The AI Pricing Question
AI prices are going up. More expensive models at least answer one of the big unknown questions holding back corporate AI investments.
AI needed to turn a profit at some stage. After years of experiments, businesses are finally using AI in production. CFOs are even seeing ROI on some of those long touted AI experiments. Content production workflows have been accelerated, campaign optimisation is happening faster, and data management workflows have gained new capabilities. A wide range of AI start-ups are seeing plenty of interest, if not large scale adoption. A new set of technology firms have become big winners. The only thing missing is profitable AI model makers. We won't need to wait long for them.
The impending IPOs of Anthropic and OpenAI are dominating both business and technology news. After multiple record breaking funding rounds, the two giants of the AI world will be listing on the Nasdaq in the autumn. The relevant paperwork was filed with the SEC last week. Going public is an important milestone for any startup. However, these listings have much broader significance because they provide the first glimpse into the true costs of generative AI technology. As such, they mark a watershed moment for AI. Until now, the true cost of running large language models has been shrouded in secrecy. The transparency of a stock market listing will pierce that veil, meaning that the underlying cost of generative AI will become public for the first time. That's critical for the future of the technology.
Predictable Costs
Executives have invested fortunes in AI pilots, without any clear picture of the long term costs of the technology. That makes it extraordinarily difficult to measure the long term ROI of a particular AI use case. The likes of Claude Code and Codex have been gaining adoption as a productivity aid, particularly for developers. However, few companies are outright replacing staff with AI, despite tech firms proclaiming the contrary. The technology isn't capable enough to replace every task carried out by any particular employee. Even if it were, CFOs don't know whether the technology will cost less than employee wages over the long term.
That's a particularly relevant question, given that model makers are finally raising prices in preparation for their IPOs. Every new model has higher token costs and lower usage limits. All-you-can-eat usage has been replaced with consumption based pricing across the board. Sometimes this backfires, as Microsoft are finding out at the moment. Recent pricing changes to GitHub Copilot have drawn a fierce backlash, resulting in some developers switching to cheaper open source models. The open question is around how far prices will rise. Investors want to know if models are profitable at current prices, both before and after training costs are taken into account.
Clarity into the finances of AI firms is critical to the future growth of the technology. Gartner told their clients not to even bother measuring the ROI of AI pilots. It was simply too new. As with any new technology, models were being run dramatically below cost, so executives were told to wait until they knew what the long term cost of AI would be. AI was unprofitable, but Wall Street won't allow that situation to persist for long. Anthropic and OpenAI have begun the long path to profitability. That has implications for AI budgets and SaaS pricing across the entire technology industry. CFOs need certainty around long term consumption costs. Decisions about which AI pilots to put into production depend on the answer.
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