Uncapped with Jack AltmanAgents in the Enterprise | Aaron Levie, CEO of Box | Ep. 3
CHAPTERS
Why AI is a breakthrough for Box: unlocking the value trapped in enterprise content
Aaron frames AI as the missing layer that turns decades of stored enterprise files into usable, queryable organizational knowledge. He explains how most company data goes “cold” after initial collaboration, even though it contains insights that could drive product, sales, and onboarding outcomes.
What “agents” do inside Box: automating content-heavy workflows
Aaron describes agents as specialized assistants that execute content-oriented tasks within Box, often in the background. He gives concrete examples across legal, procurement, marketing, and finance, emphasizing that agents will become plentiful and highly tailored to workflows.
Cross-app agents: making enterprise systems talk to each other
Beyond Box, Aaron argues the real future requires agents that orchestrate work across many SaaS systems. He outlines an emerging ecosystem of protocols and agent-to-agent communication so a single query or workflow can pull from Box, Salesforce, ServiceNow, and beyond.
Startups vs incumbents in the agent era: where new companies can still win
Jack challenges whether incumbents with data and integrations will dominate. Aaron outlines three lanes for startups: incumbents that miss the shift, incumbents constrained by business-model inertia, and entirely new AI-native use cases where no incumbent exists.
AI isn’t only cost-cutting: net-new spend and expanding what companies choose to do
Aaron pushes back on purely labor-replacement models and argues AI will fund new work companies previously couldn’t afford. He uses code generation tools as an example of spend that largely didn’t exist before—augmenting rather than replacing teams.
Customer support and job shifts: efficiency gains get reinvested upstream
Jack notes some functions have finite work (e.g., support tickets), making them more automatable. Aaron responds that savings often get reinvested into more proactive, higher-value human work—like customer success—changing ratios rather than simply eliminating functions.
Pricing AI agents: labor-based pricing vs software-margin convergence (and TAM expansion)
They explore why agents can be priced against human labor today and whether that persists. Aaron predicts competitive pressure will push pricing toward software-like margins—unless a company has a true “cornered resource”—while expanding total spend because AI agents aren’t capped by headcount.
Is AI overhyped? Valuations, outcomes, and why the framing can be misleading
Jack asks if this is a near-term bubble like 1999. Aaron argues the “overvalued short-term but undervalued long-term” framing is awkward; instead, the reality is dispersion—many companies will fail, and the winners will make today’s prices look cheap.
Lessons from being early to cloud: architectural stubbornness and moving faster
Aaron recounts launching just before AWS and the path dependency of building infrastructure in-house. He highlights key early choices—like refusing on-prem deployments—that were painful short-term but crucial to becoming a scalable, multi-tenant platform that can roll out AI instantly to all customers.
Neutrality as strategy: avoiding lock-in to one cloud or one AI model
Aaron explains why Box’s cross-cloud, model-agnostic stance has become a competitive advantage—especially as AI model quality shifts rapidly. Neutrality means customers can adopt the best model (Gemini, OpenAI, Anthropic, etc.) without relocating content or rebuilding workflows.
How to stay motivated for 20 years: platform variety, building energy, and AI-fueled momentum
Jack asks how Aaron sustains founder energy through decades and public markets. Aaron attributes it to enjoying building, having a platform with broad use cases, and a renewed surge of excitement from AI—plus the advantage of not starting from zero again.
Tech and politics: party shifts, policy tradeoffs, and building-oriented governance
In a closing political segment, Aaron discusses perceived shifts in U.S. party alignment and why some tech figures moved right. He argues Democrats have policy failures—especially around building and affordability in California—and shares cautious optimism about pro-innovation, pro-tech signals from the new administration while opposing tariffs.
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