/AI4h ago

Box CEO Aaron Levie argues applied AI companies build defensibility through ongoing integration rather than raw model performance

Investor Sarah Guo endorsed the focus on domain-specific engineering support.

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Aaron Levie@levie#560inAI

This is a critical post to read if you’re building an applied AI company right now.

“An application earns its place in the untrainable corner by doing unglamorous work: arranging a company's private reality so a model can act on it, handing the model the tools to act, working with the customer to change the reality of its workforce. A company that brings the translation is tough to copy – and the translation never ends. Integration and maintenance run as long as the relationship does, won by teams that put domain-specialized engineers and tools next to the customer.”

There’s still an insanely large gulf between model capabilities and what it takes to apply them to specific corporate workflows. Some of that is technology that needs to be built, a lot is access to (and formatting of) the right data to work with, and a ton more is on the change management and specific implementation work (FDEs, etc.) it takes to make AI work in any specific corporate setting.

2 things can be very true at once: frontier models and labs will continue to grow an incredible amount, and there will be a vast ecosystem of software and services companies that emerge to bring the power of these models to real enterprises. This makes room for new infrastructure provides, applied AI companies in every vertical, new versions of system integrators, and more players.

Incredibly exciting time on all fronts.

sarah guo@saranormous

http://x.com/i/article/2064509889708507136

9:45 PM · Jun 9, 2026 · 46.7K Views
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Many users praised Levie's claim that applied AI companies win through integration and domain expertise because it correctly highlights the hard work of handling messy data and driving organizational change beyond the models themselves.

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sarah guo@saranormous

@levie 🫡

This is a critical post to read if you’re building an applied AI company right now.

“An application earns its place in the untrainable corner by doing unglamorous work: arranging a company's private reality so a model can act on it, handing the model the tools to act, working with the customer to change the reality of its workforce. A company that brings the translation is tough to copy – and the translation never ends. Integration and maintenance run as long as the relationship does, won by teams that put domain-specialized engineers and tools next to the customer.”

There’s still an insanely large gulf between model capabilities and what it takes to apply them to specific corporate workflows. Some of that is technology that needs to be built, a lot is access to (and formatting of) the right data to work with, and a ton more is on the change management and specific implementation work (FDEs, etc.) it takes to make AI work in any specific corporate setting.

2 things can be very true at once: frontier models and labs will continue to grow an incredible amount, and there will be a vast ecosystem of software and services companies that emerge to bring the power of these models to real enterprises. This makes room for new infrastructure provides, applied AI companies in every vertical, new versions of system integrators, and more players.

Incredibly exciting time on all fronts.

3hViews 1.2KLikes 5Bookmarks 1
Tech P@Tech_p001

@levie This is powerful

4hViews 54Likes 2
Asym@Asym_Alwali

@levie Checking it right now.

I'm building a Startup with my phone building anything in public reading this amy Really help in many defferent ways.

4hViews 31Likes 1
Alan Sass@alansass

What are your thoughts on coding looping dynamic workflows when compared to process creation/optimization across orgs?

If those workflows are rapidly changing coding then won’t they come for business process automation next (once it learns and updates context through the process lifespan)?

(Writing this as I’m bored waiting on those long-horizon tasks to complete)

3hViews 64
Amol Parikh@AmolParikh10

@levie Applying AI is harder than building it.

3hViews 34
Hira@Hiraweb3

@levie so true the magic's in the messy middle

3hViews 28
小智🧠SENT@XiaoZhi_BTC

@levie The real edge is in the messy integration, not just the model itself.

3hViews 11
Arslan Iqbal@thearslaniqbal

@levie Translation work is where real value lives.

3hViews 8
Weeb@CryptoWeeb808

@levie Breakthroughs create new possibilities.

The harder challenge is turning those possibilities into repeatable outcomes inside real organizations.

That’s where a lot of the work still lives.

3hViews 4
Preet Dhatt@DhAtT_MaN

@levie This is the part most ppl miss. The model is the easy 20%. The other 80% is getting the company's messy data and dragging the org through the change

3hViews 2