EP 52: Jason Warner & Jacob Effron (Redpoint Ventures) - Former GitHub CTO Explains AI for Dummies

In the 52nd episode of the Logan Bartlett Show, host Logan Bartlett discusses the rapid advancements in artificial intelligence, particularly generative AI and large language models, with guests Jason Warner and Jacob Efron, both from Redpoint Ventures. They explore the growing influence of AI technologies like OpenAI's ChatGPT and Microsoft's integration of AI into Office products, highlighting the transformative potential across industries. The conversation touches on the ethical and regulatory challenges posed by AI, the distinction between open and closed-source models, and the societal impacts of AI-driven automation. Warner and Efron emphasize the importance of businesses understanding and adapting to AI's potential to remain competitive.

• The podcast discusses the recent advancements in AI, particularly focusing on generative AI and large language models.
• OpenAI's ChatGPT is highlighted as a significant development, with its integration into Microsoft's Office products like Outlook, Word, and PowerPoint.
• The conversation includes key figures from Redpoint Ventures, Jason Warner and Jacob Efron, who have been working extensively on AI projects.

"Now ChatGPT is going to power elements of Bing Search. Google last week rushed to make an announcement around Bard, which is their ChatGPT equivalent."

• This quote highlights the competitive landscape in AI, with major tech companies like Microsoft and Google racing to integrate AI into their products.

• Jason Warner is a former CTO at GitHub and has extensive experience in AI, particularly with OpenAI and ChatGPT.
• He played a pivotal role in developing GitHub Copilot, a generative AI model for code that enhances programmers' productivity.

"What we had like to think about Copilot being was it was going to be effectively an exoskeleton or an Iron Man suit for programmers."

• This quote illustrates Copilot's role as a tool to augment programmers' capabilities rather than replace them, enhancing efficiency and creativity.

• Copilot is widely adopted, with 1.2 million developers using it and 40% of code being written automatically by it.
• It demonstrates significant time savings, reportedly over 50%, by automating routine coding tasks.

"About 40% of code that gets written gets written automatically by Copilot...shows a greater than 50% time savings."

• This quote emphasizes Copilot's effectiveness in streamlining coding processes and enhancing developer productivity.

• The introduction of Transformers in 2017 marked a significant technological shift, allowing massive amounts of raw data to be processed efficiently.
• OpenAI's GPT-3 and subsequent models have advanced AI capabilities, leading to widespread adoption and experimentation.

"In 2022, two things happened that made these things much more open...image generation models came out in the summer of 2022 and just took off."

• This quote highlights the rapid adoption and impact of AI models once they became accessible and user-friendly.

• AI models rely on reinforcement learning and human feedback to refine and improve their outputs.
• OpenAI uses human feedback to rank and refine model-generated responses, creating a reward model to guide AI behavior.

"Humans are reinforcing it, saying, yes, this makes sense to a human. No, this does not make sense to a human."

• This quote explains the critical role of human feedback in training AI models to produce more accurate and acceptable outputs.

• Users play a crucial role in AI development by providing feedback and usage data, which helps refine AI models.
• OpenAI leverages user interactions with ChatGPT to improve model accuracy and relevance.

"They now have users actually helping them as well in the process."

• This quote underscores the importance of user engagement in enhancing AI models and accelerating their development.

• AI models are being integrated into various domains, including legal, medical, and customer service, to improve efficiency and effectiveness.
• The ongoing development of AI models involves creating domain-specific reward models to tailor AI responses to specific fields.

"As people build legal models or as they build medical models, they'll have to do a similar amount."

• This quote suggests the expanding scope of AI applications and the need for domain-specific adaptations to maximize their potential.

• Large language models and image generation models are the most mainstream AI models.
• Stability AI initially focused on image generation but is now developing a ChatGPT equivalent.
• There is a distinction between closed source models (e.g., OpenAI, Anthropic) and open source models (e.g., Stability AI, Hugging Face).
• Closed source models require sending data to a company that hosts and processes the input.
• Open source models allow companies to download and run the models themselves, offering transparency and control over the model's workings.
• Companies are incorporating AI into products, such as Canva suggesting text and images based on user context.

