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cover of episode Scott Wu - Building Cognition - [Invest Like the Best, EP.401]

Scott Wu - Building Cognition - [Invest Like the Best, EP.401]

2024/12/17
logo of podcast Invest Like the Best with Patrick O'Shaughnessy

Invest Like the Best with Patrick O'Shaughnessy

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Katie Ellenberg
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Patrick O'Shaughnessy
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Scott Wu
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Patrick O'Shaughnessy: 我对 AI 技术在软件工程领域的应用以及 Devin 的能力和局限性很感兴趣,并探讨了 AI 如何改变软件开发流程和工作方式。 我关注的是 AI 如何提高软件开发效率,以及 AI 是否会取代程序员。 我对 AI 代理以及 AI 如何改变软件开发流程和工作方式很感兴趣。 我对 AI 技术的未来发展和应用场景很感兴趣,尤其关注 AI 如何改变不同行业的工作方式。 Scott Wu: 我是 Cognition 公司的联合创始人兼 CEO,我们开发了一个名为 Devin 的 AI 软件工程师,它能够处理完整的软件开发流程,从修复 bug 到提交代码请求。 Devin 目前相当于一个初级软件工程师,能够处理一些简单的任务,但还无法胜任复杂的架构设计等工作。 Devin 的使用方式类似于团队协作,工程师可以将任务分配给 Devin,并异步地进行其他工作。 Devin 的实际应用场景超出了我们的预期,例如在代码迁移和平台现代化方面展现了显著的效率提升。 与 IDE 助手相比,Devin 采用了一种更全面的自动化方法,实现了异步工作流程,从而显著提高了工作效率。 我认为软件工程的核心始终是“告诉计算机你想让它做什么”,而技术的进步使得实现这一目标的方式不断演变。 在未来一到两年内,AI 将超越世界上最好的程序员,这将推动软件工程领域的民主化。 AI 将成为下一代人机交互界面,它将简化人机交互过程,让更多人能够参与软件开发。 在未来 5 到 10 年内,人们将能够使用自然语言来创建软件应用程序,而无需具备编程经验。 AI 代理将逐渐取代应用程序,成为人们与技术交互的主要方式。 AI 代理将改变各个行业的运作方式,例如法律、教育和软件工程领域。 Devin 的构建过程注重解决软件工程中的实际问题,并与现有的工具和技术进行集成。 Devin 的开发重点是解决软件工程中的实际问题,而不是单纯地追求模型的智力水平。 与其他技术变革不同,AI 的发展更具渐进性,因此需要持续关注模型无法自行完成的任务,并通过改进产品和用户体验来提升价值。 AI 代理的兴起将导致对大型语言模型的使用量大幅增加,这将推动模型朝着更适合代理应用的方向发展。 我们使用 Devin 来开发 Devin 本身,这体现了一种异步的工作流程,工程师可以将任务分配给 Devin,并同时处理其他工作。 在短期内,类似 Devin 的系统将能够帮助工程师处理大部分的例行任务,从而将更多的时间投入到创造性工作中。 随着软件开发效率的提高,软件的总量将持续增长,这将带来经济效益和生活质量的提升。 AI 将赋予人们更多权力,让他们能够专注于解决自己想解决的问题,并将其转化为现实。 人们往往高估了规模定律对 AI 模型的影响,而低估了技术创新对模型改进的贡献。 在商业方面,我从 Lunch Club 的创业经历中学习到,专注于基本功,并不断追求极致,能够取得更大的成功。 软件工程领域仍然处于早期阶段,存在着巨大的发展空间和机遇。 人们往往高估了 AI 技术的成熟度和普及速度,而低估了其在不同行业中的应用潜力。 Lunch Club 的创业经历让我认识到,解决更大的问题有时比解决更小的问题更容易。 在 Cognition 的创业过程中,产品发布阶段是最具挑战和压力,也是最难忘的经历。 与其他代码辅助工具相比,Devin 的优势在于其迭代式和自主式的工作流程,能够更好地处理复杂任务。 构建 AI 代理的难点在于模型能力、基础设施和软件工程的复杂性。 AI 的未来在于解放人类的创造力,让人们能够专注于创意和想象力,而不是繁琐的执行工作。 Cognition 团队成员之间的友谊和信任是公司成功的关键因素之一。 投资者应该更多地关注 Cognition 团队的协作方式和文化氛围。 AI 技术的广泛应用速度和不同行业中的采用率是不确定性因素。 在我的人生中,导师的指导和帮助对我影响最大。 Katie Ellenberg: 我负责 Geneva Capital Management 的投资运营和投资组合管理,致力于为投资团队提供最佳支持。 Geneva Capital Management 是一家独立投资顾问公司,管理着超过 60 亿美元的资产,专注于美国小型和中型成长型股票。 Ridgeline 团队的专业知识和对客户需求的深入了解打动了我们。 Ridgeline 提供了全面的解决方案,并建立了良好的客户关系。 Ridgeline 的单一数据源、AI 功能和客户门户是我们最喜欢的三个方面。 supporting_evidences Scott Wu: 'We got started actually just around one year ago, and we are focused on building AI to automate software engineering.' Katie Ellenberg: 'I am the head of investment operations and portfolio administration here at Geneva Capital.'

Deep Dive

Key Insights

What is Cognition's business and what does Devin do?

Cognition is an applied AI lab that has created Devin, the first fully autonomous software engineer. Devin can handle complete engineering workflows, from bug fixing to submitting pull requests, and functions at the level of a junior software engineer. It integrates into tools like Slack, GitHub, and works in the local developer environment.

