The $1B Al company training ChatGPT, Claude & Gemini on the path to responsible AGI | Edwin Chen

Surge AI: A Billion-Dollar Success Story

Bootstrapped Growth and Unique Philosophy

• Surge achieved a billion in revenue within four years with a lean team of 60 to 70 people, completely bootstrapped without any VC funding.

• The founder expresses disdain for the traditional Silicon Valley approach, believing that many employees in large tech companies are unnecessary distractions.

• Surge aims to build an elite team focused on quality over quantity, emphasizing the importance of understanding what "quality" means in data.

Insights on AI Model Development

• There is a critical discussion about model behavior; should models optimize for user productivity or provide endless suggestions for improvement?

• Concerns are raised about current AI labs focusing on trivial advancements rather than meaningful progress that could benefit humanity, such as curing diseases or solving poverty.

Teaching Models and Ethical Considerations

• Edwin Chen highlights the risk of training AI models to prioritize sensationalism (e.g., "tabloid" content) over truth and valuable insights.

• The conversation emphasizes the need for responsible AI development that genuinely contributes to human advancement rather than merely chasing engagement metrics.

Podcast Promotion and Guest Introduction

• The host encourages listeners to subscribe to the podcast and newsletter, offering various products as incentives for annual subscribers.

Sponsor Messages

• Vanta is introduced as a solution for compliance management, helping companies streamline their processes with automation.

• WorkOS is presented as a platform simplifying enterprise software integration challenges faced by startups.

Discussion with Edwin Chen

• The host welcomes Edwin Chen, acknowledging his unprecedented achievement in scaling Surge AI while maintaining a small workforce.

AI's Impact on Business Efficiency and Company Structure

Leveraging AI for Growth

• The speaker highlights that their company achieved over a billion in revenue with fewer than 100 employees, suggesting a future trend where companies may reach ratios of $100 million per employee due to advancements in AI.

• Reflecting on past experiences at large tech firms, the speaker believes that reducing workforce size can enhance speed and efficiency, leading to the creation of a more elite team structure.

Changing Perspectives on Company Size

• There is a growing realization that success does not require large organizations; instead, efficiencies gained from AI will foster innovative company structures.

• Fewer employees lead to reduced capital needs, allowing founders who excel in technology rather than fundraising to emerge. This shift could result in products driven by passion rather than investor demands.

A Contrarian Approach to Building Companies

• The speaker emphasizes their intentional decision to avoid traditional Silicon Valley practices like constant self-promotion on social media platforms such as LinkedIn and Twitter.

• They argue that focusing on product development rather than public relations has allowed them to build a superior product through genuine customer feedback and mission alignment.

Importance of Quality Data

• Surge specializes in training AI models using human data, emphasizing the significance of high-quality data over mere quantity.

• The speaker explains that many fail to grasp what constitutes quality data; it requires deep understanding beyond superficial metrics.

Defining High Quality Data

• Using an analogy about poetry, the speaker illustrates that true quality involves depth, uniqueness, and emotional resonance rather than just meeting basic criteria.

• They advocate for a nuanced approach when assessing quality—seeking insights into language and imagery rather than simply checking off boxes.

Understanding Quality Measurement in AI Development

The Complexity of Measuring Quality

• Measuring quality is subjective and complex, requiring advanced technology to gather thousands of signals from workers and projects.

• Signals include background expertise and performance metrics, which inform assessments of worker suitability for specific tasks.

• There is a deep exploration into what constitutes quality within various verticals related to data sales.

Mechanisms for Evaluating Performance

• The evaluation process involves gathering extensive data on user interactions, including keystrokes and response times.

• Similar to Google Search's approach, the goal is twofold: eliminate low-quality content while identifying high-quality outputs.

• Distinguishing between mediocre and exceptional work requires nuanced signals that go beyond basic compliance with instructions.

Machine Learning Applications in Quality Assessment

• The assessment process resembles a complicated machine learning problem where models are trained based on output quality.

• This methodology allows for continuous improvement in model performance by leveraging feedback from various tasks.

Factors Influencing Model Performance

• Claude has outperformed other models in coding and writing due to several factors, primarily the quality of training data used.

