This interview covers the current state and future of AI coding assistant Devin. Scott Wu, CEO of Cognition, believes AI won't replace programmers but will free them from tedious coding to focus on creativity. He predicts AI will beat the world's top programmer within 1-2 years. Key holdings mentioned: Devin (priced at $500/month, 10x more efficient than humans), GitHub Copilot (10-20% efficiency boost, a complementary tool), and Datadog (benefiting from AI trends, market cap ~$50-60 billion).
At a Glance Scott Wu, co-founder and CEO of Cognition, discussed the company's AI software engineer Devin on the Invest Like the Best podcast. Devin has already reached the level of a junior engineer, capable of handling the complete workflow from bug fixes to submitting pull requests. Wu predicts t
Scott Wu, co-founder and CEO of Cognition, former International Olympiad in Informatics (IOI) champion. This episode explores the current capabilities, technical architecture, and industry impact of his AI software engineer Devin. Core thesis: Scott Wu predicts that AI will defeat the world's best competitive programmer, Gennady Korotkevich, within 1-2 years, but AI will not replace programmers. Instead, it will shift software engineering from "survival mode" to "creation mode"—freeing humans from 90% of implementation work to focus on the 10% of creativity and problem definition.
Scott Wu believes that Devin's current capability level is equivalent to that of a junior software engineer, but its value lies not in replacing humans, but in changing workflows.
> "It's very much a junior engineer today... you're working asynchronously with your team of Devins while you're doing your own stuff."
Scott Wu distinguishes between IDE assistant tools (e.g., Copilot) and the Agent paradigm represented by Devin, arguing that the core difference lies in "synchronous vs. asynchronous" automation and "task-level vs. line-level" automation.
Scott Wu believes that Agents will become the primary consumption scenario for GPUs and language models — because behind each Agent task lie hundreds or even thousands of model calls, rather than a single query-response.
> "With Devin... for the tasks that Devin can do, it's more like a 10x rather than a 10%." (Meaning: for tasks Devin can handle, the efficiency improvement is 10x, not 10%.)
Scott Wu defines the essence of programming as "telling the computer what you want it to do," arguing that AI represents the next-generation human-computer interface, which will fundamentally transform the cost structure of software development.
Scott Wu cites a metaphor from his co-founder Walden: We have been playing Minecraft in "survival mode" (constrained by resources), and now we are about to enter "creative mode" (focusing solely on ideas and imagination).
> "All of programming... it really is just about telling your computer what to do."
Scott Wu argues that the software industry has long been constrained by supply (the number of engineers), not demand. When AI significantly lowers the cost of software creation, business models that rely on "switching costs" will be disrupted, while network effects and data personalization will become more important.
Scott Wu points out that enterprise tools (such as Slack, GitHub, Datadog, and Atlassian) will see "Agent customers" — these tools will serve not only humans but also AI Agents, and need to be prepared accordingly.
Scott Wu believes the current AI software engineering space is still in a "green field" stage, but builders need to focus on "engineering details beyond model IQ."
> "Software engineering is just very messy... there's this entire thread between of how do you take that IQ and turn that into something that is actually meaningfully useful."
| Position | Guest Sentiment | Key Data |
|---|---|---|
| Devin (Cognition) | Bullish (core product) | Priced at $500/month; hourly cost approx. $8–12; client research shows 1 hour of Devin = 8–12 hours of manual labor |
| GitHub Copilot | Neutral (complementary tool) | Efficiency improvement of approx. 10–20% |
| OpenAI (O1 Pro) | Neutral (different positioning) | Suitable for single-file code generation and Q&A, not suitable for multi-step iterative workflows |
| Datadog | Bullish (benefiting from Agent trend) | Revenue in the billions of dollars, market cap approx. $50–60 billion |
| PagerDuty | Bullish (benefiting from AI incident response) | No specific data provided |
| Slack / GitHub / Atlassian | Bullish (will see Agent clients) | No specific data provided |
1. AI will defeat the world's best competitive programmer within 1-2 years (Scott Wu) — Gennady Korotkevich is currently the world's top programmer, and models from Google and OpenAI are already approaching the level of elite competitors; reaching the top is only a matter of time. This will be an "AlphaGo moment."
2. Software engineering has long been constrained by supply, not demand (Scott Wu) — Every engineering team has 20 projects they want to pursue but can only choose 3. When AI lowers the cost of creation, we will see Jevons paradox: the easier software is to build, the more people will build it.
3. "Minecraft survival mode vs. creative mode" (Walden, co-founder of Cognition) — In the past, humans were limited by resources (time, skills, teams) and could only realize a few ideas; AI will usher humans into "creative mode," where they can focus solely on imagination and creativity.
4. Business models reliant on switching costs will face disruption (Scott Wu) — When AI can easily handle migration and reimplementation, competition will revert to "better products" themselves. Network effects and data personalization will become more important.
5. Agents will become the primary consumption scenario for GPUs (Scott Wu) — Each agent task involves hundreds of model calls, not a single query-response. Foundation model companies will need to optimize for agents (long context, multi-turn tracking, etc.).
6. "The scaling law is somewhat mythological" (Scott Wu) — Progress comes not only from piling on compute but also from the introduction of new technologies (post-training optimization, reinforcement learning, etc.). It cannot be simplistically assumed that "just adding compute will make things better."
7. Enterprise tools will see "agent customers" (Scott Wu) — Platforms like Slack, GitHub, and Datadog will serve not only humans but also AI agents. These companies need to optimize their product experience for agents.
8. Sometimes solving a bigger problem is actually easier (Scott Wu) — Of the 20 people on the Cognition team, 14 were former founders. A truly grand vision can attract top talent, making "difficult" things "easy."