Anthropic eyes $10B compute deal with Meta as it lines up autumn IPO
Anthropic is in advanced talks to secure as much as $10 billion worth of computing power from Meta Platforms over the next two years, a move that would both supercharge its AI ambitions and potentially reshape Meta’s role in the artificial intelligence infrastructure market.
According to reports based on documents shared with investors, Anthropic outlined the proposal to Meta in June. The arrangement under discussion would see Anthropic paying Meta on a monthly basis for access to the social media giant’s massive AI computing capacity, which Meta originally built to support its own models and products.
The lease, if finalized, would run for up to two years, but both sides would have the option to exit the agreement before the term expires. Negotiations are still ongoing, meaning the ultimate value, duration, and scope of the deal could shift before any formal contract is signed.
For Anthropic, the proposed agreement is fundamentally about securing the raw computing muscle required to train, refine, and deploy next‑generation AI models at scale. Modern AI development hinges on access to huge clusters of specialized chips and energy‑hungry data centers. Reliable, long‑term compute access has become as strategically important as financing or talent for companies competing at the frontier of model performance.
Meta, on the other hand, would gain a new revenue stream from infrastructure that has so far been primarily used to fuel its own AI initiatives-ranging from recommendation systems and content moderation to foundation models and developer tools. Turning part of that capacity into a leasable product would move Meta closer to the position long held by cloud providers that sell computing resources to third parties.
If the deal goes ahead, Meta would find itself in more direct competition with existing AI‑focused infrastructure providers such as CoreWeave and Nebius. Both of those companies have carved out niches supplying GPU‑dense compute to AI labs and startups that lack the resources to build their own hyperscale facilities. Meta’s entry into this space would add a tech giant with deep capital reserves, extensive data center experience, and a strong incentive to monetize its AI build‑out beyond advertising.
The broader backdrop is a global race among technology companies to secure high‑end chips, reliable electricity, and suitable data center real estate. Demand for top‑tier GPUs and AI accelerators routinely outstrips supply, and many regions are straining to provide the power and cooling needed for dense compute clusters. Against that environment, Anthropic’s strategy of locking in multi‑year access to infrastructure signals how much weight it places on guaranteed capacity.
The Meta talks are not Anthropic’s only long‑term infrastructure move. Earlier this month, the company signed a 20‑year data center lease with TeraWulf, a firm best known for its bitcoin mining operations. That agreement is expected to be repurposed to provide additional computing resources for Anthropic’s future AI workloads, effectively transforming a crypto‑mining‑style power‑hungry facility into an engine for AI training and inference.
Together, the potential Meta compute lease and the TeraWulf data center deal illustrate a broader pattern: Anthropic is systematically building a diversified infrastructure base ahead of its next phase of growth. By spreading its capacity across multiple providers and contract structures, the company reduces its dependency on any single supplier and mitigates the risk of future bottlenecks or pricing shocks in the compute market.
How transformative these agreements will be-both operationally and financially-ultimately depends on their final terms and on how much of the reserved capacity Anthropic actually consumes. A headline figure of up to $10 billion over two years suggests extremely heavy usage, but the practical cost will vary with utilization rates, hardware mix, and the balance between training and inference workloads.
Parallel to its infrastructure push, Anthropic is moving forward with preparations for a public listing. Banks advising the company have already begun organizing meetings between Anthropic executives and potential investors, the usual pre‑IPO roadshow process in which leadership lays out growth plans, business models, and risk factors to secure demand for the eventual share offering.
Those investor meetings could pave the way for an initial public offering as early as October, subject to broader market conditions and the company’s final internal decision. If Anthropic does list in that timeframe, it would likely reach public markets ahead of OpenAI, which is reportedly targeting a listing several years later, around 2027.
Chinese AI player DeepSeek is also understood to be preparing for a future listing, but Anthropic is positioned to be among the first of the new generation of frontier‑model developers to offer shares to public investors. That timing could give Anthropic an advantage in defining how public markets value standalone AI labs that do not sit within larger cloud or consumer tech conglomerates.
Regulatory and policy developments are also factoring into Anthropic’s story. Prior to news of the potential IPO, the company obtained permission from the US government to restore access to its Mythos 5 model for selected enterprises and federal agencies. This approval-coming amid heightened scrutiny of advanced AI systems-adds another data point for banks and investors evaluating the company’s regulatory risk profile and its relationships with government customers.
For prospective shareholders, the combination of infrastructure deals, government approvals, and a possible Meta partnership paints a picture of a company trying to de‑risk its operational backbone while broadening its revenue and deployment channels. At the same time, it underscores how capital‑intensive frontier AI has become: even heavily funded startups must negotiate multi‑billion‑dollar compute arrangements simply to stay competitive.
Strategically, a Meta-Anthropic agreement would signal an evolution in how big tech firms think about AI infrastructure. Until recently, most large consumer platforms focused on building compute exclusively to support their own products, occasionally opening limited access through developer programs. Acting as a large‑scale compute lessor to an independent AI lab blurs the line between cloud provider and platform company, potentially foreshadowing a future in which AI infrastructure is as widely traded as storage and general cloud compute are today.
There are also competitive and partnership implications. Anthropic already has deep relationships with other cloud and infrastructure players, and bringing Meta into that mix raises questions about data governance, model training pipelines, and how workloads are allocated between different providers. Investors will want clarity on whether Meta’s role is purely infrastructural or whether it comes with strategic cooperation on research, distribution, or product integration.
From Meta’s perspective, monetizing AI infrastructure via large leases could help justify its massive capital expenditures, which have drawn scrutiny from some shareholders focused on near‑term profitability. Revenue from third‑party AI customers offers a narrative in which Meta’s data center build‑out is not just a cost center tied to advertising and social products, but also the foundation of a separate, high‑margin infrastructure line of business.
The timing of these moves is critical. As interest rates, tech valuations, and regulatory pressures shift, windows for high‑profile IPOs can open and close quickly. By locking in or negotiating significant compute access ahead of a potential listing, Anthropic can present itself to the market as less exposed to short‑term supply shocks in chips or data center availability-an important reassurance in a sector where delays in training runs can derail product timelines and revenue projections.
For the broader AI ecosystem, Anthropic’s trajectory highlights how closely intertwined infrastructure, capital markets, and policy have become. The next generation of AI leaders is being shaped as much by who can secure long‑dated power and compute contracts and navigate regulators as by who can ship the most capable model in a benchmark.
As the negotiations with Meta progress, and as details of the TeraWulf deal and other infrastructure partnerships become clearer, investors and competitors alike will be watching for signals on three fronts: how much compute Anthropic is really locking in, how quickly it can convert that into differentiated products and revenue, and whether Meta’s tentative step toward becoming an AI infrastructure provider marks the start of a broader industry realignment.