"The big distinction between a lot of the players today is kind of this distinction of closed source models and open source models."

• Highlights the fundamental division in the AI ecosystem between proprietary and publicly accessible models.

"You might be creating a flyer to promote your podcast, and it's kind of similar functionality to what you might see in Google Docs."

• Example of AI integration in design software, demonstrating practical applications of AI in everyday tools.

• Traditionally, open source was about ethical concerns and transparency in software.
• Most users historically didn't care about open vs. closed source, focusing instead on functionality.
• In AI, open vs. closed source debate is more relevant due to data privacy and access issues.
• Open source allows inspection of data and decision-making processes, which is crucial for transparency.
• There's a potential future where open models are closed and trained on private data, creating exclusive access.

"In traditional open source... it was more ethical and concerns and learning concerns."

• Discusses the historical motivations for open source, emphasizing transparency and security.

"I hope people care about their data. But I think we've proven, you know, society generally has a little bit of care, but not a lot of care about."

• Reflects societal attitudes towards data privacy and the complex interplay between utility and privacy.

• AI models can be trained for various purposes, including potentially harmful ones.
• There's a societal debate on the safety and accessibility of AI models.
• The future may require transparency in AI decision-making, similar to accountability in professions like medicine.
• The complexity of AI systems poses challenges in understanding and regulating their outputs and impacts.
• The debate extends beyond open vs. closed source to encompass broader ethical and societal implications.

"It's going to be like a next level philosophical debate... these models are too dangerous to just let them out into the wild."

• Highlights the ethical and safety concerns surrounding the deployment and accessibility of powerful AI models.

"Could you imagine a system where we've got many models involved that are actually sorting out patient care?"

• Illustrates the potential future scenarios where AI plays a critical role in sensitive decision-making processes.

• The AI landscape consists of foundational model companies and application-focused companies.
• Key players include OpenAI, DeepMind, Anthropic, Cohere, and Ai21.
• Foundational models are expected to underpin numerous future applications.
• There's a limited talent pool for advancing foundational AI models, leading to significant investment in this area.
• The application side is still developing, with potential for disruption in various industries.

"A lot of the headlines you're seeing around these big fundraising rounds right now are... companies that are building their own version of these large language models."

• Discusses the current investment trends and the strategic focus on foundational AI models.

"It's hard to imagine a vertical that's not going to be touched by these products."

• Emphasizes the broad potential impact of AI across different industries and sectors.

• Companies like OpenAI and Stability AI are creating foundational AI models, which serve as the infrastructure for future applications and use cases.
• These foundational models are likened to the operating systems of smartphones, such as iOS and Android, upon which various applications are built.

"These are all the foundational players that are kind of going about building these different elements."

• The development of these foundational models is critical as they empower various applications across different sectors.

• AI technology has broad applications in healthcare, including diagnostics and understanding medical imaging.
• The potential for AI to assist in medical triage and automate note-taking for doctors is significant.

"Medicine obviously is a place where I think this could help just even diagnoses or understanding MRIs."

• AI could enable the creation of triage bots that help patients assess symptoms and decide if medical intervention is necessary.

"You could basically chat with a doctor Bot that could let you know, you know, hey, is this something that needs to be escalated or not?"

• AI models like OpenAI's Whisper could automate the transcription of notes from doctor visits, thus reducing the administrative burden on healthcare professionals.

"You really could actually in the very near term start to see products here that just automatically generate notes in a doctor style."

• AI can reframe medical information for different audiences, making it accessible to both professionals and patients.

"You basically can take that core information and reframe it in a bunch of different ways automatically for whoever your end audience is."

• Generative AI models are expected to remove unnecessary boilerplate content from systems, like legal documents.
• While AI won't replace professions requiring deep expertise, it can automate routine tasks within these fields.

"We're going to see a lot more of this kind of like bullshit boilerplate stuff removed from the systems."

