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Anthropic’s spending exposes AI’s profit problem

The cost to develop and serve frontier AI models is getting steeper by the day. 

In a filing to the SEC on Monday, Anthropic reported that it expects to spend more than $895 billion in the coming years, with a large majority of that spending going towards infrastructure. The company said it will incur around $448 billion of these commitments before the end of 2030. 

Alongside its plans for an infrastructure shopping spree, the company also reported a twelvefold increase in quarterly revenue as of September 30, hitting $17 billion compared to $1.4 billion in the same period last year. However, the company also reported a $23.3 billion net loss in the third quarter ending in September, driven largely by computing costs, compared to a $12.1 billion net loss from the same quarter the year before. 

• The company noted in the filing that, in order to meet its "substantial and growing financial obligations," it must achieve "significant" revenue growth to achieve the necessary cash flow. 

• "Our revenue is primarily consumption-based, and much of it is not subject to long-term contractual commitments, while our compute and infrastructure commitments generally extend over multiple years," the filing reads. "As a result, we may enter into these obligations based on revenue expectations that may not be realized." 

• Additionally, the company's IPO filing last week contained a similar warning: The company stated plainly that it "may not achieve or sustain profitability in the future."

However, Anthropic isn't the only lab grappling with cost problems. Major AI labs are facing price pressure on both sides. While they deal with the mounting cost of developing and building the infrastructure needed to power their AI, the consumers of these models are also increasingly becoming jaded with their own bills, with many turning to open-source and efficient alternatives over state-of-the-art, proprietary APIs. 

As a result, major AI labs are touting cost cuts to their frontier systems, with recent models from Anthropic, OpenAI and Google all matching or slightly undercutting one another in input, output and cached token costs. 

Still, the price wars can only take these labs so far: On Sunday, SpaceXSI's Elon Musk responded to a post on X regarding a reset of Grok Bot, the company's "always-on" AI agent, claiming that "the cost of serving Grok Bot was unsustainably high relative to revenue." 

In order to stay afloat, something has to give on at least three fronts: price, usage, or capital, Scott Bickley, advisory fellow at Infotech, told The Deep View. This means these labs need to charge more for the enterprise customers that are driving most of their revenue, get more out of the compute they're buying, and figure out a way to juice more revenue from consumers that doesn't rely on subscriptions. 

"Profitability requires some combination of higher effective prices, lower serving cost per task, or a longer useful life for the capital," said Bickley. "Labs that can’t move at least two of those three will need continued outside funding to stay in the game.”

Though the revenue that these companies are bringing in is starting to stack up, the infrastructure spending commitments are becoming astronomical. It begs the incredibly daunting question: How long can this last? While the debate regarding whether or not the industry is in a bubble right now continues to be disputed, it's undeniable that the success of these companies relies heavily on widespread buy-in. And while enterprises have been the primary target for the past few years, the adoption has to spread beyond just enterprise contracts in order for these labs to even come close to scraping the revenue they need to break even. Otherwise, they're either going to have to figure out another way to subsidize their visions of the future, or get on board with the shift towards new and efficient architectures of AI, like Jev and Pathway. 

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PRODUCTS

Hume AI launches the Jev of voice AI

Voice is often the easiest way to interact with AI, but only if the model can understand what you actually mean, including how you said it. Hume AI thinks it has the answer.

On Monday, Hume AI unveiled its Hume Audio Decision API, built on its audio-native decision model. It uses the same architecture Jev and other similar models use to take the world by storm—instead of just predicting the next token like a large language model, decision models can answer many structured questions in parallel and show confidence levels. In voice interactions, this means an AI system doesn't have to rely only on a transcript to interpret users' needs; instead, it can understand intent, emotion, and more. 

“There is a lot of opportunity to be able to leverage that raw audio and do really interesting things from an interrogation perspective to understand that, so that you can then figure out where to go build your agents, how to go build your agents, where they would perform well," Andrew Ettinger, Hume CEO, told The Deep View. 

