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Our analysis suggests that the Podcast Episode is not clickbait because it sufficiently addresses the role and emergence of an AI Engineer, distinguishing it from traditional ML roles and discussing relevant skills and industry context.
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We're observing a once-in-a-generation shift in applied AI, fueled by the emergent capabilities and the open-source API availability of foundation models. A wide range of AI tasks that used to take five years and a research team to accomplish in 2013 now just require API docs and a spare afternoon in 2023.
TL;DR
AI engineers focus on product development in the zero to one phase, while ML engineers handle the one to n phase. They need to start with API and prompt usage, evolving into complex production skills. The role is emerging, with a preference for rapid iteration in product development. Vertical AI startups thrive by using proprietary data. AI engineers communicate better about foundation models, and the cost of model training is decreasing. Trends include multimodal capabilities and the acceptance of hallucination as a creative feature. Collaboration with product managers is essential, and AI tools are being rapidly adopted in productivity.
Key Ideas
🚀 AI engineers focus on the zero to one phase, prioritizing product development, while ML engineers handle the one to n phase with more mathematical specialization.
🛠️ The AI engineer role is emerging as a distinct subdiscipline, filling gaps in traditional ML engineering, especially in areas like agent research and product optimization.
📈 AI engineers need to start with API and prompt usage but should evolve into more complex production skills as they advance.
🔥 The 'fire, ready, aim' approach is preferred for AI product development, emphasizing rapid iteration and feedback over traditional methods.
🏢 Vertical AI startups, leveraging proprietary data, dominate high-margin markets by addressing specific industry needs, making them less vulnerable to competition from larger players.
💬 AI engineers are better equipped to communicate the state-of-the-art with foundation models compared to ML engineers, enhancing team dynamics.
💸 The cost of model training is decreasing rapidly, and inference speeds are increasing, enabling more efficient AI development.
🖼️ Multimodal capabilities and expanding context windows are key trends, allowing models to process and generate across various modalities and handle larger inputs.
🎨 Hallucination in AI can be seen as a feature, fostering creativity and new knowledge generation, especially in high-temperature, non-deterministic modes.
🤝 AI engineers are becoming more product-focused, with a growing need for collaboration with product managers and domain experts to guide product decisions.
❓ The legitimacy of the AI engineer role is debated, but demand is growing, and the role is becoming more recognized in the industry.
🤖 AI tools are rapidly adopted in internal productivity, with AI employees performing parts of human jobs, offering capital leverage without managing humans.
🎤 Conferences and community-building events are crucial for AI engineers to stay relevant, with valuable conversations being more important than mere attendance.
Conclusion
The legitimacy of the AI engineer role is debated, but demand is increasing. Conferences and community events are vital for staying relevant in the field.
📃 Podcast episode Mini Summary
AI Engineers: Masters of the Zero to One Phase
🚀 AI engineers thrive in the zero to one phase, where the focus is on building new products from scratch. In contrast, ML engineers excel in the one to n phase, where they optimize and scale existing models. AI engineers are more product-focused, often working on front-end and full-stack tasks, while ML engineers are more mathematically specialized, dealing with large datasets and model optimization.
"AI engineers are more of the zero to one phase, whereas the ML engineer is more the one to n."
Filling the Gaps: The Rise of the AI Engineer
🛠️ The AI engineer role is emerging as a distinct subdiscipline, filling gaps that ML engineers traditionally don't cover. This includes areas like agent research and product optimization. AI engineers bring a different set of assumptions and experiences, often excelling in areas where ML engineers may not have a unique advantage.
"AI engineers are like a plug or filler gap for all the things that are not traditionally part of the ML engineer skill set."
From API Calls to Production: The AI Engineer's Learning Curve
🧠 AI engineers often start by using APIs and prompts to quickly get products up and running. However, as they advance, they need to develop more complex production skills, such as building fine-tuning stacks and inference capabilities. The deeper they go, the more they need to incorporate ML engineering skills into their work.
"You just need to know how to call a few APIs, but eventually, you’ll need to invest in your ops stack and fine-tuning."
Fire, Ready, Aim: The New Approach to AI Product Development
📈 In AI product development, the "fire, ready, aim" approach is preferred. This method emphasizes rapid iteration and market feedback over traditional, more deliberative processes. The goal is to ship products quickly, gather data, and iterate based on real-world usage.
"You win by moving really quickly and getting information from the market from shipping products."
Vertical AI Startups: The Power of Proprietary Data
🔥 Vertical AI startups are dominating high-margin markets by leveraging proprietary data to solve specific industry problems. These startups are less vulnerable to competition from larger players like OpenAI because they focus on niche markets with unique data and high customer demand.
"Vertical startups have the most proprietary data, pursue high-margin markets, and are less likely to be steamrolled by OpenAI."
AI Engineers: The Bridge Between Product and State-of-the-Art Models
🏢 AI engineers are better equipped to communicate the state-of-the-art in foundation models compared to ML engineers. Their product-focused mindset allows them to translate cutting-edge AI capabilities into practical applications, making them invaluable in team dynamics.
"AI engineers are much more equipped to tell product managers what the state-of-the-art is with foundation models."
The Cost of AI is Dropping Fast
💬 The cost of model training is decreasing rapidly, and inference speeds are increasing. This trend is enabling more efficient AI development, allowing companies to build products that may lose money today but will become profitable as costs continue to drop.
"The cost of a GPT-4 level model was not possible in 2022, but now it’s $2 per million tokens and dropping."
Multimodal and Expanding Context: The Future of AI
💸 Key trends in AI include multimodal capabilities—the ability to process and generate across various modalities—and expanding context windows, which allow models to handle larger inputs. These advancements are opening up new possibilities for AI applications.
"We used to have 4,000-token context models, and now we have million-token context models."
Hallucination as a Feature, Not a Bug
🎨 In certain high-temperature, non-deterministic modes, hallucination in AI can be seen as a feature rather than a bug. This fosters creativity and the generation of new knowledge, especially in applications where novelty is valued over accuracy.
"What if hallucination was a feature and not a bug?"
AI Engineers and Product Managers: A Growing Collaboration
🤝 AI engineers are becoming more product-focused, and there’s a growing need for them to collaborate with product managers and domain experts. These collaborations are crucial for making informed product decisions and ensuring that AI capabilities align with customer needs.
"No amount of AI engineering can solve a bad product decision."
The Legitimacy of the AI Engineer Role
❓ The legitimacy of the AI engineer role is still debated, but demand for the role is growing. While some argue that every software engineer should be an AI engineer, the reality is that specialization is becoming more recognized, and the role is gaining legitimacy in the industry.
"AI engineer is going to be low status for a long time, but it’s going to feel increasingly less illegitimate every year."
🤖 ### AI Tools for Internal Productivity: The Rise of AI Employees
AI tools are being rapidly adopted for internal productivity, with AI employees performing parts of human jobs. This offers companies capital leverage without the need to manage human workers, allowing AI systems to work around the clock.
"The people who are most able to take advantage of AI to do that task are going to be the most capital leveraged."
Conferences and Community: Staying Relevant as an AI Engineer
🎤 Conferences and community-building events are crucial for AI engineers to stay relevant. These events provide opportunities for valuable conversations and networking, which are often more important than the talks themselves.
"The ultimate win condition for this conference is that you don’t even go for the talks; you just go for the conversations."