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 hardware

IBM 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 developers

For 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 tools

If 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 buildout

One 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.

The seemingly unquenchable thirst of the AI data ingestion pipeline spans language, numerical, and tabular data in the first instance, while other tangential platforms have been building large audio, image, and video models at the same time. 

Straddling potentially all of these domains are the file structures where complex documents and forms of unstructured data reside; this is the road less traveled in terms of the source DNA modern AI draws from.

The schema-less, freeform, uncurated data lake

In a bid to bridge connections to the schema-less, freeform, uncurated information that all organizations naturally harbor, enterprise visual intelligence company Valantor announced its acquisition of unstructured information RAG specialist EyeLevel on Tuesday. The acquisition formally launches Valantor’s Enterprise Visual Intelligence platform, combining EyeLevel’s document intelligence with its own operational expertise.

Benjamin Fletcher, CEO and co-founder of EyeLevel, tells The New Stack that where organizations fail to adopt visual intelligence, human-only processing breaks down pretty quickly in the age of AI.

“About 80% of corporate knowledge is in millions of pages of visually complex PDFs, PPTX, and DOCX files,” Fletcher says. “This information is far beyond the capacity of any LLM context window and is effectively inaccessible to LLMs and agents.” 

“We’ve found the golden datasets that teams build by hand routinely carry 10 to 25 percent error rates. Ironically, those same teams often hold AI to a far higher standard than their own people.”

Humans are slow, expensive & prone to errors

He explains that transactional workflows (such as invoice and claims processing) typically involve documents “so visually complex and diverse” that enterprises still rely on humans to process them, who can be slow, expensive, and error-prone. 

“We’ve found the golden datasets that teams build by hand routinely carry 10 to 25 percent error rates,” Fletcher says. “Ironically, those same teams often hold AI to a far higher standard than their own people. If data sovereignty matters to a business, everything gets harder now: solving these problems with AI while your documents stay inside your own infrastructure is the hard mode version of the job, and very few tools can do it.”

Where does invisible corporate information live?

Valantor has noted that while most AI companies concentrate on models, the company itself is “focused on the information those models can’t see” today. The suggestion is that this unseen morass of valuable data is locked inside documents, claims files, contracts, engineering drawings, reports, forms, presentations, and other visually complex content.

Valantor’s flagship platform product, GroundX, operates where data resides, including private cloud, sovereign infrastructure, on-premises deployments, and fully air-gapped environments. 

“GroundX is the ingestion and retrieval layer for unstructured documents,” explains Fletcher. “It is one tightly tuned system where retrieval consumes exactly what ingestion produces. Everything is exposed through REST APIs, SDKs, and MCP. It ships as REST APIs, SDKs, and MCP, and the Helm chart drops straight into a team’s existing deploy pipeline, and our agent harness gives coding agents like Claude and Codex the skills to build the integration themselves.

As part of the acquisition announcement, Valantor is introducing GroundX Studio. The harness capabilities within GroundX Studio integrate with modern AI development environments, enabling developers to build secure AI applications that operate on enterprise knowledge while remaining within existing infrastructure. 

GroundX Studio also extends capabilities to business users, allowing organizations to create AI-powered workflows and applications without extensive custom development.

“Each agent does one small task, so cheaper models are often good enough, and teams that want direct control over cost can run the whole stack on their own hardware with Helm.”

Risk of latency-laden performance and spiraling costs?

If it feels like this new data ingestion stream is going to place a new burden on cloud workloads, application execution latency, database retrieval times, and (of course) overall token usage, then Valantor and EyeLevel say that this consideration has been taken into account by dint of their own platform’s orchestration layers.

“We never send a whole schematic to a language model; our vision model splits each page into its elements first,” Fletcher confirms. “Processing runs in multiple passes at different levels of the document, and everything inside a pass runs in parallel, so there’s a minimum processing time, but it does not scale linearly with page count. Each agent does one small task, so cheaper models are often good enough, and teams that want direct control over cost can run the whole stack on their own hardware with Helm.”

The intersection of AI and handwriting

sWhile we already know that AI and handwriting do mix in the same cocktail glass — the ViWoods AiPaper digital e-ink handwriting tablets have a useful set of AI functions on board, and similar products are available from manufacturers including reMarkable — it’s not a widely deployed use case yet. Valantor claims that its underlying data models and custom heuristics bridge the “data comprehension gap” when processing handwritten annotations.

“Our proprietary vision model, fine-tuned on more than a million pages of enterprise documents, sees the page the way a human does: tables, paragraphs, and figures,” underlines Fletcher. 

He says that handwritten marks are captured as page elements with their layout context intact. Narrow agents then distill each element into a contextual object tuned for both search and LLM completion. 

“Smaller pieces, less cognitive load — that’s how we close the gap, with better accuracy at lower cost, driving better performance and significant cost advantages,” he adds.

Working examples of this technology include Air France-KLM, which used GroundX to develop an AI-powered customer service assistant trained on thousands of policy documents, achieving 96+% accuracy on complex policy-related questions. AskVet used the platform to operationalize more than a decade of proprietary veterinary data, enabling autonomous resolution of up to 85% of customer inquiries while significantly improving operational efficiency.

Is document management sexy now?

Taking all of this on board, are we at the point where we can ask whether document management has just become interesting, compelling, and sexy? 

No, of course it didn’t; it will arguably always suffer from a degree of stigmatized disdain. That may change in the future as we interact more directly with AI tools that begin analyzing the unstructured information we know organizations have been sitting on for so long. For now, it may still remain the corporate equivalent of eating your vegetables — pass the Brussels sprouts and steamed turnips, please.

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