AI Benefits - But at What Cost? - Ardalis (Steve Smith)

In 2026 we can all agree that AI and agentic development are certainly exciting topics which many see yielding great productivity gains. But as the investor-subsidized pricing of these services gives way to realistic and profitable business models, where will the real costs land?

As many businesses downsize staff or pause hiring to see how these new models and tools actually perform in the real world, the trillion dollar question is:

At what cost?

Current AI Model Pricing

Subscription Pricing (as of March 2026)

The major consumer AI products have positioned themselves as affordable monthly services, with pricing tiers designed to normalize usage:

OpenAI / ChatGPT [1]

Plan	Price
Free	$0/month
Go	$8/month
Plus	$20/month
Pro	$200/month
Business	$25/user/month (annual)
Enterprise	Custom

Anthropic / Claude [2]

Anthropic’s Claude Max plans run $100-$200/month. These plans offer higher rate limits for heavy users, and are popular among developers using Claude Code for autonomous agentic coding workflows.

GitHub Copilot [3]

Plan	Price
Free	$0/month (50 agent requests, 2,000 completions)
Pro	$10/month or $100/year
Pro+	$39/month or $390/year
Business	Custom

Cursor (Anysphere) [4]

Plan	Price
Hobby	Free
Pro	$20/month
Pro+	$60/month
Ultra	$200/month
Teams	$40/user/month

xAI / Grok [5]

Plan	Price
Free (via X)	$0
SuperGrok	$30/month

API / Pay-As-You-Go Pricing (as of March 2026)

For developers building agentic workflows and integrating models directly, all major providers offer token-based API pricing:

OpenAI (GPT-5.4 family) [6]

Model	Input	Output
GPT-5.4	$2.50 / 1M tokens	$15.00 / 1M tokens
GPT-5.4 mini	$0.75 / 1M tokens	$4.50 / 1M tokens
GPT-5.4 nano	$0.20 / 1M tokens	$1.25 / 1M tokens

Cached inputs are discounted 90% (e.g., $0.25/1M for GPT-5.4 cached input). A 50% batch discount is also available for asynchronous workloads. [6]

Anthropic (Claude 4 family) [7]

Model	Input	Output
Claude Opus 4.6	$5.00 / 1M tokens	$25.00 / 1M tokens
Claude Sonnet 4.6	$3.00 / 1M tokens	$15.00 / 1M tokens
Claude Haiku 4.5	$1.00 / 1M tokens	$5.00 / 1M tokens

Both providers also charge separately for built-in tools: OpenAI’s web search tool runs $10/1,000 calls; code interpreter containers cost $0.03-$1.92 per 20-minute session depending on memory size. [6]

The Hidden Math: Subscriptions vs. Actual Compute Cost

The subscription prices above look reasonable next to a Netflix or Spotify bill. But they obscure a critical mismatch between what users pay and what it actually costs to serve them.

Anthropic’s own Claude Code documentation [8] cites an average cost of $6 per developer per day, with 90% of users staying under $12/day. At $6/day, a single developer costs Anthropic roughly $180/month to serve - yet the popular Max plan is priced at $100-$200/month. An independent analysis found that a user on a $20/month Claude subscription can generate up to $163 in actual compute costs. [9]

At heavier usage, reported real-world costs are far higher. One developer reported spending $200-$300/day in API costs before switching to a self-hosted open source model. [10] Another found that overrunning their Max subscription by “an hour or two” to finish a project cost nearly $600 in additional API charges. [11] Ed Zitron covered this pattern in depth in his March 17, 2026 essay. [12]

AI Company Revenues and Profitability

The headline numbers sound impressive until you look at what it costs to produce them.

