Agents in the Enterprise | Aaron Levie, CEO of Box | Ep. 3

Agents in the Enterprise | Aaron Levie, CEO of Box | Ep. 3

Jack Altman (host), Aaron Levie (guest)

Unlocking value from dormant enterprise contentAgent-driven content workflows inside BoxCross-platform agent interoperability and emerging protocolsIncumbents vs startups: execution, innovator’s dilemma, net-new marketsAgent pricing models: labor-comped vs software-margin convergenceTAM expansion beyond seat licenses via AI “digital employees”Platform neutrality across AI models (Gemini, OpenAI, Anthropic)Box architecture lessons: multi-tenant SaaS and uniform versioningWorkforce impact: automation, role shifts, proactive customer successPolitical sidebar: Democrats, regulation, building, and tech policy direction

In this episode of Uncapped with Jack Altman, featuring Jack Altman and Aaron Levie, Agents in the Enterprise | Aaron Levie, CEO of Box | Ep. 3 explores aaron Levie on enterprise AI agents, pricing, and platform neutrality Levie frames AI as the long-awaited way to activate the massive, underused value locked in enterprise documents—contracts, financials, marketing assets, and HR records—most of which sit idle after creation and storage.

Aaron Levie on enterprise AI agents, pricing, and platform neutrality

Levie frames AI as the long-awaited way to activate the massive, underused value locked in enterprise documents—contracts, financials, marketing assets, and HR records—most of which sit idle after creation and storage.

He describes a near-term future where companies create millions of specialized, content-centric agents (legal, procurement, marketing, finance) that operate inside Box and increasingly coordinate across systems like Salesforce, ServiceNow, and Slack.

The conversation covers why incumbents may still lose (asleep-at-the-wheel execution gaps, innovator’s dilemma around seat-based pricing, and net-new AI use cases) and why agent pricing should ultimately compress from “labor-comped” to software-like margins.

Levie also reflects on Box’s early architectural choices (multi-tenant SaaS, no on-prem) as a strategic advantage in the AI era, and briefly discusses U.S. tech politics, California’s governance failures (especially housing), and cautious optimism about pro-innovation tech policy momentum.

Key Takeaways

Enterprise data is valuable but mostly sits unusable after creation.

Levie argues the typical file lifecycle is “hot” briefly, then archived for compliance or retrieval—without being queried for insights that could improve sales, onboarding, product discovery, or decision-making.

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Box’s AI strategy is to turn content repositories into agent-driven workflow engines.

He envisions (and says Box is already enabling via AI Studio primitives) customers creating agents that review contracts, process invoices, extract metadata from assets, and generate domain-specific analyses directly against enterprise content.

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The real agent future requires coordination across many SaaS systems, not just one platform.

Work happens across Salesforce, ServiceNow, Workday, Slack, and more; Levie expects agents will need standards/protocols to call each other and combine data from multiple systems to complete tasks and produce full-fidelity reports.

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Startups can win even when incumbents have data and integrations.

Levie lists three durable startup wedges: incumbents that don’t move fast, incumbents blocked by business-model “innovator’s dilemma” (seat revenue threatened by automation), and net-new AI categories where no traditional incumbent exists.

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Many AI products are net-new spend before they are cost-savers.

Using AI coding tools as an example, he claims much current adoption is additive—teams swipe a credit card to augment output rather than replacing headcount—creating new budget lines instead of merely reallocating old ones.

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Agent pricing won’t stay tied to human labor for long in competitive markets.

Levie expects prices to fall from “half the cost of a person” toward compute-plus-software margins, unless a vendor has a true cornered resource (e. ...

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AI expands the software market beyond seat counts by enabling ‘digital employees.’

Seat-based SaaS is capped by headcount, but a 20-person company could run dozens of AI roles (lawyers, SDRs, marketers), meaning software spend can rise even if per-unit pricing compresses.

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Multi-tenant SaaS architecture becomes a compounding advantage in the AI era.

Levie credits Box’s early refusal to offer on-prem and its strict platform integration discipline: every customer is on the same version, so new AI capabilities can be turned on broadly without upgrade fragmentation.

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Neutral platforms gain leverage when AI model leadership changes quickly.

Because model quality shifts (Gemini vs OpenAI vs Anthropic), Levie argues customers benefit when their content layer isn’t vertically locked to a single cloud/model provider—new breakthroughs can be adopted immediately.

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Automation may shift work upstream rather than simply eliminate it.

He suggests savings in reactive support can fund proactive customer success, noting SaaS functions are often constrained by ratios (CSMs per account) and talent can retrain over time—though he acknowledges disruption in some roles.

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Notable Quotes

Most of it is underutilized... you create one, you share it, you collaborate, and then it goes somewhere, and you never see it again.

Aaron Levie

You will have... millions and millions of those kinds of agents that get created. They'll help you automate the work that you do with content in every field.

Aaron Levie

Innovator's dilemma... it's a business model-oriented issue... nobody in the management team wants to have less revenue the next year.

Aaron Levie

If I had to bet... does AI remain comped at labor... I'm gonna bet on... infrastructure cost plus software and some margin.

