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Received — 20 August 2026 AI Infrastructure Archives - The New Stack

Warp wants to make it easier to build your software factory

On Tuesday, Warp introduced Warp Factories, open infrastructure for building cloud software factories, agentic systems that automate work across the software development lifecycle, which have been popping up in different forms from companies like Augment Code and Chainguard

Warp, an agent development platform, calls Warp Factories “the building blocks” for developers to create their own scalable factories. It’s pitching the infrastructure as the solution for two problems founder and CEO Zach Lloyd says are frequent engineering complaints: 1) measuring and improving coding agent ROI; 2) governance and control.

The aim is to tackle both problems by making sure “the annoying bits [are] taken care of” so developers can focus purely on optimizing factories for specific products. 

As Lloyd writes in a blog post, he “predicts software factories will be as ubiquitous as CI/CD in the next few years.” Experts tell The New Stack they see software factories gaining traction, but they’re more cautious about the timeline.

“I think the software factory is inevitable,” Lee Faus, founder and CEO, Atomic Software and former global field CTO, GitLab, tells The New Stack. “But before software factories become as foundational as CI/CD, the industry needs to solve a deeper infrastructure problem.”

Specifically, he calls out the importance of tracing agent work: “We’re spending a lot of time talking about how to build the software factory,” Faus continues. “I think we’re going to spend much more time asking what becomes the system of record for the factory.”

Build the factory without building all the infrastructure 

Lloyd acknowledges that many organizations already have engineering teams at work building cloud software factories — but he argues that’s too big to be an inside job. 

Warp Factories, thus, emerges as the infrastructure on which developers can build their own factories, providing the core components to speed development without making organizations sacrifice flexibility, programmability, customization, or ownership.

When asked about Lloyd’s take on building infrastructure, Erik Gfesser, long-time engineer, tells The New Stack he agrees it doesn’t make sense for most organizations to tackle it in house.

As Lloyd writes, Warp’s new infrastructure is “built to increase coding agent ROI over time” with evals and benchmarks to measure effectiveness and built-in self-improvement and memory. Developers get queryable metrics on agent throughput, cost, quality, and ROI, visible via the Factory control room, API, and Factory MCP. Scorers evaluate how work items move through the factory with an eye on things like token spend, code quality, and whether or not the work introduced defects. 

From there, those scores power self-improvement loops and benchmarks. “Observer” agents score select agent runs and then search for ways to make improvements by adjusting variables like the harness, model, or context before making PRs to improve underlying factory functionality. Benchmarks, meanwhile, let developers score tasks across different models and harness configurations to compare performance.

Governance gets easier, but there’s more to solve

Per Warp, the infrastructure includes features to address governance and control, alongside factory definitions as version-controlled code, definitions for distinct agents, plus skills, MCPs, and permissions. 

Looking more broadly, Faus tells The New Stack software factory governance will require more than just controlling how agents operate, though:

“A software factory without a record of change risks becoming a very efficient way to manufacture code that nobody can fully explain.”

“Shared infrastructure can make permissions, model access, tool use, MCP connections, policies, cost, and execution environments easier to manage centrally. That’s valuable,” he says. “But governance isn’t just being able to control what an agent is allowed to do. It is being able to prove what it actually did.”

As software factories help speed up code generation, he says the harder problem becomes understanding the scores of interconnected decisions both humans and agents make across the development cycle. 

For example, if one agent triages an issue, another researches it, a third implements it, and still others review and verify it, how can an engineer reconstruct why that change was made six months later? “That record has to remain connected to the change itself,” says Faus. “[Otherwise,] a software factory without a record of change risks becoming a very efficient way to manufacture code that nobody can fully explain.”

Software factories are probably the future, but it will be a slow roll-out

Though Warp’s founder is gung-ho about the rapid rise of software factories, other experts are less certain. Like Faus, Gfesser expects software factory adoption to take time: 

“My expectation is that software factory adoption will likely be fragmented across multiple vendors similarly to the early stages of CI/CD.”

“As an early adopter of CI/CD myself, I know that CI/CD didn’t catch on the way it did until quality open source products were made available for widespread usage.”

He points out that while the Warp client is open source, the server, the Warp Drive backend, and OZ (Warp’s agent orchestration layer) are proprietary. Also worth noting: OpenAI is named as the founding sponsor of Warp’s open source repository. 

“As such, my expectation is that software factory adoption will likely be fragmented across multiple vendors similarly to the early stages of CI/CD,” he says. 

The post Warp wants to make it easier to build your software factory appeared first on The New Stack.

Received — 14 July 2026 AI Infrastructure Archives - The New Stack

“We did not adapt and move quickly enough”: What IBM’s earnings miss says about enterprise AI spending

Dealing with Distributed Data When Training AI Models

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.

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