What is an AI Engineer with Shawn Wang (a.k.a Swyx) of @LatentSpaceTV

Summiz Holo

Emerging engineering roles in AI, the shift from traditional ML processes to rapid iteration, and the necessity for specialized skills in a dynamic landscape

• There's some advantage to being early, but it doesn't guarantee success.
• An engineer fills gaps in the traditional ML engineer skill set, focusing on the zero to one phase.
• A fire Ready Aim approach is preferred over Ready Aim Fire for faster iteration and market feedback.
• The old ML engineering process was more deliberative; now speed is crucial for success.
• A new type of engineering role is emerging as a result of LLMs and generative AI.
• Software engineering will have a new subdiscipline specializing in AI applications.
• The API line between ML and product has shifted, leading to more outsourcing of ML roles.
• There's a spectrum from data constraint to product constraint with various engineering roles.
• Keeping up with the evolving AI stack is a full-time job, and specialists will outperform generalists.

Distinction between full-time AI engineers and traditional engineers, evolving AI landscape enabling rapid task completion, unique skill sets for AI engineers versus ML engineers, initial API and prompt usage evolving into complex production skills, AI engineers addressing gaps in ML engineering, qualitative differences in problem-solving approaches

• There is a distinction between full-time AI engineers and traditional engineers, as companies seek specific profiles for AI roles that differ from general engineering skills.
• The evolving landscape of AI allows tasks that once took years of research to be accomplished quickly, often with just an API and some basic knowledge.
• The skill set for an AI engineer differs from that of an ML engineer; while having ML skills can enhance success as an AI engineer, it's not a strict requirement to get started.
• AI engineers need to know how to use APIs and prompts initially, but as they advance, they should invest in more complex skills related to production and model optimization.
• AI engineers fill gaps in skills not traditionally covered by ML engineers, particularly in areas like agent research, leveraging different experiences and perspectives.
• There is a qualitative difference between ML engineers and AI engineers, with ML engineers often focused on end-to-end problems while AI engineers may approach tasks differently.

AI engineers prioritizing product development in the zero to one phase, contrasting with mathematically specialized ML engineers managing the one to n phase, and the emergence of hybrid personas enhancing team dynamics

• AI engineers focus on the zero to one phase, while ML engineers handle the one to n phase.
• AI engineers are more product-focused and possess front-end skills, whereas ML engineers are mathematically sophisticated and specialized.
• The model is given to AI engineers, who then work on making it a useful product.
• There is a new type of persona emerging that blends skills from both AI and ML engineers, with a product focus.
• Team composition should ideally have a higher ratio of AI engineers to ML engineers, especially in mature teams.
• Product managers and domain experts play a crucial role in guiding engineers, providing customer insights, and making product decisions.
• AI engineers are better equipped to communicate the state-of-the-art with foundation models compared to ML engineers.
• Product managers and subject matter experts are becoming more directly involved in the product creation process, collaborating closely with AI engineers.

Evolving collaboration dynamics, AI engineer role ambiguity, and the ML versus AI perception divide

• The shape of collaboration is evolving, allowing domain experts to be more involved in their work.
• There are criticisms about the necessity of the AI engineer role, with some arguing every software engineer is already an AI engineer.
• The perception of AI engineer as a low-status role compared to ML engineers and research scientists is acknowledged.
• There is a lack of standardization in defining the AI engineer role and its required skills, with no clear hierarchy established yet.
• OpenAI's job listing for an AI engineer requires extensive ML experience, raising questions about the actual necessity of such qualifications.
• The term 'AI engineer' is preferred over alternatives like 'AI developer' due to its engineering implications.
• The distinction between machine learning (ML) and AI, where ML is seen as functional and AI as more magical, is noted.

Debate on AI engineer legitimacy, early career advantages, hype cycles, community-building events, evolving conference tracks, startup and Fortune 500 perspectives, and revenue validation in AI solutions

• The legitimacy of the AI engineer title is debated, but demand and supply indicate growing recognition of the role.
• Being early in the AI engineering field can provide advantages, but it doesn't guarantee success; continuous learning is crucial.
• Hype in AI can lead to quick interest but can dissipate just as fast if products fail to deliver.
• The upcoming event represents the speaker's vision of AI engineering and aims to foster community connections.
• The conference has evolved from a single track to multiple tracks, reflecting diverse interests and topics in AI.
• New tracks address criticisms of AI adoption and include perspectives from both startups and Fortune 500 companies.
• The importance of revenue in validating AI solutions is emphasized, as it keeps companies accountable.

