Aaron Levie

Enterprise AI’s near-term bottleneck is organizational integration, not model capability. Aaron Levie says coding agents thrive because engineers are technical, autonomous, able to debug failures, and working on verifiable outputs; ordinary knowledge work instead spans less-technical users, fragmented data, legacy systems, and undocumented relationships. Diffusion from startups into large enterprises will therefore take “a number of years.”

Top-down AI mandates are producing misleading failure statistics and measurable theater rather than operational change. Martin Casado says the claim that 95% of large-company AI efforts fail is “clearly silly” because employees are already using ChatGPT effectively; he distinguishes that from centralized, consultant-led programs that lack operational alignment. Token-counting incentives make the distortion worse—Aaron Levie says he and his coworkers assign agents useless tasks because “you get whatever you measure.”

Integration, permissions, and change management remain the durable enterprise workload—and potentially a decades-long services market. Steven Sinofsky’s hard stop is that every company with 1,000-plus employees or more than 10 years of history contains “a massive [amount of] stuff that’s sitting there waiting to be integrated,” while “AI actually doesn’t help to integrate anything.” Levie argues this makes work by Accenture, Deloitte, and other systems integrators entirely logical: people must implement the agents that may later automate work.

The central architectural shift is to treat an agent as a worker with identity, onboarding, and bounded authority—not merely software embedded in another product. Casado argues that companies already spent 40 years designing interfaces and processes for messy, nondeterministic humans, so an enterprise can “hire the agent,” give it email and application access, and reuse those controls. The unresolved problem is context: agents can operate at enormous parallel scale but do not naturally know which Sally or Bob to ask when the documented system fails.

SaaS may gain machine seats, but the API-versus-browser path remains contested. Levie sees Salesforce’s “full headless” move as a bellwether and says machine use could reach 100x or 1,000x human activity; Sinofsky calls an agent “another seat” because sharing human credentials would be indefensible. Casado and Sinofsky favor an API/MCP/CLI-first approach with browser use when those interfaces fail, while Casado notes that agents may need ordinary Safari when headless browsers are blocked.

Agentic volume creates two separate risks: familiar infrastructure scaling and less-understood operational entropy. Sinofsky asks what happens when 10,000 employee agents each hit a SaaS system 500 times more often; Casado says caching and standard distributed-systems techniques can address that load. His deeper concern is that AI-generated code “gets worse over time,” potentially creating as many problems as solutions, and companies do not yet know how to govern long-running agents whose output continuously changes shared systems.

The speakers expect AI to expand software, infrastructure, and skilled employment before it eliminates them. Box saw AI produce roughly 80%–90% of one feature, yet security review still constrained release; Levie therefore estimates perhaps 2x–3x engineering productivity, not 5x–10x. More code creates more systems to secure, upgrade, and repair, while John Deere, Caterpillar, Eli Lilly, and thousands of other companies can employ engineers using Claude Code, Codex, and Cursor: “We’re just getting started with the jobs on this front.”

🔗 Original source & video: Aaron Levie on AI Adoption and Enterprise Workflows | The a16z Show

Levie’s central labor bet is that AI will produce more engineers and lawyers over the next five years, not fewer. Tech represents perhaps 8-15% of GDP; giving the other 85% Silicon Valley-grade engineering shifts developers toward John Deere, Caterpillar, and Eli Lilly. AI also floods lawyers with draft contracts and memos while courts, patents, and professional approval remain constrained: “We haven’t removed humans from the loop. We’ve just changed where they enter the loop.”

Enterprise adoption’s bottleneck is organizational redesign, creating perhaps 500,000 to 1 million “agent operator” jobs. These technically fluent operators will understand MCPs, CLIs, skills, and AGENTS.md while rebuilding regulated workflows around agents. The resulting software workload is less another phone app than “an unlimited amount of software” connecting fragmented data and automating background processes.

