IBM’s value has plunged after the company issued a preliminary second-quarter earnings update that fell short of Wall Street’s expectations. Ahead of next week’s full earnings report, IBM CEO Arvind Krishna issued a statement on Tuesday warning that second-quarter revenue will miss expectations as customers continue to redirect IT budgets toward AI initiatives.Why it matters for developers: The double-digit drop in IBM stock highlights another consequence of the AI buildout: Enterprise spending is shifting faster than some incumbent vendors can adapt.Here’s what developers and platform teams should know.IBM surprised investors on Tuesday by releasing a preliminary look at its second-quarter results, more than a week before its scheduled earnings report on July 22. The company now expects second-quarter revenue of $17.2 billion, up 1% year over year, with non-GAAP diluted earnings per share of $2.93, up 5%.Those figures fell short of Wall Street’s expectations: FactSet analysts had forecast revenue of $17.86 billion and earnings per share of $3.01, the Associated Press reported. The early update did little to calm investors, sending IBM shares sharply lower.But the miss itself wasn’t the full story. Management’s explanation for the weaker outlook may be even more important for developers and platform teams.Capex shifts toward AI hardwareIBM now derives much of its business from enterprise software and infrastructure. As a major player in the enterprise (B2B) market, it provides software solutions ranging from security and data analysis to “middleware,” the software that lets myriad apps, databases, and platforms interconnect.Software enterprise products are generally high-margin, making them great for a company’s bottom line. The problem for IBM is that the AI boom is causing many of its largest customers to cut spending on software services, enabling them to transfer funds toward purchasing the hardware components needed to build large AI data centers.“In the last few weeks of June, we saw clients shift their quarterly capex spend toward servers, storage, and memory purchases to secure supply-constrained infrastructure ahead of expected price increases,” Krishna writes in the announcement. “This dynamic impacted client buying patterns.”“In the last few weeks of June, we saw clients shift their quarterly capex spend toward servers, storage, and memory purchases to secure supply-constrained infrastructure ahead of expected price increases.”However, Krishna also points out that IBM itself dropped the ball because it “did not anticipate the magnitude of the capex reprioritization.”“These conditions require our teams to execute perfectly, and this quarter we faltered. We did not adapt and move quickly enough, and numerous large deals failed to close on the timelines we expected, driving the majority of our shortfall.”“These conditions require our teams to execute perfectly, and this quarter we faltered. We did not adapt and move quickly enough, and numerous large deals failed to close on the timelines we expected, driving the majority of our shortfall.”Middleware costs fall on developersFor software developers, the chain reactions of this capex reallocation will be felt nearly immediately. When enterprises freeze spending on high-margin middleware and off-the-shelf software from IBM and its competitors, the burden of consolidation falls entirely on internal engineering teams. To address the lack of expensive vendor solutions, platform engineers will be tasked with paving “golden paths” and building Internal Developer Portals (IDPs) using open-source tools.If a company refuses to license the software required to connect legacy databases smoothly to new, expensive AI environments…developers will have to build those bridges manually.Building bridges without vendor toolsIf a company refuses to license the software required to connect legacy databases smoothly to new, expensive AI environments — like building ETL pipelines to feed legacy mainframe data into vector databases for Retrieval-Augmented Generation (RAG) — developers will have to build those bridges manually. This means more time writing custom APIs, maintaining brittle integrations using open-source alternatives like Apache Kafka or Envoy, and stitching systems together by hand.What follows the infrastructure buildoutOne way to interpret IBM’s warning is that many enterprises are still building AI infrastructure. Rather than expanding software budgets, organizations are prioritizing spending on servers, storage, memory, and other hardware needed to support AI workloads.Once that infrastructure is in place, executives will expect it to generate business value. For engineering teams, the next phase is likely to focus on building AI applications, agentic workflows, retrieval systems, and production services that justify the billions already invested in compute.In the near term, that could leave developers balancing two competing priorities of integrating new AI infrastructure while working within tighter software budgets. Whether those software budgets rebound later this year remains to be seen.The post “We did not adapt and move quickly enough”: What IBM’s earnings miss says about enterprise AI spending appeared first on The New Stack.