ai spending3 articles
Google Burned Through More Cash Than It Made Last Quarter. Thanks, AI.
Despite record revenues of $119.8 billion in Q2 2026, Google has reported negative free cash flow for the first time since going public, spending $44.9 billion on AI infrastructure against $39.1 billion in operating cash flow. The company has significantly increased its capital expenditure forecast to as much as $205 billion for 2026, reflecting the massive cost of building AI data centers, which caused its stock to drop around 4.5%. Google's long-term AI position also faces uncertainty, with a delayed flagship Gemini model, reports of falling behind competitors, and a wave of departures among top AI researchers.
IBM Says AI Delayed Its Software Sales, Not Killed Them. Wall Street Isn't Convinced.
IBM is reassuring investors that a software sales slowdown in Q2 was merely a temporary delay caused by customers prioritising AI infrastructure spending, not a sign of permanent demand destruction, with CEO Arvind Krishna noting that a third of the stalled deals have already closed in the first weeks of the new quarter. Wall Street remains sceptical, questioning whether the shift reflects deferred or fundamentally changed spending priorities. Meanwhile, IBM is positioning AI as its next major growth opportunity, notably through Project Lightwell, a new service helping enterprises secure legacy open source software using AI, which has already attracted major financial institutions at $1 million per year.
Half a Billion Dollars in One Month: What Happens When Nobody Watches the AI Tab
An unnamed company reportedly spent $500 million on Anthropic's Claude in a single month after failing to set usage limits on its AI licenses, highlighting how quickly enterprise AI costs can spiral out of control. Broader industry examples, such as employees using AI to check the weather or misusing large models for simple tasks, point to widespread inefficiency in how companies deploy AI tools. Experts argue that businesses need greater internal AI expertise, better model selection, and smarter usage controls to manage costs and ensure quality outcomes.