a16z Analyzes AI Computing Power: Doubling Revenue, Pressured Stock Prices, and Astonishing Capital Expenditure

Original Title: Charts of the Week: Head In The Neoclouds

Original Author: Moses Sternstein, a16z

Editor’s Note: In the context of generative AI driving a new wave of computing power investment, discussions about AI infrastructure are shifting from "Is there enough GPU?" to "Who can sustainably provide computing power?" As the consensus grows around model training, inference demand, and data center expansion, a more fundamental question emerges: Can the rapid growth in computing power demand truly translate into stable profits and cash flow?

In the latest edition of "Charts of the Week" published by a16z New Media, author Moses Sternstein delves into the growth, valuation, and profitability contradictions of the AI computing power market through the lens of new cloud companies like CoreWeave, Nebius, and Applied Digital, extending the discussion to horizontal SaaS, model routing, and talent competition in cutting-edge laboratories.

In this article, the author does not simply assess whether AI demand is strong; rather, he breaks down the current AI transactions into a set of more fundamental structural issues: how existing infrastructure is being repriced, why revenue growth has not aligned with market expectations, and why the competitive focus in the AI industry is shifting from mere expansion to efficiency and returns.

First, there is the rediscovery of infrastructure value. In the past, land along railroads, natural gas pipelines, and cable television networks served specific industries before being repurposed into telecommunications and internet infrastructure. Today, a similar asset revaluation is occurring. Some new cloud companies that originally served cryptocurrency mining now possess operational experience in power, data centers, cooling systems, and high-density computing; after the explosion of AI demand, these capabilities quickly transformed into scarce computing power supply. This signifies that the competition for AI infrastructure does not start from scratch; early advantages often stem from the recombination of old assets, energy resources, and engineering capabilities.

Second, there is the coexistence of high revenue growth and profitability uncertainty. The early revenue growth rates of new cloud companies like CoreWeave once exceeded those of cloud giants like AWS in their early stages, but the capital markets did not grant them equal recognition. The reason is that new clouds are not typical asset-light software businesses. GPU procurement, power access, data center construction, chip depreciation, and debt interest will rise in tandem with scale, sometimes even faster than revenue growth. This means that revenue expansion can only demonstrate strong AI computing power demand but does not automatically prove that the business model has a sufficiently high capital return rate. What the market is truly waiting for is whether these companies can convert orders and revenue into sustainable free cash flow.

Third, the value of software is being re-differentiated according to AI impacts. In the past, the market worried that generative AI would generally weaken the moats of SaaS companies, but Atlassian's performance indicates that AI may also become a tool to enhance customer spending and product stickiness. Meanwhile, cybersecurity and observability software continue to receive valuation premiums because AI has expanded potential risks and increased enterprises' reliance on mature solutions. This means that the so-called "SaaS apocalypse" will not occur uniformly. Whether AI serves as a substitute product, drives down prices, or expands demand is becoming a new standard for software valuation differentiation.

Fourth, AI applications are shifting from "stacking tokens" to optimizing tokens. In the past, companies often preferred to directly call the most powerful models or give engineering teams a budget to experiment; now, companies like Databricks are beginning to use intelligent routing to match different prices and performance models based on task difficulty, reducing costs while maintaining effectiveness. A decrease in token unit price does not necessarily mean a contraction in total AI spending: as unit costs decrease and application scenarios increase, total token consumption and overall market size may continue to rise. Efficiency and demand are not mutually exclusive; they may form a mutually reinforcing cycle.

If this article can be compressed into a single judgment, it is this: AI infrastructure has proven it can create rapid growth, but the next phase of victory will depend on whether companies can convert that growth into higher capital efficiency. In this sense, the subjects of this discussion are no longer just whether CoreWeave and others can become the next generation of cloud giants, but whether the entire AI industry can transition from computing power expansion to sustainable business returns.

Above the "New Cloud"

In the early 20th century, the Southern Pacific Railroad Company owned a large amount of idle construction rights on cleared land connecting cities and towns across the United States. The scope of railroad rights-of-way extended far beyond the tracks themselves, leaving many corridors available for development along the route.

Thus, this railroad company laid a communication network along the rail line, naming it the "Southern Pacific Railroad Internal Networking Telephony." By the 1970s, the company began commercializing this network, opening it to a broader user base.

Subsequently, two things happened simultaneously: on one hand, the monopoly structure of the long-distance telephone market came to an end; on the other hand, fiber optic cables began to become commercially viable. The original communication corridors were transformed into fiber optic lines, and this network later became known by its English abbreviation, "Sprint." The assets that once served the railroads thus became the backbone of the telecommunications revolution.

It was not only the railroad companies that transformed existing physical networks into larger-scale commercial technology infrastructures.

In the 1980s, Williams Company repurposed idle natural gas pipelines into fiber optic channels, establishing WilTel. This company was later sold and eventually renamed WorldCom. By the 1990s, the one-way coaxial cables laid for cable television businesses underwent a massive and costly upgrade, ultimately becoming the infrastructure for Comcast and Charter to provide broadband internet services to consumers.

