Why cramming AI agents into old apps is so hard

Welcome to Runtime! Today: Writer CEO May Habib explains her company's generative AI strategy, Ampere and Arm get a little cozier, and the latest enterprise moves.

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Model citizen

Over the last year or so, as businesses grew weary of magical thinking around generative AI and more interested in practical applications, their attention shifted away from the foundation model builders like OpenAI and Anthropic and toward development tool vendors that can help them build, deploy, and maintain production applications. Writer has raised $326 million on the premise that it can do both.

Writer likes to think of itself as a "full stack" generative AI company, according to May Habib, co-founder and CEO. It has built five different large-language models — with more on the way — and also developed a series of APIs and software tools that work with those models to help companies build apps, which has proven trickier than expected.

"Agentic AI is like the straw that broke the enterprise's back when it comes to generative AI, and people are understanding they can't be doing all of this themselves. They need support," Habib said in a recent interview. Excerpts from that conversation follow below.

On Writer's decision to build its own models:

Habib: The story of Writer is the story of the transformer. We started the company to build models, and with every big step-function change, we've either led it or been like four weeks behind. If DeepSeek proved anything to the world, it's that you don't need billions of dollars to do this, and necessity is the mother of all invention.

I think enterprises quickly learned that building, fine tuning, and maintaining LLMs is not their core business. And for five years now, we have kept up and led many of these innovations, with the next one up being self-evolving models, which we believe is the path to super intelligence. These are models that are able to update their training data in real time so they can learn from every wrong interaction, which is going to be very critical in agentic AI.

On AGI:

Habib: I think it actually doesn't matter. Super intelligence is the term we use to really give shape to this idea that we're going to have, very soon, technology inside of companies that knows more than any one person. But it is an AI system, and it is controllable, and it has to be observable for it to actually work at scale. AGI doesn't mean God. It means super, super good.

On the slow progress of agentic AI:

Habib: Right now, the agents that we're seeing scale fall into really two categories. One is things people couldn't do before and weren't doing before.

When you are creating a process that automates 20,000 different websites for a major hotel chain by connecting to the source of SEO data directly, with no human intervention, and then actually making all those updates right programmatically and only nudging the user when you know one of those updates might break something, that is really, really cool. You just simply would not be doing that with humans. That actually is what makes those categories of use cases much easier. There is no change management, because there was nobody doing the thing. And from a data perspective, you're really able to use systems that are there. You're not inventing what good looks like, right?

Then there's a category of use cases that is much harder, and this is in spaces where you already have quite a bit of automation. So, claim adjudication, medical summarization, customer support … where you know five percent or 10% or 15% of the cases require human intervention, and that's taking up 80%-90% of the work.

The reason those are hard is because there are no standard operating procedures that are written down, and a lot of the know-how on how you handle those exceptions sits in people's heads. What makes the second category of use cases tougher is that it's a very manual process pulling out that data, and there's a lot of coordination that needs to happen. Very few enterprises are in the kind of crisis mode required to do something like that very quickly.

I have seen the second category of agentic use cases really, really take off when there has been a crisis of some sort, where a supply chain issue totally broke the current support model, and they needed a load of agents, or they needed 24/7 support for some reason and couldn't manage it with humans.

Read the full interview on Runtime.

Chipping away

Given that all of the Big Three cloud providers have now shipped their own custom Arm server processors, Ampere's path forward as an independent chip designer was starting to look a little tricky. Softbank erased a lot of that uncertainty Thursday, announcing plans to acquire the company for $6.5 billion.

That's a little less than Ampere was valued at four years ago when Softbank made a small investment, according to Bloomberg. Ampere's server processors were well received and used by cloud customers of Microsoft, Google Cloud, and Oracle, but it's never been clear how widely they were used by cloud infrastructure customers with a lot of options.

Ampere is expected to work closely with Arm itself as fellow members of the Softbank family. Given the long-term uncertainty surrounding Intel, there could be lots of opportunity to displace x86 chips with Arm chips over the course of the rest of the decade, and Ampere should remain part of that conversation under Softbank.

Enterprise moves

Rob Kaloustian is the new chief customer officer of Hyland, joining the content management company from Cloudera.

Amy Coleman is the new chief people officer at Microsoft, replacing Kathleen Hogan, who will remain at the company in a new strategic role.

The Runtime roundup

Microsoft turned down the option to exercise a $12 billion contract with CoreWeave, according to Semafor.

The server market nearly doubled in the fourth quarter of 2024, according to IDC, with shipments of GPU servers growing by 192.6%.

Nvidia acquired Gretel, which specializes in generating synthetic data — a key part of Writer's strategy — for an undisclosed amount in the "nine figures" range, according to Wired.

Thanks for reading — see you Saturday!