Valuable conversations over mere attendance, justifying conference expenses through work relevance, launching foundational AI models, audience size disparities between in-person and online participation, adopting rapid development mentalities, favoring iterative approaches in AI product development, prioritizing rapid deployment and feedback for AI improvements, and experimenting with AI products while managing risks

• The ultimate win condition for conferences is valuable conversations rather than just attending talks.
• Conferences are expensive, and people need to justify the expense by gaining work relevance and opportunities.
• The aim is to launch three Foundation models, though not at the highest tier yet.
• There is a significant difference in audience size between in-person and online participation at conferences.
• AI and product engineers should adopt a faster development mentality, moving from a traditional timeline to a quicker, more iterative approach.
• The 'fire, ready, aim' approach is preferred over the traditional 'ready, aim, fire' for product development in AI.
• Rapid deployment and gathering feedback are essential for improving AI products effectively.
• Companies can experiment with AI products, acknowledging potential risks while setting appropriate expectations.

Vertical AI startups leveraging proprietary data for high-margin market dominance and tailored industry solutions

• Vertical startups perform better than horizontal startups due to proprietary data and insights into non-technical audiences with burning pain points.
• High margin markets are pursued by vertical startups, making them less vulnerable to competition from larger players like OpenAI.
• Examples of successful vertical AI startups include Harvey in the legal space, Midjourney in the creative market, and Perplexity as an anti-Google.
• Vertical AI solutions often address specific industry needs, such as real estate virtual staging and research analysis in finance.
• The horizontal developer tool space is crowded, making it challenging for buyers to navigate and find essential tools.
• Buying solutions initially and selectively building later is advisable for understanding community problems and avoiding lock-in issues.

Leveraging AI for Enhanced Productivity, Tool Adoption, and Competitive Dynamics in Human-AI Collaboration

• You should buy tools to move faster and explore existing solutions before building your own.
• There are internal productivity tools that get adopted quickly, especially developer tooling like co-pilot and meeting summarizers.
• AI employees can perform parts of human jobs and will grow in their roles over time, offering capital leverage without the need to manage humans.
• Comparing humans and AI directly is a mistake due to their differing strengths and weaknesses.
• The concept of 'human level AI' is flawed; AI is already superhuman in some areas and weaker in others.
• Key battlegrounds for AI companies include competition over data, GPUs, god models vs. domain-specific models, and RAGOps.
• Not all areas have intense competition; for example, code generation is important but lacks a 'war' aspect.

Navigating influencer noise on Twitter with essential research filters, decreasing model training costs, evolving MML benchmarks, increasing inference speeds, expanding context windows, and emerging multimodal capabilities

• Having a filter for worthwhile research directions is essential to navigate the influencer noise on Twitter.
• Key research trends include long inference, synthetic data, alternative architectures, mixture of experts, and online learning systems.
• The cost of model training (like a 70M LLM) decreases by 5 to 10x yearly, influencing product strategy.
• MML (massive multitask learning) is a benchmark for AI models, created by Dan Hendricks, but will likely evolve in the next few years.
• The cost to achieve the same AI intelligence performance is decreasing, with future models expected to trend towards lower costs.
• Inference speed is increasing significantly, with credible sources predicting Brock aims for 5,000 tokens per second.
• Context windows for models are expanding from 4,000 tokens to millions of tokens, creating new use cases.
• Multimodal capabilities are becoming a prominent trend in AI, with models processing and generating across various modalities.

Exploring the Creative Potential of High Temperature AI Models, Variance Trends, and Hallucination as a Feature in Knowledge Generation

• Variance is an interesting trend that could become more permanent over time.
• Temperature zero use cases involve locking down a model for retrieval-oriented generation, which is seen as a boring use case.
• There is a potential benefit in allowing models to be less constrained, leading to unexpected creativity and ideas.
• Hallucination in AI could be considered a feature rather than a flaw, providing new creative insights.
• A combination of models acting as conjecture machines and testing mechanisms is necessary to generate new knowledge.
• High temperature, non-deterministic modes in AI systems are essential for knowledge creation.