AI Disruptors: How the Next Generation of Business is Being Built

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By Dinesh Murthy

Updated: May 29, 2026 8 min read

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Getting your hands on a capable AI model is the easy part now. Every team can reach the same frontier models through an API, so a strong model is not what sets a product apart. What separates a working product from a demo is everything around the model. You have to measure whether the agent is actually doing its job, then keep grinding on reliability until it stops making expensive mistakes in front of real users.

I moderated a panel on exactly that at DigitalOcean’s Deploy 2026 conference in San Francisco, a forty-minute conversation with four founders on what they’ve learned shipping AI products that people depend on:

Angela Hoover , co-founder and CEO of Andi AI , an ad-free consumer search engine that pairs generative AI with live web data to give people direct answers instead of a page of ad-heavy links.

Alex Mashrabov , co-founder and CEO of Higgsfield AI , a platform that lets creators and agencies produce cinematic video without any physical production.

Hovsep Seraydarian , co-founder and CTO of LawVo , a Canadian legal platform that pairs hundreds of AI agents trained in specific legal areas with human lawyers who verify their accuracy.

Peter Elias , founder of Probably , a data analysis agent that lets non-technical people query their data in plain English and runs calculations on a local engine instead of an LLM so it can decline to answer when the data does not support a clear result.

The discussion got into what each founder underestimated once their agents had to run at scale, how they choose models from a field that keeps growing, what “agentic” actually means in production, and where a real moat comes from when everyone builds on the same foundation.

Watch the full session from Deploy 2026 :

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Making agents work in production

When the founders were asked what they underestimated once their agents had to run in production, none of them pointed to the model.

You need creative DNA

Higgsfield spent a year on R&D without traction. What finally moved the product was bringing on people who understood how creative work actually happens, then putting them next to the engineers every day.

“We started to see success when we got non-technical people on the team, like ex-creative directors, who now work daily with engineers to wrap this powerful technology and make it accessible for creatives.”

Alex Mashrabov, Higgsfield AI

In high-stakes domains, AI plus humans beats AI alone

LawVo assumed its agents could handle legal guidance with little human involvement. That did not survive contact with real users.

“We need human lawyers to verify the data and test these agents every day.”

Hovsep Seraydarian, LawVo

When asked whether that human role shrinks as the agents get smarter, Hovsep said the opposite is happening. The team watches what its lawyers do and folds that judgment back into the agents one step at a time, which means leaning on people more, not less.

Measurement infrastructure is unavoidable

Peter’s point was that one of the first problems you have to solve when you build an agent is the infrastructure that tells you whether the thing works at all. Probably went through several rounds of this until it built an analytics system that watches everything the agent does, and the product now evaluates its own behavior.

“As long as we record everything the AI is doing, this particular AI can now actually aid us in improving its own performance.”

Peter Elias, Probably

Angela made the same point from the oversight side.

“We’re still early on in implementing agents at Andi. We’ve noticed that when you let them run wild, they’ll do anything. You really do have to make sure that they get high quality, accurate, grounded data. We keep an eye on the agents that we have running; we haven’t let them be fully autonomous.”

Angela Hoover, Andi AI

Model selection is a four-variable problem

The number of available models has exploded over the past year, with frontier releases from Anthropic and OpenAI followed within weeks by open-source alternatives. Capable models are everywhere now. It’s challenging to choose among dozens of them when each carries a different mix of cost and capability.

Peter broke the decision into four variables he is always trading against each other: cost, latency, intelligence, and capacity. Smaller models tend to be faster and cheaper but give up intelligence, and an agent that fires many parallel calls runs into capacity limits fast.

“You want to get to the dumbest model you can get to before you actually go below the product performance that you need.”

Peter Elias, Probably

He also warned that users have less patience than founders expect.

“People will start to get impatient. We found that users were more latency-sensitive than we wanted them to be.”

Peter Elias, Probably

Alex runs evaluations every week at Higgsfield because the proprietary data about how users take action has to stay current as models change. He has also moved away from fine-tuning small models toward prompting larger ones, which he finds faster with fewer hallucinations.

“The rules of machine learning do not change. Understand the customer, understand the business goal.”

Alex Mashrabov, Higgsfield AI

Hovsep’s rule for any new startup is to start on a frontier model but architect for independence, so only a small slice of the system depends on the LLM and the rest lives in your own application and orchestration. Angela took the lean path from day one, relying on open-source models wherever they were good enough at a lower cost.

‘Agentic’ doesn’t mean autonomous

None of these founders treat “agentic” as a synonym for autonomous. I asked what happens as agents move from co-pilots toward systems that act on their own, and what guardrails that calls for.

Hovsep described a legal field run by dinosaurs, where regulation moves slowly and full autonomy is simply not on the table.

“Regulations won’t allow you to go fully autonomous. You literally get shut down if you do that in this space.”

Hovsep Seraydarian, LawVo

What makes that constraint interesting is that LawVo’s agents already beat human lawyers on accuracy.

“We have 92% accuracy on our…