The industry is debating whether AI should be viewed as augmenting or replacing human-centric tasks. But we don’t yet have a framework to discuss the technical and business impacts of that spectrum of decisions.

SHOW: 926

SHOW TRANSCRIPT: The Cloudcast #926 Transcript

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• The final 5% of AI success

HOW DO WE THINK ABOUT THE INVOLVEMENT OF HUMANS WITH AI SYSTEMS?

• What do we think AI systems should be capable of?
• What do we think human systems should be capable of?
• We know how to quantify error, but do we really know how to quantify error?
• Almost every question about AI comes down to augment vs. replace, and yet humans tend to skew towards the replace angle.
• We like the thought of humans in the loop, but often don’t want to be bothered to interact with humans
• We haven’t yet created a framework to think about the human +/- AI involvement, because we haven’t really created it for general-purpose automation yet either.

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Joel Christner, (@joelchristner, Founder/CEO at @viewyourdata) discusses the complexities of data management in AI, structured and unstructured data, the importance of RAG pipelines and vector databases.

SHOW SUMMARY: Aaron and Joel discusses the complexities of data management in AI, focusing on the concept of universal data representation. They explore the challenges organizations face with structured and unstructured data, the importance of RAG pipelines and vector databases, and the implications of data privacy in regulated industries. The conversation also touches on managing model versions and the emerging patterns in AI tooling that can help enterprises effectively utilize AI technologies.

SHOW: 925

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Topic 1 - Welcome to the show, Joel. Give everyone a quick introduction.

Topic 2 - Our topic today is everything data and how to represent it and embed it into AI systems. First, what is the challenge with data, structured or unstructured, in organizations today and what is behind the concept of Universal Data Representation

Topic 3 - Industry or customer specific data today is big challenge for organziations, especially in highly regulated industries such as healthcare, financial services, etc. The most prevalent solution I am seeing is taking an existing foundational model and then adding a RAG pipeline vs. the cost and time to fine tuning. What are you seeing?

Topic 4 - Even when companies have good data, that doesn’t mean that data makes it into the AI pipeline correctly, this is where the embedding problem and your concept of Universal Data Representation comes into play, correct?

Topic 5 - But, once you get the first model out, then what? How should the data and models be handled over time? How do you create a platform and a continuous feedback loop to improve the results over time?

Topic 6 - What are the most successful use cases you are seeing today with your customers?

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Where does the next phase of AI-assistants for software development go next? Is it an evolution of developer productivity, or a complete rethinking of the barriers and limitations for broader software development?

SHOW: 924

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WHERE DO AI DEVELOPER-ASSISTANTS GO NEXT?

• A year ago it felt like co-pilots were one of the entry point use-cases for AI.
• Since then we’ve seen numbers say the uplift is 10-20% productivity.
• Microsoft claims that 20-30% of their code is now written by AIs.
• We’ve seen many senior developers speakout that it’s not replacement level technology and they don’t trust it.
• Companies like Cursor have a $9-10B valuation. Windsurf just got purchased for $3B by OpenAI.
• Microsoft has Co-Pilot (based on OpenAI models). Google and Amazon are rumored to be launching their own.
• Lots of companies are launching agents (IBM, Salesforce, Oracle, etc.), and lots of agent frameworks now exist.
• So where does it go next? Is it just wide-spead adoption of developer productivity?
  • Is it specialized functions within developer workflows? (e.g. CI/CD, documentation, security evaluations, bug fixes, long-term maintenance, etc.)
  • How far are we from teams being just a few architects, leads, Sr. Devs, and then teams of AI’s (agents, etc.)?
    • Is that a good thing for Sr. Dev personalities that didn’t want to focus on soft-skills?
    • Does that allow for greater experimentation against feature-requests or stories, since they can create more, test more, etc.?
    • Do we start to see companies create skunkworks teams/groups that try to adopt this approach?
    • Are product managers ready to have zero-backlogs and the demand for new ideas to increase?

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