Where are the biggest value opportunities in the AI stack?

Most answers highlight value in application layers, proprietary data, and workflow integration, while a few emphasize upgrading infrastructure or data processing as key opportunities beyond foundational models.

7 answers

Answers

  • Ben Horowitz

    Horowitz · Andreessen Horowitz

    AI Infrastructure

    Ben Horowitz points to AI infrastructure, saying the infrastructure for AI is out of date and not strong enough — much like cloud computing's, which eventually got rebuilt from storage to networking.

    The answer starts at 48:40.

    a16z Podcast · Listen on Apple Podcasts
  • Emily Man

    Partner · Primary Venture Partners

    Applications Layer

    Emily Man says she focuses on the applications layer, the last level of the stack, after chips, infrastructure and models; she notes past tech shifts lay the pipes first before building on top.

    Run the Numbers with CJ Gustafson · Watch on YouTube
  • Itamar Novick

    Founder & General Partner · Recursive Ventures

    Application Layer

    Itamar Novick points to the application layer, saying AI infrastructure is in a bubble and application companies delivering end solutions to customers are the ones that emerge, not LLM firms.

    The answer starts at 24:20.

    The Tech Trek · Listen on Apple Podcasts
  • Morgan Flager

    Managing Partner · Silverton Partners

    Proprietary Data

    Morgan Flager says the biggest advantage he looks for is a proprietary data and learning advantage, since data few others can access and learning from it or its metadata can sustain an edge others can't copy.

    VC10X with Prashant Choubey · Watch on YouTube
  • Anish Acharya

    General Partner · Andreessen Horowitz

    Consumer Product Design

    Anish Acharya says the opportunity for this technology lies in basic consumer needs—feeling connected, loved, making progress, having fun—and that it is not a model or capability challenge but just a product design challenge.

    The answer starts at 1:14.

    a16z Podcast · Listen on Apple Podcasts
  • Julien-David Nitlech

    Managing Partner · Iris

    Data-Homogenizing Layers

    Julien-David Nitlech points to data-homogenizing layers, saying everyone will use them to make data homogeneous and operate automations, since current stacks aren’t fit for automation.

    The European VC · Watch on YouTube
  • Sam Altman

    Chief Executive Officer · OpenAI

    Workflow Depth & Judgment

    Sam Altman says the value is shifting to compute scale, workflow depth and human judgment, and that a founder's moat is the workflow, integration depth and switching costs built around whichever model they use.

    The AI Corner · The AI Corner · Read

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