Until late November, when the epic saga of OpenAI’s board breakdown unfolded, the casual observer might be forgiven for assuming that the industry around generative AI was a vibrant competitive ecosystem.
But this is just not the case—nor has it ever been. And understanding why is key to understanding what AI is, and what threats it poses. Put simply, within the context of the present paradigm of constructing larger- and larger-scale AI systems, there isn’t a AI without Big Tech. With vanishingly few exceptions, every startup, latest entrant, and even AI research lab relies on these firms. All depend on the computing infrastructure of Microsoft, Amazon, and Google to coach their systems, and on those self same firms’ vast consumer market reach to deploy and sell their AI products.
Indeed, many startups simply license and rebrand AI models created and sold by these tech giants or their partner startups. It’s because large tech firms have accrued significant benefits over the past decade. Due to platform dominance and the self-reinforcing properties of the surveillance business model, they own and control the ingredients vital to develop and deploy large-scale AI. In addition they shape the motivation structures for the sphere of research and development in AI, defining the technology’s present and future.
The recent OpenAI saga, wherein Microsoft exerted its quiet but firm dominance over the “capped profit” entity, provides a robust demonstration of what we’ve been analyzing for the last half-decade. To wit: those with the cash make the foundations. And at once, they’re engaged in a race to the underside, releasing systems before they’re ready in an try and retain their dominant position.
Concentrated power isn’t just an issue for markets. Counting on a couple of unaccountable corporate actors for core infrastructure is an issue for democracy, culture, and individual and collective agency. Without significant intervention, the AI market will only find yourself rewarding and entrenching the exact same firms that reaped the profits of the invasive surveillance business model that has powered the business web, often on the expense of the general public.
The Cambridge Analytica scandal was just one amongst many who exposed this seedy reality. Such concentration also creates single points of failure, which raises real security threats. And Securities and Exchange Commission chair Gary Gensler has warned that having a small variety of AI models and actors at the muse of the AI ecosystem poses systemic risks to the financial order, wherein the consequences of a single failure might be distributed rather more widely.
The assertion that AI is contingent on—and exacerbates—concentration of power within the tech industry has often been met with pushback. Investors who’ve hurried from Web3 to the metaverse to AI are keen to understand returns in an ecosystem where a frenzied press cycle drives valuations toward profitable IPOs and acquisitions, even when the guarantees of the technology in query aren’t ever realized.
However the attempted ouster—and subsequent reintegration—of OpenAI cofounders Sam Altman and Greg Brockman doesn’t just bring the facility and influence of Microsoft into sharp focus; it also proves our case that these business arrangements give Big Tech profound control over the trajectory of AI. The story is fairly easy: after apparently being blindsided by the board’s decision, Microsoft moved to guard its investment and its road map to profit. The corporate quickly exerted its weight, rallying behind Altman and promising to “acquihire” those that desired to defect.
Microsoft now has a seat on OpenAI’s board, albeit a nonvoting one. However the true leverage that Big Tech holds within the AI landscape is the mixture of its computing power, data, and vast market reach. As a way to pursue its bigger-is-better approach to AI development, OpenAI made a deal. It exclusively licenses its GPT-4 system and all other OpenAI models to Microsoft in exchange for access to Microsoft’s computing infrastructure.
For firms hoping to construct base models, there’s little alternative to working with either Microsoft, Google, or Amazon. And people at the middle of AI are well aware of this, as illustrated by Sam Altman’s furtive seek for Saudi and Emirati sovereign investment in a hardware enterprise he hoped would rival Nvidia. That company holds a near monopoly on state-of-the-art chips for AI training and is one other key choke point along the AI supply chain. US regulators have since unwound an initial investment by Saudi Arabia into an Altman-backed company, RainAI, reinforcing the issue OpenAI faces in navigating the much more concentrated chipmaking market.
There are few meaningful alternatives, even for those willing to go the additional mile to construct industry-independent AI. As we’ve outlined elsewhere, “‘open-source AI”—an ill-defined term that’s currently used to explain all the pieces from Meta’s (comparatively closed) LLaMA-2 and Eleuther’s (maximally open) Pythia series—can’t by itself offer escape velocity from industry concentration. For one thing, many open-source AI projects operate through compute credits, revenue sharing, or other contractual arrangements with tech giants that grapple with the identical structural dependencies. As well as, Big Tech has an extended legacy of capturing, or otherwise attempting to hunt benefit from, open-source development. Open-source AI can offer transparency, reusability, and extensibility, and these could be positive. Nevertheless it doesn’t address the issue of concentrated power within the AI market.
The OpenAI-Microsoft saga also demonstrates a incontrovertible fact that’s continuously lost within the hype around AI: there isn’t yet a transparent business model outside of accelerating cloud profits for Big Tech by bundling AI services with cloud infrastructure. And a business model is essential while you’re talking about systems that may cost a whole bunch of tens of millions of dollars to coach and develop.
Microsoft isn’t alone here: Amazon, for instance, runs a marketplace for AI models, on which all of its products, and a handful of others, operate using Amazon Web Services. The corporate recently struck an investment deal of as much as $4 billion with Anthropic, which has also pledged to make use of Amazon’s in-house chip, Trainium, optimized for constructing large-scale AI.
Big Tech is becoming increasingly assertive in its maneuverings to guard its hold over the market. Make no mistake: though OpenAI was within the crosshairs this time, now that we’ve all seen what it looks like for a small entity when an enormous firm it is dependent upon decides to flex, others shall be being attentive and falling in line.
Regulation could help, but government policy often winds up entrenching, slightly than mitigating, the facility of those firms as they leverage their access to money and their political clout. Take Microsoft’s recent moves within the UK for instance: last week it announced a £2.5 billion investment in constructing out cloud infrastructure within the UK, a move lauded by a major minister who has clearly signaled his ambitions to construct a homegrown AI sector within the UK as his primary legacy. This news can’t be read in isolation: it’s a transparent try and blunt an investigation into the cloud market by the UK’s competition regulator following a study that specifically called out concerns registered by a variety of market participants regarding Microsoft’s anticompetitive behavior.
From OpenAI’s (ultimately empty) threat to depart the EU over the AI Act to Meta’s lobbying to exempt open-source AI from basic accountability obligations to Microsoft’s push for restrictive licensing to the Big Tech–funded campaign to embed fellows in Congress, we’re seeing increasingly aggressive stances from large firms which are attempting to shore up their dominance by wielding their considerable economic and political power.
Tech industry giants are already circling their wagons as latest regulations emerge from the White House, the EU, and elsewhere. Nevertheless it’s clear we want to go much further. Now’s the time for a meaningful and robust accountability regime that places the interests of the general public above the guarantees of firms not known for keeping them.
We’d like aggressive transparency mandates that clear away the opacity around fundamental issues like the info AI firms are accessing to coach their models. We also need liability regimes that place the burden on firms to exhibit that they meet baseline privacy, security, and bias standards before their AI products are publicly released. And to start to handle concentration, we want daring regulation that forces business separation between different layers of the AI stack and doesn’t allow Big Tech to leverage its dominance in infrastructure to consolidate its position available in the market for AI models and applications.
But when governments keep giving the identical narrow group of industry interests primacy in guiding policy, we won’t get far. After last week’s events, it’s all too clear what these firms serve: their bottom line.