This founder-turned-VC is betting against the AI hype

^{A week in AI is like a year in other industries. I hope these issues become your weekly source of AI information, inspiration, and ideas. If we haven’t met before, I’m Amanda Smith. I write about AI and the fascinating folks who are building in this brave new world.}

Here at AI Odyssey, we profile founders building on the bleeding edge of AI. But we rarely hear from the VCs funding this technological revolution. 

This week, we’re turning the spotlight on a founder-turned-investor whose focus is on the less glamorous but critical backbone of AI: The infrastructure powering it, rather than the latest application-layer trends.  

He shares what he’s seeing across the AI investment landscape, what’s overhyped, where the opportunities lie, and what makes a founder worth backing. 

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Taking DigitalOcean from startup to a New York Stock Exchange IPO taught Karl Alomar a thing or two about building a business. It also changed how he evaluates founders.

The journey wasn't always smooth. Beyond the financials, Alomar learned how much depends on a founder's ability to make difficult decisions, manage people, and keep moving when things don't go to plan.

Now investing at M13, he brings that operational experience to AI startups, with a particular interest in the infrastructure powering the technology.

He's watching how companies solve the compute bottleneck, secure autonomous agents, and build businesses that can survive the next model release.

From operator to investor

Running DigitalOcean shaped Alomar’s belief that the founder is the biggest determinant of a company's success.

"Operating is far more nuanced than a balance sheet. You have to understand the ramifications of every decision and know how to bring your team along on the journey."

When evaluating founders, he pays attention to how they hire, handle talent decisions, and respond to challenges.

One green flag is a founder who acts on advice. Alomar met Prepared founder Mike Chime in 2020 after speaking at Yale. The business wasn't investable at the time, but they stayed in touch.

"Every time we spoke, he had executed on every action or strategy we'd discussed." M13 became Prepared's first institutional investor. The company was later acquired by Axon.

And the red flag? Hubris. AI moves too quickly for founders who believe they have all the answers.

Competing with the giants

DigitalOcean entered a market dominated by AWS, which primarily served executive buyers. Developers were underserved, so DigitalOcean built around their needs, prioritizing simplicity, usability and transparent pricing.

That approach still applies to infrastructure startups competing with hyperscalers.

He said if you understand the giants in a category and where their blind spots or limitations are, you can build to solve those and corner a valuable piece of the market.

"A founder wins by going deep on one painful problem for a customer the giants treat as an afterthought."

Building a company, not a feature

In AI, a model update can quickly make a standalone product redundant. So how does Alomar distinguish a product from a company?

"Ask what happens in three years. A product founder gives you a feature roadmap. A company founder talks about economics, distribution, and who they'll need to hire to get there."

For example, Prepared began by helping schools respond to emergencies but expanded into a public safety platform connecting 911 callers and responders.

Alomar has learned not to impose his own vision on a founder's business. Early in his investing career, he'd hear a pitch and start imagining how he'd build the company himself. Now, he focuses on what the founder sees and can execute.

The AI investments to watch

Alomar’s standout investment for 2026 is Baselayer, where M13 recently led the Series A. As AI agents increasingly act on behalf of people and businesses, identity verification is becoming a bigger problem.

"Who sent the agent? What is it permitted to do? Can the business on the other end validate any of this?"

Alomar expects agent security and identity to become increasingly important in 2027, particularly as autonomous agents begin handling payments and transactions.

Another priority is compute efficiency. He believes the idea that we can simply build our way out of the compute shortage is overhyped. Demand for GPUs, memory and power continues to outpace supply, making better use of existing capacity essential.

Alomar expects GPU optimization, across hardware and software, to become a board-level priority in 2027.

Where the long-term value is  

Alomar said chat apps continue to attract investor attention because they reach consumers through familiar channels. AI assistants that operate inside messaging apps can encourage adoption without requiring users to learn a new platform. He points to Instinct's growth inside messaging apps as an example.

But he sees greater long-term value in the infrastructure underneath them.

"My personal preference is that foundational layer, which ultimately powers everything delivered through the app."

His thinking has also shifted towards the services model over traditional software. As coding agents become more powerful and software gets easier to build, early traction alone is no guarantee of a durable business. Products that simply wrap existing AI models are particularly vulnerable.

Instead, Alomar is looking for AI businesses that can handle an entire job rather than a single task, embedding themselves into enterprise operations and becoming harder to replace.

"The product has to behave like a complete service that becomes a dependency for the customer. That's where the power of AI is truly realized."

If one of these stories stuck with you, I’d love to hear which one.

Speak soon,
Amanda

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