technology promises to revolutionise every facet of our modern economy, from healthcare and financial services to education. Leading the way to this next digital age are the reusable Foundation Models trained on massive datasets of text and images, which can be adapted and applied to any industry task or sector.

Currently, competition appears healthy – the CMA notes that an estimated 160 FMs have emerged since 2018. Moreover, unlike search engine or social media markets, where network effects establish "winner-takes-most" outcomes, FMs are not natural monopolies. ChatGPT's service does not improve and thus reduce the likelihood of effective competition just by virtue of its popularity – in this it diverges markedly from the dominant players of web 2; Chrome's dominance is, for instance, very much tied to its high usage due to improvements made to algorithm with repeated use. Likewise, users become locked into Facebook by the presence of their friends, past, present and future. The CMA does recognise, however, that user-generated data could become an important characteristic of FMs in the future — perhaps leading to economies of scale and network effects — even if it is largely unusable at present. For the moment, as the report notes, switching between FMs is relatively cheap and easy, even if certain properties of FMs — their ability to customise their outputs to users or their integration in web ecosystems — may lend themselves to 'lock in' effects.

Notwithstanding this current positive market characteristic, there are fears that the vast data resources and computational power, which is required for the training of FMs and dominated by incumbent Big Tech firms, could distort competitive outcomes.

The vast majority of foundation model providers rent computational capabilities from a limited number of cloud providers (namely, Google, Amazon, and Microsoft). Indeed, the massive cost and complexity of doing otherwise — which would require significant investment in data centres and semiconductor technology that the Tech Giants already possess — means that, in practice, the only alternative to renting from these firms is partnership, tying new entrants to the infrastructure and expertise of the existing tech giants. For instance, in 2019 Microsoft partnered with Open AI by investing $1 billion towards the development of the company's supercomputer. The fact that these large technology firms are already vertically integrated in the supply chain poses a threat to the future competitiveness of the market.

By the same token, these large firms also have access to proprietary data. It is currently unclear how significant this will be: there is a public stock of high-quality data but the CMA notes that these resources may soon be "fully exploited for gaining improved model performance". The effects of this exhaustion are yet to be determined. Perhaps, the proprietary data to which Big Tech companies — especially those with search engines — have access will form a vast competitive edge. The CMA notes that the outcome of the Intellectual Property concerns that FMs have prompted will have a vital effect on this issue: if data-scraping practices are ruled to violate copyright, high-quality training data would become scarcer — which would benefit the companies with proprietary data at their fingertips. The CMA qualifies this threat with the potential for 'synthetic data', which is artificially generated by other FMs, to provide a cheap and accessible alternative.

Professor Furman, author of the highly influential Furman Report, also highlighted these issues when giving evidence to Parliament on the new Parliament Digital Markets, Competition and Consumers Bill. He explained that the requirement for large amounts of data does not "necessarily lend [artificial intelligence] to a new upstart competitor but would instead entrench the power of the existing ones".

Downstream competitive concerns:

The broad uses of FMs in downstream markets are yet to be fully realised with consumer and industry interactions largely being limited to general purpose chatbots, but the potential is enormous. Boston Consulting Group, for instance, has predicted that the total market for generative AI will increase from $18 billion in 2023 to $121 billion in 2027. Tools tailored to specific industries and tasks could see enormous efficiency boosts across the economy. However, the cost of developing in-house tools means that with notable exceptions — Bloomberg has developed its own proprietary LLM, BloombergGPT, integrated into Bloomberg Terminal — most downstream uses will rely on licenses from FM developers.

The CMA also recognises that the relative generality or specificity of FMs will influence the future downstream competitiveness of the supply chain. If highly-specific FMs that have been fine-tuned to specific tasks — for instance, an FM that deals with drafting legal contracts — can remain viable in future markets, then a wide range of organisations may be able to innovate in this market. But if larger and highly generalised FMs can outperform the specialised FMs then 'the number of FMs available could consolidate, as a small number of models could meet the needs of most users. Because of the vast amounts of resources required to build generalised FMs, it would only be the largest and most entrenched market players that could develop these products. But the CMA raises this concern as a point of uncertainty; it is unclear how the downstream ecosystem will develop in the coming years.

As it stands, key FM model developers also typically have some consumer interface — Google's Bard, for instance — and it is likely that upstream players would be interested in further developing tailored consumer and industry tools as their demand proliferates. The natural concern highlighted by the CMA then emerges if Google, as an FM provider with substantial market power, refused or restricted access to its FM in order to weaken its dependent competitors. Like most similar scenarios where a dominant platform has the incentive to foreclose customers with whom they also compete, the answer lies in interoperability measures (see the Commission's Google Shopping case, in which Google was fined 2.4 billion euros for preferring its proprietary comparison shopping service over those of rivals).


Major causes of concern in both the upstream (data inequality) and downstream FM market (dual supplier competitor relationships) are arguably solvable by allowing mechanisms for data sharing and preventing bundling and exclusion. Indeed, the central conclusion of Professor Furman's report to Parliament was that pro-competition policies and legislation are absolutely necessary to support innovation in digital markets. Specifically, interoperability protocols are essential to counteract the natural advantages enjoyed by Big Tech and foster healthy competition from new entrants. Obtaining an internationally recognized metric for comparing FM performance would form one method of making the FM ecosystem easier to navigate for startups and consumers.

Transparency and interoperability are not novel ideas. When IBM, then the dominant supplier of mainframe computers, was forced by the US/EU authorities to open up interfaces in the 1970s, this gave rise to innovation in peripherals like PCs, paving the way for future tech titans, Apple and Microsoft.

Fortunately, as the CMA's report attests, policymakers appear aware of the lessons of the past. The CMA's guiding principles for AI, which stress the need for access, choice and flexibility, indeed self-consciously "draw on lessons learned from the evolution of other technology markets and how they might apply to FMs, as they are developed". Moreover, in Europe, the EU Data Governance Act similarly proposes wider data transfer measures; the Digital Markets Act requires that large search engines share data, including ranking, query, click and view data, again, might provide a legislative remedy to the distortion of AI markets.

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