• Data selection involves critical decisions about the type of human-generated content included in training datasets.

Balancing Trade-offs in AI Training

• Companies face trade-offs between optimizing for academic benchmarks versus real-world task performance.

• Decisions regarding synthetic data inclusion and focus areas (e.g., front-end vs. back-end coding priorities) significantly impact model effectiveness.

• Post-training refinement requires an artistic touch; it’s not purely scientific but involves taste and sophistication in model tuning.

Understanding the Role of Taste in AI Design

The Influence of Personal Taste on Model Design

• Different notions of visual design can affect how models are trained, as personal taste influences choices like minimalism versus 3D animations.

• The objective function that guides model optimization is shaped by the designer's preferences, impacting data selection and training outcomes.

• High-quality data significantly contributes to growth in AI applications, exemplified by companies like Enthropic thriving due to superior data utilization.

Human Judgment vs. Objective Metrics

• Despite AI's computational nature, human judgment remains crucial for success; good poetry cannot be reduced to mere checklists.

• Frontier labs with a refined sense of taste consider subtle qualities beyond fixed criteria, leading to better outputs compared to those relying solely on benchmarks.

Trusting Benchmarks in AI Development

• There is skepticism about the reliability of benchmarks; many contain inaccuracies that researchers may overlook.

• Benchmarks often present well-defined answers that do not reflect the complexity and ambiguity found in real-world scenarios.

The Marketing Aspect of Benchmark Performance

• Achieving high benchmark scores can serve as a marketing tool for new models, but this may involve optimizing for specific tasks rather than real-world applicability.

• Some labs manipulate evaluation methods or system prompts to artificially enhance benchmark performance.

Measuring Progress Towards AGI

• To assess model progress towards AGI, human evaluations are conducted where experts interact with models across various topics.

• Evaluators focus deeply on accuracy and instruction adherence, providing insights that casual users might miss during interactions with AI systems.

AGI Development and Human Involvement

The Role of Humans in AI Evaluation

• The speaker critiques the superficial evaluation of AI responses, noting that many people simply choose flashy answers rather than deeply analyzing them.

• There is a concern about whether humans will eventually become obsolete in AI development, but the speaker believes this won't happen until Artificial General Intelligence (AGI) is achieved.

Timelines for AGI

• The speaker expresses skepticism about rapid advancements towards AGI, suggesting it may take decades rather than just a few years.

• They highlight the significant challenges in improving AI performance from 80% to higher levels, indicating that while some jobs may be automated soon, true AGI remains far off.

Concerns About Current AI Development Trends

• The speaker warns that current efforts in AI are misdirected; instead of solving major global issues like cancer or poverty, they focus on optimizing for engagement and superficial metrics.

• They criticize industry practices such as using leaderboards like LM Arena, where users vote based on flashy presentations rather than factual accuracy.

Negative Incentives Affecting AI Quality

• The emphasis on attention-grabbing features leads to models that can hallucinate information yet still appear impressive due to their presentation style.

• Researchers feel pressured to improve leaderboard rankings at the expense of model accuracy and reliability due to corporate incentives tied to sales performance.

Engagement Optimization Issues

• Drawing parallels with social media's negative outcomes from engagement optimization, the speaker fears similar trends are emerging within AI development.

• They note how models often reinforce user biases by providing overly flattering feedback or engaging content that can lead users down misleading paths.

Future Directions and Ethical Considerations

• The discussion concludes with concerns over how current benchmarks might hinder genuine progress toward AGI by prioritizing incorrect objectives.

• Despite these challenges, the speaker acknowledges Anthropics as a company taking a more principled approach in their model development.

Insights on AI Development and Silicon Valley Dynamics

The Impact of Product Development on Progress

• Discussion on how certain products may hinder or help progress in AI development, emphasizing the importance of evaluating what is being built.

• Reflection on the implications of companies developing specific AI models, hinting at a deeper understanding of their goals and future directions.

Ethical Considerations in AI Growth

• Acknowledgment that while some developments are fun and revenue-generating, they may distract from long-term objectives.

• Analogy comparing selling tabloids for funding to potentially harmful practices in tech; highlights the importance of caring about the path taken in business.