How does the level of sophistication of Devin compare to human engineers?

Devin is currently at the level of a junior engineer. Initially, it was like a high school CS student, then an intern, and now it's comparable to an entry-level junior engineer. However, it can't handle complex tasks like re-architecting an entire codebase.

What is the pricing model for Devin and why is it different from other AI tools?

Devin is priced at $500 a month for engineering teams. This is based on usage, with a unit called ACUs (agent compute units) that measure the decisions and work Devin does. The pricing reflects the value of Devin's capabilities, which can make tasks 10x cheaper to complete, compared to a more general $20 per month model for text-based tasks.

What are the key differences between IDE assistants and Devin's approach?

IDE assistants provide text completion and simple code suggestions, improving productivity by 10-20%. Devin, however, functions as a full co-worker, handling tasks asynchronously and end-to-end, from debugging to testing and submitting pull requests. This can save 90% of the time on routine tasks.

How does Scott Wu describe the history of computer programming?

Scott describes programming's history as a growing abstraction layer that simplifies tasks and reduces the need for deep technical knowledge. From punch cards to assembly, BASIC, C, and modern languages like Python, each step has made programming more accessible, but the core remains about telling the computer what you want it to do.

What qualities make a 10x engineer in software development?

A 10x engineer deeply understands abstractions and can effectively solve problems by thinking through customer needs and designing efficient solutions. While they only spend about 10% of their time on creative problem-solving, this quality distinguishes them from average engineers.

What are the most surprising use cases for Devin that Scott has observed?

One of the most surprising use cases for Devin is its ability to handle large-scale migrations, modernizations, and version upgrades. These tasks are often tedious and time-consuming, but Devin can complete them much faster, saving engineers significant time and effort.

Why does Scott believe AI will surpass the best competitive programmer in 1-2 years?

Scott believes AI will surpass the best competitive programmer, like Gennady Korotkevich from Belarus, within 1-2 years. Current AI models are already competitive with top human programmers and are rapidly improving. This milestone will be a significant moment, similar to AlphaGo, demonstrating the potential of AI in programming.

How does Scott envision the future of software engineering with AI?

Scott predicts that in the future, AI will democratize software creation by allowing anyone to describe what they want to build in natural language. This will shift the focus from implementation to ideation, enabling rapid iteration and more personalized software solutions.

What distinguishes an agent-based system like Devin from traditional AI tools?

Agent-based systems like Devin are designed to make decisions and interact with the real world, unlike traditional AI tools that provide text completion or simple answers. Devin can test its own code, run commands, and navigate through a series of tasks, making it a powerful co-worker.

What challenges does Cognition face in building Devin?

Cognition faces challenges in adapting model capabilities to real-world software engineering tasks, integrating with various developer tools, and maintaining machine state for efficient parallel work. The team focuses on solving these infrastructure and decision-making problems to enhance Devin's effectiveness.

What is the kindest thing that anyone has done for Scott?

The kindest thing Scott has experienced is the mentorship and guidance he received, especially from a mentor who pushed him to excel in competitive programming and math. This mentor invested significant time and effort, providing critical feedback and support that shaped Scott's career and success.

Chapters
Cognition, founded a year ago, created Devin, the first AI software engineer. Devin handles complete engineering workflows and functions at the level of a junior engineer. The company's approach uses an agentic, asynchronous method, offering a 10x improvement over traditional IDE assistants.
  • Cognition built Devin, the first fully autonomous software engineer.
  • Devin handles entire software workflows, including bug fixing and pull requests.
  • Cognition's agentic approach to AI offers a 10x improvement in efficiency compared to traditional IDE assistants.

Shownotes Transcript

My guest today is Scott Wu). Scott is the co-founder and CEO of Cognition, which is an applied AI lab that has created the first AI software engineer, which they call Devin. In just a year since founding Cognition, Devin functions at the level of a junior software engineer, capable of handling complete engineering workflows from bug fixing to submitting pull requests. He is a former competitive programming champion and describes the field as simply “the art of telling the computer what you want it to do." Scott predicts AI will surpass the world's best competitive programmer within 1-2 years and sees this technology not as replacing programmers, but as democratizing software creation. We discuss the bottleneck in software development, the future of AI in various industries, and the challenges of leveling up Devin. Towards the end, you’ll also hear Scott do an insane card trick on me. You can find the video on our X and YouTube to grasp the madness fully. Please enjoy my conversation with Scott Wu.

For the full show notes, transcript, and links to mentioned content, check out the episode page here.)

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Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit** joincolossus.com/episodes**)**. **

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Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)).

Show Notes:

(00:00:00) Welcome to Invest Like the Best

(00:07:00) Discussion of Devin's current capabilities as a junior engineer 

(00:09:00) Early use cases and customer adoption 

(00:11:00) Comparison between IDE assistants and Devin's autonomous approach

(00:14:00) History of computer programming and its evolution 

(00:17:00) Scott's background in competitive programming 

(00:20:00) Future predictions for AI in software engineering 

(00:26:00) Explanation of AI agents and their significance 

(00:29:00) Technical details of how Devin is built 

(00:40:00) Impact on software engineering jobs and industry 

(00:47:00) Discussion of business model and pricing 

(00:52:00) Thoughts on AGI and its practical implications 

(00:59:00) Commentary on the competitive landscape 

(01:24:00) Future of AI adoption across industries 

(01:25:00) Card trick demonstration

(01:29:00) The Kindest Thing Anyone Has Done For Scott