SaaS is not uniformly cooked: agents strip value from button-heavy interfaces but amplify systems rich in APIs, proprietary business logic, governed data, and human review. An ERP is more than its database because supply-chain and accounting logic remain valuable; Box similarly says agents could drive API activity up by potentially 100x or 1,000x. Levie’s mandate: become “the best place where agents want to work with that data.”

Token spend escapes the IT budget and becomes regular operating expense, potentially doubling global technology spend rather than increasing it tenfold. Enterprises could trade a marketing campaign against automation, allocate premium models to the highest-value 5-10% of users, or make teams pitch for compute like contestants on Shark Tank. Unlike startups, large enterprises cannot “token max” indiscriminately: they have annual budgets and, for public companies, EPS commitments.

AI creates a structurally larger cyber market because agents both generate more vulnerable code and let attackers scan faster. As AI moves from writing most code toward 90% or 95%, every additional feature becomes another opportunity to open the wrong port or introduce a flaw. Defensive agents can review that output, but Levie’s formulation captures the circular opportunity: “Agents are the solution to the problem that agents have caused.”

Public software is being treated as an indiscriminately bucketed sector, but Levie expects separation over the next one to two years. Three-times free cash flow can be “aggressively low” for companies that respond effectively, although parts of software were previously valued beyond plausible terminal outcomes; winners will possess deep workflow ownership, valuable data, agent-ready APIs, and monetizable automation. He views Atlassian as potentially oversold because more engineering should expand demand for engineering infrastructure, even as its products must evolve.

Levie would still “load up” on frontier-model rounds despite the host’s test of an $850 billion valuation needing roughly a 3x path to $2.1 trillion. His cloud analogy is AWS at $500 million of revenue in 2010 versus a couple-hundred-billion-dollar ecosystem 15 years later: markets that work can become vastly larger than expected. OpenAI and Anthropic can both win in a multi-model world, while cross-lab infrastructure such as agent evaluations may spawn “a dozen, two dozen, five dozen” new categories.

🔗 Original source & video: Are SaaS Companies Cooked: Which Thrive & Which Die | Aaron Levie

The enterprise AI adoption gap is governed less by model capability than by permissions, liability, and operational control. Startups have little to “blow up,” while a bank must contain prompt injection, accidental writes, conflicting agents, and information leakage before granting autonomy. The result: “the diffusion of AI capability is going to take longer than people in Silicon Valley realize.”

Software demand could be transformed if organizations deploy “a hundred or a thousand times more agents than people.” Aaron Levie argues that vendors must expose APIs, CLIs, tools, identity, and access controls because business performance will increasingly correlate with how effectively agents reach company data. Martin Casado’s caveat is that interface polish is not the moat: agents select backends based on semantics, cost, durability, and similar substance rather than interface or documentation quality.

Systems of record are far more defensible than the “SaaS apocalypse” framing implies. Steven Sinofsky calls it “just absurd to think you’re going to vibe-code your way to SAP,” because decades of domain knowledge reside across interfaces, middle tiers, workflows, and operating habits—not in one clean data layer. Agents may change consumption and monetization faster than they replace core systems.

AI initially raises the value of domain experts who can decompose work, then moves that skill into a higher abstraction layer. Most employees cannot produce a flowchart of their own job, making “algorithmic thinking” the immediate bottleneck; Casado’s Anthropic growth-marketer example showed one systems thinker automating work previously spread across five or 10 roles. Sinofsky expects the “rocket-science part” to evaporate as spreadsheet complexity once did.

Giving every agent a separate account does not make it equivalent to an employee. Agents can be given phone numbers, Gmail accounts, cards, and role-based permissions, but their owners retain liability and require complete oversight; anything entering a context window might still be extracted through prompt injection. For sensitive workflows such as an M&A data room, Sinofsky suggests the near-term enterprise state may remain read-only “for a number of years before N is very large.”