Critique of Silicon Valley's Approach

• Criticism of common Silicon Valley strategies like rapid pivoting and growth chasing, suggesting these lead to superficial success rather than meaningful innovation.

• Advice against following conventional wisdom such as frequent pivots or hiring based solely on prestige; advocates for building unique products driven by personal insights.

The Value of Consistency and Mission Focus

• Emphasis on maintaining a consistent mission rather than chasing trends; warns against companies lacking a clear purpose.

• Argument that true startup success comes from taking significant risks with innovative ideas instead of merely seeking quick profits through market fads.

Future Aspirations in Technology Development

• Encouragement to focus on ambitious projects that matter, resisting distractions from fleeting opportunities or popular trends.

• Mention of ongoing research into identifying successful generational companies, highlighting ambition as a key trait among founders who make impactful choices.

KOD: The All-in-One Workspace Solution

Introduction to KOD

• KOD offers a collaborative all-in-one workspace that consolidates project trackers, OKRs, documents, and spreadsheets into one tab.

• Over 50,000 teams utilize KOD daily for enhanced alignment and focus, particularly beneficial for startup teams aiming to improve agility.

Special Offer

• Users can try KOD for free with a six-month trial of the team plan by visiting kod.io/lenny.

The Future of AI: Learning Models and AGI

Richard Sutton's Perspective on Learning Models

• Discussion references Richard Sutton's views on learning models (LM), suggesting they may reach a plateau in development.

• The speaker aligns with the belief that new breakthroughs will be necessary to achieve Artificial General Intelligence (AGI).

Mimicking Human Learning

• Emphasizes the need for AI models to replicate diverse human learning methods rather than relying solely on existing LM frameworks.

• Advocates for algorithms and data structures that allow models to learn similarly to humans across various contexts.

Reinforcement Learning Explained

Understanding Reinforcement Learning

• Reinforcement learning involves training models through reward systems based on their performance in simulated environments.

Simulation Environments

• An R environment simulates real-world scenarios where AI must navigate complex tasks, akin to video game mechanics with interactive elements.

Challenges in Real-world Applications

• Highlights how current models excel in isolated benchmarks but struggle in messy real-world situations due to their lack of adaptability over longer time horizons.

Practical Applications of Reinforcement Learning

Designing Tasks for AI Models

• Discusses creating challenging tasks within R environments that require models to interact with various tools and data sources effectively.

Objective Functions and Rewards

• Explains how objective functions guide model behavior by defining success criteria through rewards based on task completion or problem-solving.

Understanding the Evolution of Model Learning

The Next Phase of Model Intelligence

• The discussion highlights a new phase in model development, focusing on reinforcement learning (RL) environments tailored for specific economically valuable tasks.

• Previous methods like Supervised Fine Tuning (SFT) and Reinforcement Learning from Human Feedback (RHF) are not obsolete but rather complementary to this new approach.

• Experts, such as financial analysts, are now designing RL environments instead of merely creating rubrics for models to follow.

Designing Effective Learning Environments

• Financial analysts may create tools and spreadsheets that models need to interact with, enhancing their ability to perform calculations and access necessary data.

• This method mirrors human learning processes where trial and error lead to understanding; models learn by attempting various approaches until they find the correct solution.

Importance of Trajectories in Learning

• Trajectories refer to the paths taken by models during problem-solving; even if a model reaches the correct answer, its journey can involve numerous failed attempts.

• Understanding these trajectories is crucial because they provide insights into how efficiently or inefficiently a model arrives at an answer.

• Ignoring intermediate steps can result in missing valuable information about the model's learning process and potential improvements.

Steps in Advancing Model Training

• The evolution of model training has progressed from SFT to RHF, with each step contributing significantly to improving model performance.

• SFT is likened to mimicking a master’s actions, while RHF involves feedback mechanisms similar to grading essays based on quality.

Evaluations and Their Role

• Evaluations serve dual purposes: training models through feedback on performance and measuring progress across different checkpoints before public release.

• The ongoing adaptation within the industry reflects a continuous need for innovative solutions that cater to diverse human learning styles.

Becoming a Great Writer

The Path to Writing Mastery

• Becoming a great writer involves more than just memorizing grammar rules; it requires reading great literature, practicing writing, and receiving feedback from readers and teachers.