OpenAI

OpenAI claims $13.1 billion in revenue for full-year 2025 and “only” $8 billion in losses for that year. [13] As of March 2026, it reports $25 billion in annualized revenue. [14] However, these figures are difficult to reconcile with earlier reports: through September 2025, the company reportedly earned $4.3 billion in revenue against $8.67 billion in inference costs alone. [15] The Wall Street Journal reported a $12 billion loss in Q3 2025 alone [16] - which makes the claim of only $8 billion in losses for the full year implausible.

OpenAI has raised over $200 billion in total funding. [12] It remains deeply unprofitable.

Anthropic

In a March 9, 2026 legal filing, Anthropic’s CFO Krishna Rao stated the company had made “exceeding” $5 billion in cumulative lifetime revenue while spending “over” $10 billion on inference and training combined. [17] In other words, Anthropic has burned through the majority of its $30 billion in total fundraising and has less than $5 billion in cumulative revenue to show for it.

Anthropic has previously projected gross margins above 70% by 2027 - but that projection deliberately excludes training costs, which are enormous and continuous. [18] Per The Information, if training costs were included (as would be standard when calculating true gross margins), the business picture is dramatically worse. Anthropic’s current gross margin is approximately 38%. [18]

Cursor (Anysphere)

Cursor crossed $2 billion in annualized revenue in March 2026 [19] - but reached that milestone having raised $3 billion in venture capital in 2025 alone. $2 billion annualized is $166 million per month. The company has never disclosed its margins, but given that Cursor is a wrapper on top of frontier models from OpenAI and Anthropic (whose own economics are broken), any margin-positive outcome at scale requires those upstream providers to remain artificially cheap.

Harvey (legal AI)

Harvey raised $200 million at an $11 billion valuation in February 2026, [20] with only $190 million in ARR ($15.8 million/month). [21] The company has raised over $800 million total, including $300 million in February 2025 [22] and $300 million in June 2025. [23] Like most AI startups, it converts hundreds of millions in capital into tens of millions in monthly revenue with no clear path to profitability.

Lovable (vibe coding)

Lovable reached $400 million in annualized revenue ($33.3 million/month) as of March 2026 with only 146 employees [24] - after raising $15 million in February 2025, [25] $200 million in July 2025, [26] and $330 million in December 2025. That’s over $545 million raised to produce $33 million/month in revenue.

The Pattern

As Zitron summarizes in his March 2026 analysis [12]: “Every single AI startup without exception does the same thing: turn hundreds of millions of dollars into tens of millions of dollars, or a few billion dollars into a few hundred million dollars. None of them are improving their margins. None of them have a solution.”

The Infrastructure Bet

Underlying all of this is an extraordinary infrastructure bet. Hyperscalers (Google, Amazon, Meta, Microsoft) are on track to spend approximately $700 billion in capital expenditure in 2026 alone on AI infrastructure, [27] on top of the roughly $800 billion already spent through 2025. [28] This has created a parallel ecosystem of “neocloud” AI compute companies - CoreWeave, Nebius, Nscale - that raise billions in debt backed by hyperscaler contracts, then use that debt to buy NVIDIA GPUs.

CoreWeave’s 2025 annual report shows that 77% of its revenue came from Microsoft and NVIDIA alone. [29] NVIDIA has separately committed to a $6.3 billion cloud computing capacity order with CoreWeave - effectively renting back GPU capacity it helped CoreWeave build - further suggesting that organic third-party demand for AI compute is quite thin. [30]

The debt financing for these data centers increasingly comes from pension funds and insurance companies, attracted by what appear to be stable, yield-bearing infrastructure assets. [31] Data center debt deals have received junk bond ratings - CoreWeave received a B+ on one 2025 deal - [32] yet markets have largely ignored the risk. The analogy to mortgage-backed CDOs rated AAA before the 2008 financial crisis is uncomfortable but hard to dismiss.

Betting on AI Productivity for Your Company’s Software Development Needs

Given all of the above, something has to give. And we all know what it is: price. The only way any of these companies can get close to profitability is by either dramatically decreasing their costs (which none of them are even suggesting they’re trying to achieve) or by dramatically increasing their prices. Just growing revenue won’t do it - they’ll still be losing money on every new subscription and trying to “make it up on volume.”