This leads to another type of enterprise: they also possess ready-made infrastructure, and these assets are now being significantly transformed and repriced to meet the demands of an emerging technology—this is the "new cloud" (neocloud) company.

In summary, new cloud companies were mostly engaged in energy and computing-intensive cryptocurrency mining businesses before the AI wave arrived. Suddenly, those who possess rights to electricity usage, infrastructure, and experience in building and managing high-intensity computing loads—such as CoreWeave, which also includes a large number of GPUs—are now on one of the hottest tracks in the market.

Of course, this is not a strict comparison of like for like. However, if we observe the three largest publicly listed new cloud companies, their revenue growth rates are indeed remarkable.

We can only estimate the early cloud business revenues of ultra-large cloud service providers, but the general trend is clear: new cloud companies are growing rapidly, and significantly faster than the growth rates of the three major cloud service providers in their early stages.

It should be noted that in the entire computing power sales market, new cloud companies are still relatively small players.

They still have a long way to go to reach the scale of ultra-large cloud service providers.

Ultra-large cloud service providers generate revenues each quarter that are several orders of magnitude higher than those of new cloud companies. However, at the same time, CoreWeave achieved $2.6 billion in revenue in just about 25 quarters, a milestone that AWS took until its 40th quarter to reach. Again, it is worth emphasizing: these companies are indeed growing very quickly.

With such high growth rates and riding the wave of the AI industry, one would expect investors to be quite excited. To some extent, this is indeed the case, but the reality is more complex.

Although these companies have generally performed well in their recent earnings reports, CoreWeave's stock price has still fallen by about 16% over the past year; only Nebius is relatively close to its previous high point.

Thus, the overall story remains positive, but for the largest new cloud companies, the appeal is evidently weaker.

Recent market performance has been relatively flat, partly because many growth expectations may already be reflected in valuations.

For capital-intensive enterprises like new clouds, the price-to-sales ratio is not the most suitable valuation metric, but it still illustrates the issue quite intuitively. Smaller, faster-growing Nebius and Applied Digital have valuation premiums far exceeding those of the much larger CoreWeave. CoreWeave's revenue is still doubling, but it is no longer keeping pace with the 400% to 450% growth rates of the leading companies.

If there is a real issue with new cloud companies, it is not growth but long-term profitability. New cloud companies need to continuously invest in chips, power, and physical infrastructure to scale up, and these costs are not low:

Taking CoreWeave as an example, its revenue growth is indeed impressive, but its capital expenditure is even more astonishing. Other massive costs include chip depreciation—depreciation amounts have already exceeded half of the revenue—and the interest expenses incurred from borrowing to build expensive infrastructure in advance, which are still on the rise.

This article does not intend to judge whether new cloud companies will ultimately succeed or whether their current stock prices are reasonable. Beyond the inherent heat of the topic, what is truly meant to be conveyed here is that new cloud companies precisely represent a microcosm of the tug-of-war in the entire AI transaction.

On one hand, they are in a vertical market—the computing power market—that is much larger than anyone had previously anticipated and continues to expand, creating a historically rare growth rate; on the other hand, the cost of building such enterprises is also at a historical high, requiring substantial and continuously depreciating fixed infrastructure.

Is Horizontal SaaS Making a Comeback?

Here’s a brief update on the ever-changing market landscape of the "SaaS apocalypse." One of the companies that suffered the most during the recent sell-off of software stocks has performed quite well over the past month.

Over the past 30 trading days, horizontal software companies have ranked at the top of the IGV software ETF constituents—despite having given back some of their gains since the data collection began.

Overall, the fundamentals of these companies remain robust. Notably, Atlassian has not declined as previously expected under the impact of AI.

This productivity software company has achieved "double beats" in both performance and guidance: cloud business revenue grew by 31% year-on-year, and the growth rate of revenue backlog orders was even higher. But perhaps the more critical signal is that AI is becoming a booster for business growth rather than a hindrance. Atlassian stated that its AI assistant Rovo has been widely adopted; at the same time, customers using Rovo are spending at a growth rate nearly twice that of non-Rovo users.

This is good news for Atlassian, good news for Rovo, and good news for horizontal SaaS.

However, the overall valuation of horizontal SaaS still lags slightly behind other software categories.

With few exceptions, including Atlassian, horizontal SaaS companies generally have expected price-to-sales ratios that are below the levels corresponding to the "growth---valuation multiple" trend line.

To reiterate, horizontal SaaS has only gone through a relatively good "month." To convince the market that the "SaaS apocalypse" has been canceled, one month of performance is far from sufficient.

Of course, if your software business falls within the cybersecurity or observability domains, that’s another story—"SaaS apocalypse" has never occurred for these companies.