• Learning to write well is an endless cycle of practice and reflection, with various methods contributing to the development of one's writing skills.

• The process of learning parallels how neural networks operate, suggesting that enhancing AI may involve mimicking human learning processes.

Research in AI Development

• The speaker highlights the importance of having a dedicated research team within their company, which is relatively rare in the industry.

• Their research team consists of two types: forward-deployed researchers who collaborate with customers on model understanding and internal researchers focused on improving benchmarks and leaderboards.

Collaborative Research Approach

• Forward-deployed researchers work closely with clients to identify areas for improvement in their models, designing datasets and evaluation methods tailored to client goals.

• Internal researchers aim to create better benchmarks while also training models to determine what data performs best for enhancing quality.

Commitment to Research Over Revenue

• The speaker expresses a preference for being seen as a research lab rather than just a startup focused on revenue generation, emphasizing the drive for advancing AI knowledge.

• They seek individuals passionate about data analysis who can engage deeply with datasets and think critically about model behaviors beyond mere algorithms.

Future Trends in AI Models

• In the coming years, AI models are expected to become increasingly differentiated based on the unique personalities and objectives of different labs developing them.

• Contrary to previous beliefs that all AI models would become commoditized, there is now an understanding that distinct characteristics will emerge among them.

The Impact of AI on Productivity and Model Behavior

Understanding Model Behavior in AI

• The speaker reflects on the importance of company values in shaping AI models, suggesting that these values will influence how models operate.

• A personal anecdote illustrates a dilemma: spending 30 minutes refining an email with AI assistance, questioning whether this time investment was worthwhile.

• The discussion raises a critical question about model behavior: should an AI optimize for productivity by encouraging users to move on quickly or allow for extensive iterations?

• The speaker compares different companies' approaches to building search engines, emphasizing that each company's principles will lead to distinct model behaviors.

• An example is given regarding Grock's unique personality and approach, indicating a trend towards differentiation among AI models.

Underhyped and Overhyped Aspects of AI

• The conversation shifts to what aspects of AI are underhyped versus overhyped.

• Built-in products within chatbots are identified as underappreciated; the potential for mini-applications within chat interfaces is highlighted as an exciting development.

• Conversely, "vibe coding" is deemed overhyped due to concerns about long-term maintainability issues if developers rely too heavily on it without proper oversight.

Future of Product Development with AI

• A reference is made to discussions with product leaders from Anthropic and OpenAI about the future role of product teams in light of advanced AI capabilities.

• There’s speculation that future advancements may enable users to simply describe their needs, allowing AI to autonomously create products based on those specifications.

Personal Journey Leading to Surge

• The speaker shares their background, noting a fascination with math and language which led them to MIT for studies in math and computer science.

• Their experience at major tech companies revealed challenges in obtaining quality data necessary for training effective models, driving their interest in creating better solutions.

• They express frustration over the focus on simple tasks like image labeling instead of leveraging human intelligence for more complex problems, motivating their work at Surge.

Exploring the Future of AI and Its Impact on Humanity

The Genesis of Serge

• The speaker discusses their background in mathematics, computer science, and linguistics, which influenced their mission to build use cases for advancing AI after the launch of GPT-3.

• They express a scientific mindset, originally aspiring to be a professor focused on understanding communication and language.

Passion for Research and Analysis

• The speaker enjoys deep diving into new AI models, conducting evaluations, and analyzing improvements or regressions in performance.

• They admit to struggling with typical CEO responsibilities but thrive on hands-on data analysis and collaboration with research teams.

Vision for AI's Role in Humanity

• The speaker emphasizes Serge's unique position as a research-oriented company that prioritizes curiosity over short-term metrics.

• Their goal is to shape AI development positively for humanity by leveraging their distinct perspectives on data quality and measurement.

Influence on the AI Ecosystem

• Acknowledges the significant influence they have within the broader ecosystem of AI development beyond just major companies like OpenAI.

• Highlights an opportunity to guide discussions about how humanity can engage with evolving AI technologies.

Philosophical Underpinnings of Their Work

• Discusses the deeper mission behind training and evaluating AI: helping clients define their ideal objectives for model performance.

