Invoice Enclosed<br>- CERIAS - Purdue University

PEOPLE

PARTNERS

SEMINARS

NEWS & BLOGS

RESEARCH

EDUCATION

RESOURCES

STUDENTS

SYMPOSIUM

Search CERIAS

Go

Invoice Enclosed

Invoice Enclosed<br>--><br>Tuesday, July 07, 2026 by Prof. Spafford<br>in General,

In my recent series of posts, beginning with New Myths for Old, I examined some impacts of AI on the cybersecurity workforce and profession. That examination included the claim that LLMs make software developers, security analysts, and incident responders optional, as well as the layoffs conducted under that banner. I called those cuts the patch-instead-of-fix mindset extended to staffing. Patches defer costs rather than remove them, and the bill for this one is now arriving, itemized.

The first line item is the expected return that is missing. PwC’s 2026 Global CEO Survey found that 56% of chief executives report AI has neither increased revenue nor reduced costs in the past twelve months. Bain’s June survey of enterprise adopters found that the double-digit cost reductions firms expected mostly failed to materialize: among firms that measured outcomes, the largest share saw improvements of 10% or less. Both echo the controversial MIT report from last August, which found that roughly 95% of enterprise generative-AI pilots produced no measurable profit-and-loss impact. Gartner projects that more than 40% of agentic AI projects (deployments in which the system selects and executes actions with little human authorization) will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. The same analysis estimates that only 130 of the thousands of vendors selling agentic AI offer the genuine article. The rest practice what Gartner labels agent washing.

The expense side of the ledger tells a similar story: over the past year, large employers have swung from mandating AI use to rationing it. Accenture originally told senior employees they risked losing out on promotions if they did not use AI. By late June, its agentic-AI strategy lead was explaining, in audio leaked to 404 Media, that the firm was trying to stop employees from draining its token reserves on tasks such as converting PDFs into slides: Spend is becoming very unpredictable; and leadership … are still asking the question of whether they’re getting value. Meta employees consumed 73.7 trillion tokens in roughly thirty days, a pace encouraged by internal leaderboards; with internal AI costs heading toward billions of dollars for 2026, the company is now imposing central caps. Amazon deleted an employee-built internal leaderboard that ranked developers by token consumption. Uber exhausted its 2026 AI coding budget in four months and now caps employees at $1,500 per month per tool. Enterprise-AI vendors describe clients whose annual AI budgets are exhausted in one or two months, and CNBC’s summary of the resulting boardroom choice could be this post’s epigraph: tokens or humans. The press has named both phases: tokenmaxxing on the way up, token minimizing on the way down.

An organization that orders its employees to use a tool and, months later, to ration it, has not identified any value in the interval between the two orders. This should have surprised no one. Campbell’s Law is well known: the more a quantitative indicator is used for decision-making, the more it corrupts the process it is intended to monitor. (Goodhart’s Law is its pithier cousin.) Usage became the target, and usage is what employees produced.

The second line item in the invoice is the rehiring. Careerminds surveyed 600 HR professionals who had conducted layoffs in the prior year. Two-thirds of employers that cut jobs for AI are already rehiring for the eliminated roles, more than half within six months of the layoff. Among those rehiring, about 31% spent more bringing the roles back than the cuts had saved, and another 42% broke even; about one in four came out ahead. Only 8.4% said the restructuring delivered what was promised and would repeat it unchanged. Orgvue’s survey of senior decision-makers found 55% of firms that made AI-motivated redundancies admitting wrong decisions. A follow-up Orgvue study found something more revealing: 23% of companies that made layoffs relied on general assumptions about what AI could do rather than analysis of what the eliminated employees did. That is consistent with the Oxford Economics observation cited in New Myths for Old: the AI label often dressed up ordinary cost-cutting. Robert Half reports that 32% of U.S. hiring managers eliminated a role primarily because of AI and later rehired for the same or a similar position.

Some particular cases trace the arc:

Klarna, which had boasted that its chatbot did the work of 700 customer service agents, resumed hiring people in May 2025. The CEO’s diagnosis: cost had been a too predominant evaluation factor, and the result was lower quality.

The Commonwealth Bank of Australia declared 45...