• AI is anticipated to affect jobs that are routine and can be commoditized, potentially leading to job displacement.

"It's sort of like how the likelihood that your job could have been outsourced 25 years ago or something like that."

• The rapid advancement of AI technologies requires society to adapt quickly to potential job losses and changes in job roles.

"The pace at which this is going to happen is going to be mind boggling."

• The societal impact of AI includes potential job losses and the need for retraining workers displaced by automation.
• There are calls for social safety nets to support those affected by job displacement due to AI.

"There needs to be elements of social safety nets or whatever there are that are associated with this."

• The rapid development of AI poses challenges in terms of regulation and safety, with concerns about the potential misuse of AI technologies.

"We're not ready for a machine that's going to try to hack the us, the irs. We're not ready for any of those things."

• There is a debate about whether AI regulation should be handled by governments, companies, or a combination of both.

"This is something that, you know, because it's so transformational and a profound change is something that it does need to be regulated outside of these, these tech companies."

• The comparison between AI and the crypto industry highlights the need for proactive regulation to prevent negative societal impacts.

"I think philosophically, you know, I think I probably fall more in the Ahmad camp of, you know, this is something that, you know, because it's so transformational and a profound change is something that it does need to be regulated outside of these, these tech companies."

• Investors are pivoting from crypto to AI, with entire firms rebranding from crypto to AI.
• Both crypto and AI attract a similar audience interested in sci-fi, algorithms, and mathematics.
• Crypto attracted scammers due to its nature as money, while AI sees scammers creating misleading content.
• The hype around AI is comparable to crypto, but the engagement methods differ.

"We're seeing entire firms change the brandage from crypto to AI."

• Indicates the significant shift in investor focus from crypto to AI.

"Crypto, because of the nature of it being effectively money, also attracted the biggest scammers in the world."

• Highlights a key difference between crypto and AI: the financial scams prevalent in crypto.

• Major tech companies like Microsoft and Google are rapidly adopting AI.
• AI is being integrated into practical applications faster than crypto was.
• The average person can now envision practical uses for AI in everyday life.

"You can get the average person to all of a sudden imagine a world where this is useful."

• AI's potential for practical applications is more accessible to the general public compared to crypto.

• Non-AI businesses should stay informed about AI developments to remain competitive.
• Understanding AI's potential applications can help businesses adopt useful technologies.
• Experimentation with AI can start small and scale as understanding grows.

"The pace at which the space moves, I feel like on Twitter on a week to week basis there's, you know, just new things that people are coming up with."

• Emphasizes the rapid development within the AI industry and the importance of staying informed.

• Businesses should decide between an offensive or defensive approach to AI integration.
• Offensive strategies involve actively developing AI capabilities internally.
• Defensive strategies focus on understanding AI's potential to enhance existing operations.

"If you're taking a defensive posture, just understand what's possible."

• Understanding AI's potential can help businesses enhance existing operations without full-scale development.

• OpenAI's Cookbook and LangChain's GitHub page are recommended resources for exploring AI applications.
• Experimenting with generation models like ChatGPT and Mid Journey can provide insights into AI capabilities.
• Engaging with AI tools can be an accessible entry point for businesses.

"OpenAI is just a great place to, in a light touch way, experiment with these models."

• OpenAI provides accessible resources for businesses to begin exploring AI capabilities.

• Quick experimentation with AI is possible and can lead to rapid insights.
• Legal and ownership issues can complicate AI partnerships and implementations.
• User feedback is crucial in designing AI applications that feel collaborative rather than alienating.

"You can get going really quickly. So start experimenting."

• Encourages businesses to begin experimenting with AI to quickly understand its potential.

• AI's development will be rapid and transformative, with significant societal impacts.
• Recommended reading: "The Age of AI and Our Human Future" by Henry Kissinger and Eric Schmidt for insights into AI's implications.

"This is a real thing. It's going to get super weird from here and it's going to happen fast."

• Highlights the rapid and transformative nature of AI's development and its potential societal impacts.