Dive Brief:
- The rise in price of tokens will lead AI coding to become more expensive than an average developer’s salary by 2028, according to a Gartner report released Wednesday. The shift will happen as consumption-based pricing overtakes subscription models among key providers.
- Changing pricing models is also making AI costs a highly variable figure, keeping enterprise tech leaders from accurately forecasting and controlling spending, the report said. Vendors frequently lack transparency into how token consumption is calculated and billed.
- The cost structure is changing as enterprises still struggle to find maturity in AI projects and measure their business impact, said Nitish Tyagi, senior principal analyst at Gartner, in a statement. “Software engineering leaders are increasingly concerned as token-driven AI spend becomes harder to justify, with budgets often being depleted earlier than expected.”
Dive Insight:
Adopting AI into software workflows has become the default, leading employees to spend less time writing code and more time managing AI outputs.
As AI use proliferates the enterprise, the cost is adding up, especially in engineering departments, Gartner found. Token overspending was linked to how software engineering leaders govern usage, with many using ungoverned, autonomous agents in their workflows.
“AI coding costs will continue to rise as infrastructure investment and profitability challenges push model pricing higher,” Tyagi said. “At the same time, as more developers adopt AI tools, light users are expected to rapidly become mainstream users as familiarity and reliance increase, driving further growth in token consumption and overall spend.”
Few enterprises have a clear strategy with defined goals and outcomes for their AI projects, according to Altimetrik data published in April, but they choose to forge ahead rather than miss out on the AI moment. Just over one-quarter of enterprise C-suite leaders reported having complete, real-time visibility into what their AI systems cost to operate, a KPMG report released Wednesday found.
It’s likely many agents are working on tasks in the background for days, without leaders’ knowledge or audits, Rahsaan Shears, AI enterprise transformation leader at KPMG LLP, told CIO Dive in an email.
“The CFO does not see it. The CIO may not either,” Shears said. “That is enterprise AI economics right now: costs compounding inside workflows no one has fully instrumented.”
To keep AI usage and costs from outrunning budgets, CIOs must consolidate and closely track usage, keeping watch over consumption across cloud platforms, copilots, agent frameworks, coding tools, business workflows and team-level experiments, Shears said.
Organizations should pursue a tokenomics semantic model, which connects usage to cost and cost to ownership. This approach will also map ownership of AI to business value, workload, behavior patterns and risk.
While overspending is a costly mistake for enterprises, lack of visibility into what an organization’s AI systems operate is a bigger risk, Shears said. If a provider degrades a capability, or cost spikes, tech leaders will be in the dark.
Shears said CIOs should investigate which agents to throttle, which harnesses are running discretionary loops on premium models and which workflows are mission-critical.
“Without that visibility, organizations are governing an agent fleet with no instrumentation and no triage plan,” Shears said.