C’mon Kid, The First One’s Free

What these companies hope for is to convert enough companies over to using their tools in such a way that switching costs are too high for them to accept. Give the companies subsidized AI compute, get them to lay off staff and build their products around the models, and then jack up the prices. If your business software relies on these models to operate, you’re going to have to pay or else find a different way for your software to satisfy your users’ needs. And if you’ve already laid off most of your software developers in favor of an “agents all the way down” approach, it’s going to be hard to reverse that trend when suddenly the costs for those agents increase manyfold.

The big question is, will AI compute costs increase only by an order of magnitude, or by two or more?

I predict that by the end of 2027, agentic AI subscriptions like Claude Code and Copilot will increase by somewhere between 10x and 100x from their January 2026 levels.

I know, that’s an awfully wide range, but you know what they say: predictions are hard, especially about the future. The thing is, even if the prices only increase by 10x, if you’re making bets on these services based on today’s rates, you’re going to be in for a rude awakening.

What Can You Do?

If you’re trying to make plans for the future of your business, the best thing you can do is have contingency plans. Unless you’re extremely risk-tolerant and you can just YOLO the business on whatever AI might cost in the future, it behooves you to consider several alternatives. Essentially you’re creating a portfolio that manages your overall risk, just like an investment portfolio.

On the less risky end, you have your current software development teams. You know what they cost, you know the value they provide, and it’s likely they’ll be more effective with certain AI tools assuming these tools remain reasonable in price.

On the more risky end, you have the Ralph-loop-many-agent-team that can do things very fast but uses up a lot of tokens in the process and generally requires close supervision to make sure it’s doing the right things. Putting all of your eggs into this basket exposes you to much greater risk if the cost chart for the team suddenly looks like a hockey stick.

What most companies are doing is of course somewhere in the middle, based on their risk tolerance and a host of other factors. And that’s probably the right thing for you to do, too. But deliberately, not accidentally. Keep close tabs on your AI spending, and if possible, the value you’re getting from it. And be ready to adjust your portolio balance if the value proposition of one approach, such as leaning heavily on AI services, suddenly changes.

Conclusion

AI tools are genuinely useful today. If you are a software developer using Claude, Copilot, or Cursor, you have probably experienced real productivity gains - faster boilerplate, quicker research, a second opinion at 2am when nobody else is around. That value is real, and it isn’t going away.

What is going to change is what you pay for it.

The economics documented in this article are not a matter of opinion. Every major AI lab is spending far more than it earns. The subscription prices that made AI tools feel as casual as a streaming service are artificially low - subsidized by hundreds of billions of investor dollars that cannot flow forever. When the price correction comes, it will likely be abrupt, not gradual, because these companies have no clean lever to pull. They can’t raise prices slowly when a 10x increase is the minimum needed to approach sustainability.

The businesses most at risk are the ones making irreversible decisions based on today’s rates. Laying off engineering staff, shipping products that depend entirely on agentic pipelines, or building organizational muscle memory around “just ask the AI” - these are bets that assume current pricing will hold. The data suggests it won’t.

The businesses best positioned for what comes next are the ones treating this moment as a portfolio decision, not a pivot. Keep your engineering team capable of understanding the code that AI generates. Instrument your AI costs now, before they matter, so you understand what a 10x increase would actually do to your margins. Know which parts of your workflows are genuinely AI-dependent and which ones just feel that way because the price makes it easy. Invest in the tools and processes that would survive a dramatic price increase - and use the cheap-AI window we’re currently in to build everything that depends on it while you still can.

The trillion dollar question at the top of this article - “at what cost?” - doesn’t have a final answer yet. But the direction is clear. The cost is going up. The only question is whether you’ll be ready when it does.

Sources

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