The cybersecurity sector continues to significantly outperform other categories within the IGV software ETF. In this field, AI has become a tailwind: the market generally believes that AI has heightened awareness of cybersecurity threats, and no enterprise customer would rely on "vibe coding" to cobble together their own security solutions.

Whether this logic ultimately holds true will, of course, require time to test. But at least for now, the situation for traditional software companies is far from uniform.

Investors are highly focused on whether AI will bring benefits or cause erosion for each company, continuously adjusting their original judgments with each new batch of data—this is only natural.

Moving Towards the Efficiency Frontier of Token Investment

The market landscape surrounding model usage, token consumption, and token expenditure management continues to evolve in various interesting ways.

Take Databricks as an example.

On the questions of "which model should we use" and "which model is best," Databricks has not adopted a winner-takes-all approach, nor has it simply given engineers a budget to decide how to spend it. Instead, it posed another question: "What if we develop a solution that automatically assigns the right tasks to the right models?"

Databricks is certainly not the only company doing this, but it has developed a "Smart Router," and the actual results have been quite satisfactory.

Reportedly, Databricks' router can call upon more powerful, higher-priced models when necessary, while using weaker, lower-priced models when conditions allow, thereby "continuously reducing the average task cost by over 30%."

Overall, pursuing the "efficiency frontier" of token expenditure is hard not to be a good thing. This indicates that demand continues to grow, and application scenarios are not only evolving at the performance frontier but also spreading to models that are not as top-tier. In the initial pessimistic narrative, these suboptimal models were originally thought to be quickly eliminated.

As we previously mentioned, efficiency improvements will expand the coverage of demand, which is precisely the dynamic the market hopes to see, similar to the Jevons Paradox.

The Token Price Strength Index from Silicon Data shows that overall price strength is declining, especially as lower-priced open models capture a higher share in the continuously expanding market.

It is necessary to clarify a frequently misunderstood concept once again: these indices measure the cost intensity of token expenditure, not absolute dollar amounts. It also depends on the number of tokens consumed and the comprehensive cost of tokens. This means that even if the price per token decreases, the total consumption of tokens and total expenditure may still continue to rise.

What is truly important is that overall demand continues to grow, and the gradual movement towards efficiency frontiers in pricing and model selection will only further drive this growth. Notably, "AI demand" or "AI adoption" is not a single, homogeneous concept. There remains a significant gap between heavy users and other users. This clearly indicates that "always using the best model" may suit some enterprises but certainly not all.

Today, the market is rapidly forming more alternative options. Overall, this is a good thing.

According to data from Ramp, an enterprise expenditure management platform, all enterprises are increasing AI spending, but the disparity in median expenditure between the top 10% of enterprises and the median enterprise, as well as between the top 10% and the top 1%, is extremely pronounced.

Ramp's data tends to favor tech companies, so this needs to be taken into account when interpreting it. However, the data shows that enterprises in the top 10% of spending have an average AI expenditure about 50 times that of median enterprises.

This distribution is likely not coincidental. Enterprises that can extract more value from AI spending are probably also the ones investing the most—though not every company fits this pattern, at least a significant portion does.

An analysis by Boston Consulting Group of 107 publicly traded companies found that those in the top two quintiles of token usage have revenue growth rates significantly faster than other enterprises.

The core message here is that token demand and usage efficiency are mutually reinforcing: the more value enterprises gain, the more tokens they consume.

This process, of course, involves a repeated trade-off between investment and return, and R&D will always include some upfront costs. But for the vast majority of enterprises, indiscriminately "piling on tokens" has never been an effective strategy.

Therefore, it is clearly a good thing that enterprises will increasingly not need to adopt such practices in the future.

The Talent War in Cutting-Edge Labs

New Media recently welcomed two outstanding team members to OpenAI, so we’ll conclude with a few interesting charts that illustrate the talent recruitment situation in cutting-edge AI labs.

Dario Amodei recently expressed concern that employees are placing money above mission. Based on data from Levels.fyi, this concern may not be unfounded.

If we interpret the data at face value, Anthropic is offering engineers very high salaries, far exceeding those of engineers at comparable companies like Google and Tesla.

It seems that being a member of the tech team is indeed a good thing.

Additionally, there’s another set of data that is also quite interesting.

According to data from Live Data Technologies—presented by Truist Securities—the talent sources for various labs have significant overlaps as well as clear differences.

Both companies have recruited a significant number of talents from large-cap tech firms, but only OpenAI has hired from Nvidia and Tesla, and both instances occurred in 2026.

Databricks, Snowflake (recently), Palantir, and DeepMind are also common sources of talent for both companies.

Both labs have recruited a considerable number of employees from Salesforce and Stripe.

However, the overlap between the two seems to end there. Anthropic has recruited many talents from SaaS companies, while OpenAI has done so very little; OpenAI, on the other hand, has hired extensively from consumer internet, platform markets, and ad tech companies, whereas Anthropic has relatively fewer hires in these areas, except for Airbnb, Netflix, and Uber.

As for what these differences mean, that is left for everyone to interpret.

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