The panel rejects Wall Street’s fixed-revenue-pie assumptions and sees AI demand as structurally underestimated. Sinofsky says forecasts are “off by at least an order of magnitude,” invoking PCs, cloud, and CRM as markets where falling friction expanded consumption rather than merely reallocating spend. Casado adds that every one of the infrastructure companies he can observe has gone “asymptotic” over six months because far more software is being written.

Token spending is nevertheless an immediate earnings and management problem, even if efficiency eventually overwhelms scarcity. Engineering compute could plausibly range from 1% to 100% of relevant expense in today’s debate; with public-tech R&D at roughly 14%-30% of revenue, compute costing twice the engineering team versus being 3% more can determine EPS. Martin calls this “the most wild” budget conversation ahead, while Sinofsky predicts a transistor-like shift will make today’s token accounting disappear: “guaranteed.”

🔗 Original source & video: Box CEO on the AI Adoption Gap | The a16z Show

Box’s core thesis is that enterprises will have “10x or 100x” more agents than people, turning dormant corporate files into continuously useful infrastructure. Contracts, research, roadmaps, and customer material become inputs for onboarding, sales, and autonomous work rather than documents humans occasionally reopen. The pitch writes itself: “Every agent needs a Box.”

Agent identity and authorization, not raw model intelligence, may determine whether autonomous agents can enter regulated enterprises safely. Today’s “easy mode” makes the agent identical to its human operator; independent agents create harder questions about privacy, liability, oversight, and multi-party access. Levie expects “spectacularly crazy security incidents” unless permissions and governance become agent-native.

AI coding’s rapid adoption is a misleading benchmark for the rest of knowledge work because software development enjoys unusually favorable conditions. Code is largely text-in/text-out, engineers commonly access broad repositories, models are heavily trained on code, and the labs’ own developers supply continuous feedback. Bankers, lawyers, and other workers instead face fragmented permissions, undocumented context, mixed media, and information trapped in conversations.

Enterprises will have to redesign work around agents rather than wait for agents to assimilate into existing processes. “The agent didn’t really adapt to how we work. We basically adapted to how the agent works.” swyx challenged the consultant-friendly premise and cited OpenAI hiring FDEs and Anthropic embedding at Goldman Sachs as evidence that there is no effortless “come as you are” path. Levie agreed that reaching a well-organized data environment will be difficult and said the opposite extreme—an agent inferring everything from a totally messy environment—is technically impossible.

Context engineering is fundamentally a retrieval problem: perhaps 50 million pages of accessible information must be reduced to roughly 60,000 dependable tokens. Larger windows do not remove the need for search, ranking, access control, and judgment about when to stop looking. Better models can detect contradictory or stale documents, but “it still doesn’t work if you just have a total wasteland of data.”

Knowledge-work reliability requires private evals because plausible slop can create professional and legal exposure invisible in ordinary software output. Box’s held-out industry benchmark reportedly showed roughly a 15-point jump between model versions in one comparison, while internal tests catch regressions in both models and agent harnesses. Levie expects every enterprise eventually to maintain evals for workflows such as RFP creation, sales collateral, and invoice processing.

Box is positioning its governed file system as both an agent data layer and a sandboxed workspace, while organizing a roughly 3,000-person company around an existential agent transition. A core group of a few dozen is supported by search, metadata, infrastructure, security, and compliance teams. Beyond Box, swyx and Alessio argue that software output could increase by 10 to 100 times, making technical workers, deployment, and DevRel more important rather than less.

🔗 Original source & video: Why Every Agent Needs a Box — Aaron Levie, Box

Coding agents are giving tiny startups the operating scale once reserved for large companies. Levie’s company says roughly 30% of its code is “coming from AI,” while employees self-report gains ranging from 20–30% to 75%; founders of three-, five-, and 10-person startups claim 3–10x. The strongest teams dispatch tasks to background agents, get results in about 20 minutes, and are “in the business of doing code review, not code writing.” Sinofsky warns that dazzling output can feel productive without changing actual output.

