Anthropic’s anticipated initial public offering could become a defining event for the artificial intelligence industry, testing investor appetite for fast-growing AI companies at a time when questions about revenue growth, infrastructure spending and profitability are intensifying.
However, longtime technology analyst Gil Luria believes investors may be better served by focusing on the companies supplying the infrastructure behind the AI boom than on which model developer ultimately wins the race.
Luria, head of technology research at DA Davidson, described Anthropic as a potentially uncertain investment after it becomes a publicly traded company, arguing that the commercial success of individual AI model developers is less important to the broader investment thesis than the continued expansion of AI adoption.
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“Anthropic will end up being a very speculative investment,” Luria said in a conversation with CNBC on Monday. “They may do well, they may not.”
His assessment comes as Anthropic prepares for a highly anticipated IPO and investors scrutinize its financial performance for evidence that the rapid growth of generative AI can translate into sustainable business returns. The company’s reported revenue of nearly $17 billion in the third quarter has attracted particular attention because it represents a slowdown in growth compared with the second quarter.
The figures have fueled debate over whether the AI industry is approaching a more challenging phase after a period of extraordinary expansion. Yet Luria does not interpret Anthropic’s deceleration as sufficient evidence that the wider AI investment cycle is losing momentum.
Instead, he argues that the market’s long-term prospects depend on whether businesses continue to use AI more extensively and increase their spending on the technology, regardless of which model provider captures the largest share of demand.
Anthropic’s growth trajectory has become a focal point for investors because of its strong position in the enterprise AI market. According to Luria, the company reached an advantageous position earlier in the year, when its products appeared to command a substantial share of corporate demand.
“They were at peak power earlier this year,” he said of Anthropic. “They had almost the entire enterprise market. That’s when their growth was really skyrocketing.”
That advantage has since faced pressure as rival OpenAI has caught up and businesses have become more selective about how they deploy AI models.
“What happened since then is OpenAI caught up, and enterprises realized they were spending so much that they had to start using open weight models as well, and that’s why Anthropic decelerated,” Luria said.
The shift highlights two forces reshaping the AI software market: intensifying competition among leading model developers and a growing emphasis on controlling the cost of deploying AI across large organizations.
Enterprises have reasons to use different models for different tasks rather than depend on a single provider. Commercial considerations can include performance, reliability, integration requirements, and the cost of running models at scale. Open-weight models, which allow users to access and run model weights under their respective licensing terms, can offer organizations additional flexibility over deployment and operating costs.
For Anthropic, the challenge is therefore not simply to maintain technical leadership. The company now must also persuade customers that its models deliver enough value to justify their cost as alternatives improve and corporate buyers seek to manage AI budgets.
This competition complicates the investment case for model developers. Strong revenue growth can demonstrate demand, but investors must also determine whether that demand will remain durable, whether customers will continue paying premium prices, and how much spending is required to maintain a competitive position.
A slowdown in growth does not necessarily mean revenue is contracting or that demand has disappeared. It can, however, affect valuations when investors have priced a company on expectations of exceptionally rapid expansion. In the case of a prospective IPO, the distinction matters because the price investors are willing to pay will depend not only on current revenue but also on confidence in future growth and the company’s ability to defend its market position.
IPO Performance Could Influence AI Investor Sentiment
Anthropic’s public-market debut is likely to attract attention beyond the company itself because it would provide investors with a major opportunity to assess the value of a leading AI model developer through the public equity market.
A strong listing could bolster confidence in the commercial potential of generative AI and encourage investors to support other companies operating in the sector. A disappointing debut, by contrast, could raise questions about valuations and the sustainability of the financial expectations attached to AI businesses.
Luria acknowledged that a weak IPO could temporarily disrupt the broader AI trade by unsettling investors. However, he does not believe Anthropic must deliver an exceptional market debut for AI-related investments to retain their appeal.
In his view, a “reasonably successful” IPO would be sufficient to prevent the listing from becoming a significant source of pressure on the sector.
The fortunes of an individual AI company do not necessarily determine the prospects of the entire industry. Anthropic could experience slower growth or encounter valuation pressure even as businesses continue adopting AI tools and technology companies expand their infrastructure to accommodate rising workloads.
Equally, a successful IPO would not automatically establish that the sector’s valuations are justified. Investors would still need to assess whether the company’s growth, margins and capital requirements support the price placed on its shares.
For the wider market, Anthropic’s listing could serve as a test of how public investors value businesses whose competitive positions depend on rapid product development, expensive computing resources and an evolving market in which leading providers can lose ground to rivals.
Microsoft, Amazon, Google and Chipmakers Offer a Different Investment Case
Luria’s preferred approach is to focus on the infrastructure providers that benefit from the expansion of AI usage rather than attempt to predict the ultimate winner among model developers.
He identified two questions as central to assessing the industry’s health: whether people are using AI models more than before and whether they are paying more for them.
If both trends continue, he expects investment in AI infrastructure to remain strong, creating opportunities for established cloud providers and semiconductor manufacturers.
“That means AI investments are going to continue, and that’s good for your Microsoft, your Amazon, and Google because they’re going to be the ones serving it, and they’re going to be buying chips primarily from Nvidia, Broadcom, Micron,” he added.
The argument rests on the contrast between competition at the application and model layers of AI and the infrastructure required to run the technology. Model developers compete to attract customers with increasingly capable products, but those products still require computing capacity, specialized chips, data centers and cloud services.
Large cloud providers are positioned to benefit when businesses require more computing resources to train, deploy and operate AI systems. Semiconductor companies, meanwhile, supply components used in the servers and data centers that support these workloads. This creates a potentially broader investment opportunity than backing a single model developer. Even if competition reduces the market share or pricing power of individual AI companies, total demand for computing could continue to rise if AI applications become more widely used.
However, the infrastructure thesis is not without risks. Continued investment does not guarantee attractive returns for every supplier. Semiconductor and cloud businesses must still contend with capital expenditure, capacity constraints, competitive pressures, and the possibility that customers eventually reduce spending if AI applications fail to generate sufficient economic value.
Investors are keenly waiting to see if AI usage and monetization can grow quickly enough to justify the infrastructure being built. If they do, cloud providers and chipmakers could benefit even as the competitive balance among model developers changes. But if spending outpaces the revenue generated by AI applications, infrastructure companies could also face pressure.
Together, Luria’s assessment offers an alternative to viewing Anthropic’s IPO as a verdict on the entire AI boom. The listing will be an important event for investors seeking to value a leading model developer.
Anthropic’s decelerating growth raises legitimate questions about competitive advantage, enterprise budgets, and the durability of premium pricing. OpenAI’s progress and the adoption of open-weight models suggest that corporate customers have more options, potentially limiting the ability of any one provider to dominate the market.
At the same time, competition could expand AI adoption by giving businesses more choices across different performance levels, deployment models, and price points. That outcome could benefit infrastructure providers even if individual model companies experience uneven growth.
Anthropic’s IPO could still unsettle sentiment if investors reject its valuation or question its growth outlook. But Luria’s argument is that the longer-term AI investment case depends less on a single listing and more on whether businesses continue expanding their use of AI and allocating more money to it.
If that demand persists, Microsoft, Amazon, Google, and major chipmakers could remain important beneficiaries of the AI expansion, regardless of which model developer ultimately emerges as the market leader.