# Look for bottlenecks between AI answers and useful outcomes

Markdown copy of https://realalpha.app/s/look-for-bottlenecks-between-ai-answers-and-useful-outcomes/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Alex Atallah](https://realalpha.app/builders/alex-atallah/) · Alex Atallah is cofounder of OpenRouter and OpenSea. · Co-founded OpenRouter, a marketplace connecting apps to AI models and providers via one API; also co-founded OpenSea.
- Host: [Sabrina Halper](https://realalpha.app/people/sabrina-halper/) · Host, Sabrina Halper Show · Has worked as an investor at HOF Capital and founded SHS Media.
- Show: Sabrina Halper Show
- Episode: [Alex Atallah (OpenRouter, OpenSea): Selling to Stripe for $7B+ and the Unfolding AI Economy](https://realalpha.app/v/6hWBWprdowI/), Sep 29, 2026
- Moment: 42:10 to 45:57 of the episode (4 min), playing on https://realalpha.app/s/look-for-bottlenecks-between-ai-answers-and-useful-outcomes/; the episode from there: https://realalpha.app/v/6hWBWprdowI/?t=2530

## Quote

> “One way of looking for companies is to figure out **what the bottleneck is**”
> — [Alex Atallah](https://realalpha.app/builders/alex-atallah/) at [42:46](https://realalpha.app/v/6hWBWprdowI/?t=2566) · Sabrina Halper Show

## Takeaways

1. Look for bottlenecks between model capability and useful outcomes.
2. Explore robotics, persistent context and hard research problems.
3. Ask what newly feasible tasks people will pay for.

## About this segment

Alex Atallah suggests finding gaps between what models can do now and what they may do in a year, including robotics, memory and difficult research. Those gaps can point to new companies.

From “Alex Atallah (OpenRouter, OpenSea): Selling to Stripe for $7B+ and the Unfolding AI Economy”, Sep 29, 2026. [Watch the full episode](https://realalpha.app/v/6hWBWprdowI/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[42:14](https://realalpha.app/v/6hWBWprdowI/?t=2534) and and you know in what sector well I think the next three to four years are gonna be really insane a lot of things are gonna change and and it's it's It's really hard to make predictions right now because we're still seeing significant intelligent advancements every four months. One way of looking for companies is to figure out what the bottleneck is between the best possible answer and where you think answers from models are going to wind up in 12 months not just answers but actions and services and like any kind of you know any kind of like monetizable thing that a human might pay for or an agent might pay for so you know there's still a lot to be done in robotics like Like, we don't have a consumer robot that everybody has.

[43:38](https://realalpha.app/v/6hWBWprdowI/?t=2618) Seems crazy. Like, why is that? Like, there's a lot of questions you can ask there, and there's a lot of ways to go deep. There's also some promising companies, too. There's, like, context. Like, how can we, like, get... No. Models better and more continuous context to help answer their questions. When models get better at memory, what will be needed for a model that has really, really good, even perfect memory about everything that has happened in the past?

[44:19](https://realalpha.app/v/6hWBWprdowI/?t=2659) What kind of infrastructure will be needed? What kind of connections will be needed? What kind of collaboration will be needed between humans and agents? um you know i think you can also um you can also spend time thinking about like a lot of tasks will be very you know solved and deterministic and we'll have you know models that are like good at doing these specific things but many tasks will be like non-deterministic and all researchy and all very like you know yeah like figure out some incredibly valuable incredibly difficult problem that like would have taken you know 20 000 20 million dollars in inference to do um but let's try to do it in like a day like thinking about those problems and thinking about um what happens when it all becomes doable within a day or within you know a week um is really hard like i like a lot of people don't know they can just ask chat gpt

[45:24](https://realalpha.app/v/6hWBWprdowI/?t=2724) or, you know, most agents to read all the news sources they care about and send them a summary every two days. Like, you just have to ask the question sometimes. Yeah. So I think, like, being very curious is just a good general tactic and also thinking about, like, the little portion of tasks that get unlocked at the end of the value curve is another useful tactic.