Latent Space
Latent Space: The AI Engineer Podcast — CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0
[AIE Summit Preview #1] Swyx on Software 3.0 and the Rise of the AI Engineer
[AIE Summit Preview #1] Swyx on Software 3.0 and the Rise of the AI Engineer
An excellent conversation following Swyx's Infobip Shift conference keynote, and a preview of next week's AI Engineer Summit. As heard on Podrocket!

No transcript...

This is a special double weekend crosspost of AI podcasts, helping attendees prepare for the AI Engineer Summit next week. Swyx gave a keynote on the Software 3.0 Landscape recently (referenced in our recent Humanloop episode) and was invited to go deeper in podcast format, and to preview the AI Engineer Summit Schedule.

For those seeking to ramp up on the current state of thinking on AI Engineering, this should be the perfect place to start, alongside our upcoming Latent Space University course (which is being tested live for the first time at the Summit workshops).

While you are listening, there are two things you can do to be part of the AI Engineer experience. One, join the AI Engineer Summit Slack. Two, take the State of AI Engineering survey and help us get to 1000 respondents!

Full transcript available here!


Note: This podcast intro voice was AI Anna again, from our Wondercraft pod!

Show notes

  • Explaining Software 1.0, 2.0, and 3.0

    • Software 1.0: Hand-coded software with conditional logic, loops, etc.

    • Software 2.0: Machine learning models like neural nets trained on data

    • Software 3.0: Using large pre-trained foundation models without needing to collect/label training data

  • Foundation Models and Model Architecture

    • Foundation models like GPT-3/4, Claude, Whisper - can be used off the shelf via API

    • Model architecture refers to the layers and structure of a ML model

    • Grabbing a pre-trained model lets you skip data collection and training

  • Putting Foundation Models into Production

    • Levels of difficulty: calling an API, running locally, fully serving high-volume predictions

    • Key factors: GPU utilization, batching, infrastructure expertise

  • The Emerging AI Developer Landscape

    • AI is becoming more accessible to "traditional" software engineers

    • Distinction between ML engineers and new role of AI engineers

    • AI engineers consume foundation model APIs vs. developing models from scratch

  • The Economics of AI Engineers

    • Demand for AI exceeds supply of ML experts to build it

    • AI engineers will emerge out of software engineers learning these skills

  • Defining the AI Engineering Stack

    • System of reasoning: Foundation model APIs

    • Retrieval augmented generation (RAG) stack: Connects models to data

    • AI UX: New modalities and interfaces beyond chatbots

  • Building Products with Foundation Models

    • Replicating existing features isn't enough - need unique value

    • Focus on solving customer problems and building trust

  • AI Skepticism and Hype

    • Some skepticism is healthy, but "AI blame" also emerges

    • High expectations from media/industry creators

    • Important to stay grounded in real customer needs

  • Meaningful AI Applications

    • Many examples of AI positively impacting lives already

    • Engineers have power to build and explore - lots of opportunity

  • Closing and AI Engineer Summit Details

    • October 8-10 virtual conference for AI engineers

    • Speakers from OpenAI, Microsoft, Amazon, etc

    • Free to attend online

Latent Space

Latent Space: The AI Engineer Podcast — CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

The podcast by and for AI Engineers! We are the first place over 50k developers hear news and interviews about Software 3.0 - Foundation Models changing every domain in Code Generation, Computer Vision, AI Agents, and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from tiny (George Hotz), Databricks, Glean, Replit, Roboflow, MosaicML, UC Berkeley, OpenAI, and more.