I got to see the model in action pre-release, which put it into perspective. Chris Ren, a product lead at Hume, spoke to the model in a demo and, in real time, it was able to give feedback on different questions such as how many speakers were in the room, the language used, the tone of voice, the gender of the speaker, whether there was music playing in the background and more, alongside a confidence threshold for each response. Developers can, of course, add their own questions to best fit their applications in formats that include "yes/no", "choice", "scale" and "span". 

To measure its accuracy, Hume tested the model against real human testers and, in initial testing, it matched a human rating panel's collective judgments more closely (70.7%) than any individual rater (56.5%). Hume also reported: 

• Using average accuracy-vs.-speed across leading benchmarks, Hume's Audio Decision Model outperformed every other model on the market. 

• Hume's Audio Decision Model's average score over 10 leading benchmarks was 82%. 

• The closest competitors scored as follows: Gemini 3.8 Flash (78%), Qwen 3.8-Omni-Flash (76%), and Inkling-Small (67%). 

There is also a bulk option for analyzing many existing audio recordings. In a demo, there were three hours of simulated patient–physician recordings with eight or nine questions about the conversations queued. Hume explained that, in this case, the model could track the patient's emotions and identify signals such as distress that a traditional transcript would miss.

In the agentic-first world we live in, predicting the next token often falls short because agents' actions depend on instantaneous input from several simultaneous queues. That is one reason TypeSafe AI's Jev model made such a popular debut, along with its impressive cost cuts that make it much cheaper than leading models from frontier labs. Its release also couldn't have come at a better time, as the industry pushes toward efficiency and lower costs. OpenAI followed suit, releasing its own Decisions API to customers shortly after. Hume is uniquely positioned to capitalize on the moment, with deep expertise in emotionally aware models and interfaces, an area with many practical use cases for developers to create engaging experiences for users. 

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POLICY

OpenAI’s power plan targets a PR crisis

As AI demand continues to soar, the compute to run it and the resources to back it are only becoming more scarce. OpenAI is looking for solutions. 

On Monday, the AI lab announced the Grid Protectors Program, aiming to provide "affordable, reliable electricity" for the communities where it's building out data centers. The company will work with utilities, regulators and policymakers to reduce electricity demand of its data centers when the grid is "under strain." 

"Our goal is to make campus flexibility a resource that utilities can call on when communities need it most," OpenAI said in its announcement. "We have a once-in-a-generation opportunity to strengthen the electric grid by building campuses that can adjust their power use when communities need it most." 

OpenAI's plan involves designing and building facilities that can flexibly adjust their electricity use during times when demand is higher, such as during an extreme weather event. 

• The company is also working with grid operators on batteries and on-site power to "smooth changes in demand." 

• It also laid out an existing model for a planned campus in Georgia, saying that it has struck a deal with Georgia Power to free up 1,000 megawatts of flexible demand response, the equivalent electricity demand of 700,000 households. The lab noted that it has already committed to paying in full for the infrastructure and electric-service costs.

• OpenAI said that the initiative aims to mitigate the need for costly updates to the grid as a means of meeting peak demand, as well as keeping costs down for rate payers. With AI representing a "significant part" of growing electricity demand, the company said that "families and small businesses should not be asked to subsidize a compute campus’s electricity needs." 

However, this program isn't OpenAI's only plan to give AI a better reputation. Axios reported over the weekend that the lab is attempting to rebrand these facilities as "AI compute campuses" amid its preparation for a “PR blitz” following midterm elections.

Amid the historic infrastructure buildout, AI labs and tech giants are facing an unprecedented challenge in garnering the resources they need to realize their visions, with some even turning to space-based data centers as a solution. Still, it's no secret that data centers are facing a PR crisis, especially in the US. These facilities are largely reviled among the American public. With an average of 7 in 10 Americans opposing data center construction in their area, with many concerned about the environmental impact, these facilities themselves have become a political rallying point for politicians on both sides of the aisle. However, these facilities represent more than just an eyesore and a high electricity bill. They represent the physical manifestation of a daunting transformation that could impact the social and economic fabric of our society. While OpenAI's initiative is a step in the right direction, changing the perception of AI represents a major hurdle for frontier AI labs, and will likely require more than just flexible energy use and a name rebrand. 

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