Aaron Levie

Now with AI, that 20-person company could have, like, 10 AI lawyers and 10 AI SDRs... that's the big TAM expansion.

Aaron Levie

Questions Answered in This Episode

In Box AI Studio, what are the exact “core primitives” you consider necessary to build a reliable enterprise agent (tools, memory, permissions, evaluation, audit logs)?

Levie frames AI as the long-awaited way to activate the massive, underused value locked in enterprise documents—contracts, financials, marketing assets, and HR records—most of which sit idle after creation and storage.

Get the full analysis with uListen AI

What’s your current view on the leading approach for agent-to-agent interoperability across SaaS—standard protocols, vendor hubs (e.g., Salesforce), or something else?

He describes a near-term future where companies create millions of specialized, content-centric agents (legal, procurement, marketing, finance) that operate inside Box and increasingly coordinate across systems like Salesforce, ServiceNow, and Slack.

Get the full analysis with uListen AI

For a cross-system task like “run a full customer report,” what should be the source of truth when Box content conflicts with Salesforce fields or ServiceNow tickets?

The conversation covers why incumbents may still lose (asleep-at-the-wheel execution gaps, innovator’s dilemma around seat-based pricing, and net-new AI use cases) and why agent pricing should ultimately compress from “labor-comped” to software-like margins.

Get the full analysis with uListen AI

Which enterprise workflows do you expect to be ‘agent-first’ the soonest: contract review, invoice processing, marketing ops, or something else—and why?

Levie also reflects on Box’s early architectural choices (multi-tenant SaaS, no on-prem) as a strategic advantage in the AI era, and briefly discusses U. ...

Get the full analysis with uListen AI

If pricing compresses to software-like margins, what becomes the durable moat for agent products: distribution, proprietary workflows, data access, or governance/compliance?

Get the full analysis with uListen AI

Transcript Preview

Jack Altman

[upbeat music] All right, Aaron, thanks for doing this. Really appreciate you making time. I know you're really busy, and it's really good to get to sit down and talk to you.

Aaron Levie

Good to be here. Uh, I'm, I'm actually always here, and so thank you for coming.

Jack Altman

My pleasure. Um, so I wanna start by talking about something that you've been super vocal about online lately: AI agents, AI software in the enterprise. Obviously, everybody's talking about it, but you've been talking about it in, like, great detail-

Aaron Levie

Yeah

Jack Altman

... and how it relates to Box. You've got this-

Aaron Levie

I thought you were gonna say that we were talking about it first.

Jack Altman

Um, [chuckles]

Aaron Levie

Okay. [chuckles]

Jack Altman

You got the- that's right, too. But can, can you tell me sort of briefly just, like, where are you sort of, um, where are you thinking about, you know, AI as it relates to Box?

Aaron Levie

Yeah.

Jack Altman

How do you think agents are playing out right now? Obviously, this is what you're talking a ton about, but, like, what's sort of the kinda high level about what's in your mind right now?

Aaron Levie

So for us, the reason I'm, I'm just so, so pumped about AI right now is we've been building this platform for nearly two decades, uh, to help enterprises store, manage, share, collaborate on their most important data. And, um, and, you know, th- that data is their financial documents, their contracts, their marketing assets, their employee records, all that data. And inside of that information contains an incredible amount of value, but most of it is underutilized. And so, you know, you just think about what, what you mostly do with your files. You, you know, you create one, you share it, you collaborate, and then it goes somewhere, and you never see it again. And that, that... We, we, we just see that with the life cycle of data. Data, you know, starts out hot for the first couple o- you know, hours, couple days, week or two, and then it sort of you go- it goes into a place where you just manage it. It's actually really important to keep around because you might need to pull it up, like, five years later, or there might be some legal reason why you need it, but, uh, but you never tap into it in the meantime. You don't ask it questions. You can't pull out value from it, and yet it actually contains an incredible amount of wealth of, of, of value for an organization because it might have an insight that would lead to your next product discovery. It might re- you know, have information that would make a sales rep better at selling their product. It might have information that a new employee onboarding into a company will ramp up faster, but you have not been able to tap into it. So for us, A- AI is just this massive breakthrough, which is we can finally actually open up the value of all of this data that organizations are sitting on. We kind of are in the right place, right time in terms of having built out a platform that, uh, 115,000 customers trust. We're in about 67% of the Fortune 500, and companies have been using Box to be able to manage that data, automate workflows around it, secure that data, and now AI kind of plugs in right at the core of, of everything we're doing. So we're sort of running the company and, and imagining the company as if, you know, if we had started the company in 2025, what would we be building? How would we be running it? What would our business model be? And we're asking ourselves, like, like, "Are we doing everything as if we were starting from scratch in this new era of AI?" As opposed to, you know, the, the traditional challenge with, with being either an incumbent or, or some, some company that's been around for a while is you, you take too long to, to address a new technology. You don't sort of pivot hard enough. Uh, you, you don't reimagine your business model for that new era that, that's emerging. And so we're trying to, you know, learn all those lessons, m- maybe to some extent, to prevent anything bad from happening. But then I think just generally, I'm just way more excited about the upside of, of what's going on.

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