• Compares defining success in models to parenting—it's complex and involves more than just achieving high scores; it’s about holistic growth.

Measuring Success Beyond Metrics

• Questions whether current systems are genuinely beneficial or merely optimize for superficial metrics that may lead to negative outcomes.

• Emphasizes the importance of building systems that advance humanity rather than making people lazier or less engaged.

Understanding the Complexities of AI Development

The Challenge of Measuring Progress in AI

• It is difficult to define and measure what constitutes genuine advancement for humanity, as opposed to easily quantifiable metrics like clicks and likes.

• The focus should be on developing complex objective functions that reflect true progress rather than relying on simplistic proxies that may not capture the full picture.

• There is a need for metrics that assess whether AI enriches human life, promoting curiosity and creativity instead of laziness.

Insights from Building Surge

• The speaker reflects on their initial misconceptions about entrepreneurship, believing it required constant fundraising and marketing rather than focusing on research and development.

• They emphasize the joy found in hands-on data analysis and applied research, which contrasts with their earlier fears of becoming detached from meaningful work.

• A successful company can be built by creating exceptional products without succumbing to external pressures like hype or fundraising.

Philosophical Perspectives on Data Labeling

• The speaker critiques the oversimplification of data labeling tasks, likening them more to nurturing a child than merely categorizing images.

• They argue that teaching values and creativity is essential in training AI systems, highlighting the depth involved in this process.

• This perspective frames AI development as a significant responsibility towards shaping future generations.

Recommended Reading for Founders

• The speaker recommends "Story of Your Life" by Ted Chiang, emphasizing its exploration of language through a linguist's experience with aliens.

• "Myth of Sisyphus" by Albert Camus is noted for its inspiring final chapter, though its appeal remains hard to articulate fully.

• "Letobo Dearo" by Douglas Hofstadter is praised for its examination of translation's complexities, paralleling themes relevant to data quality in machine learning systems.

Favorite Movies and TV Shows

Discussion on Media Preferences

• The speaker expresses a desire to help translate alien languages, indicating a strong interest in science fiction.

• They mention enjoying the TV show "Travelers," which involves time travelers from the future trying to prevent an apocalypse.

• The speaker also shares their love for the movie "Contact," highlighting a recurring theme of scientists deciphering alien communication.

Recent Discoveries and Experiences

Insights on New Technologies

• The speaker describes their experience with Whimo, a driverless car service in San Francisco, calling it "magical" and exceeding expectations.

• They note that driverless cars are common in SF, emphasizing how they are integrated into everyday life during events.

Life Philosophy and Company Building

Founder's Mindset

• The speaker discusses the principle that founders should build companies uniquely suited to their experiences and interests.

• They advise following personal interests as a way to acquire unique experiences necessary for impactful creation.

Values in Decision Making

• The speaker reflects on how companies embody their CEOs, suggesting that personal values should guide decision-making rather than just metrics or appearances.

• They emphasize asking oneself about personal values when making significant decisions within a company.

Cultural References: Soda vs. Pop

Fun Cultural Map Project

• The conversation shifts to a project where the speaker created a map showing regional preferences for the terms "pop" versus "soda."

• The speaker identifies as someone who says "soda," humorously noting they might look at someone funny if they say "pop."

Engagement with Audience and Future Plans

Online Presence and Hiring Needs

• The speaker mentions starting to write again on their blog at surgehq.ai/blog after taking a break from blogging.

• They express openness to hiring individuals passionate about data, math, language, and computer science.

Community Interaction Requests

• The speaker invites listeners to suggest blog topics they would like covered and shares interest in real-world AI failures as discussion points.

Thank You and Closing Remarks

Appreciation for Audience Engagement

• Edwin expresses gratitude for the audience's presence, highlighting the importance of their engagement.

• Acknowledges the listeners' support and encourages them to subscribe to the podcast on various platforms like Apple Podcasts and Spotify.

• Emphasizes the value of ratings and reviews, noting that they significantly help other potential listeners discover the podcast.

• Invites listeners to explore past episodes or learn more about the show, reinforcing community involvement.

• Concludes with a warm farewell, thanking everyone once again for their participation.