The strongest observed gains come from experts and senior small teams, not from novices magically acquiring judgment. Casado says experienced AI-enabled teams are “superhuman,” as if “they woke up and they were all Tony Stark”; Levie says willingness to push AI further helps explain the wide productivity variance. Casado argues expertise lets users identify the perhaps 2% of outputs that are hallucinated or misdirected. AI is a “turbocharger” for domain knowledge, while professional taste remains the monetizable layer.

AI productivity may surface as velocity, software quality, and higher-level work rather than faster feature releases. Developers can generate documentation and tests while improving maintainability and architecture, even if the shipping calendar stays unchanged. Levie’s personal example compresses a three-day analyst loop into 10–20 minutes of deep research, analysis, and prototyping: “It’s just a fundamentally different thing” from assigning tasks serially.

The startup reset comes from combining agentic scale with distribution that already exists on 7 billion phones. Background agents neutralize the incumbent’s headcount advantage, while consumer familiarity eliminates much of the platform-distribution hurdle; Levie’s blunt framing is that incumbents retain “advantage in distribution, but that is it.” Twenty-year-olds who might once have been 10x engineers can behave like “100x engineers,” making the 2025 company-building process unrecognizable relative to 2005.

The biggest greenfield may be professional services, where AI packages domain intelligence into software without a traditional software incumbent to displace. Agriculture, construction, systems integration, and advertising can be rebuilt AI-native—and the firms nominally being disrupted may become the product’s primary customers. Levie’s agency example captures the pricing wedge: if AI can produce a $1 million ad-video campaign for $5,000, a new entrant can charge somewhere between those figures.

Incumbents do not have to disappear for insurgents to capture enormous new categories. The panel expects existing systems of record with obvious agentic workflows to favor incumbents at the margin, while new fields favor disruptors; both can grow because markets may be “a hundred times larger” than previously understood. A Microsoft worth “$4 trillion” can coexist with new $10B, $20B, $50B, and $100B companies—the durable incumbent weakness is refusing products that conflict with the existing business model.

Consumer adoption is laying the groundwork for a forced enterprise upgrade cycle. A self-reported survey put repeated weekly AI usage at up to 75% of adults, versus roughly half of Americans owning computers in a 1999 Pew study. Levie’s nontechnical teacher sister said she was asking ChatGPT questions. Employees and graduates will increasingly ask why enterprise systems cannot answer questions—or turn around reports—as quickly as their consumer tools, while security and nondeterministic outputs remain the large-company bottleneck.

The immigration debate focused on reducing lottery friction and protecting wages, but not on agreement over a $100K price. Casado initially favored pricing to allocate scarce supply; Sinofsky said a high price could directly target consulting body shops. Levie argued $100K could favor Amazon and Google and exclude valuable startup hires; Keith Rabois’s suggested $20K and a minimum-salary rule emerged as alternatives. The sharpest objection was that the proposal remained “$100K to participate in the lottery system,” rather than replacing costly uncertainty with a system optimized for “the absolute best in the world” and net-positive wages.

🔗 Original source & video: Software Finally Eats Services - Aaron Levie

The AI-worker end state is autonomous background execution, not a better chat interface. Aaron Levie measures agency by how much useful work completes without intervention, while Martin Casado adds a stricter test: the system must consume its own output and continue sensibly. Because long-running autonomy can compound errors, the practical architecture is likely many bounded workers with human checkpoints—Steven Sinofsky’s “ampersand in Linux,” upgraded from “really bad interns.”

The emerging architecture looks less like monolithic AGI and more like specialized agents coordinated around a human. Erik Torenberg frames the split between deep task expertise and orchestration; Levie says he has yet to see a high-performing system without a human somewhere in the loop. For investors, that shifts attention from a universal intelligence claim toward workflow depth, orchestration, and whether “the economics pencil out.”

Dated AGI forecasts and “recursive self-improvement” reveal less than their precision suggests. Sinofsky expects a 2027 target to become a dispute over definitions—“OKRs for an industry”—because exponential progress is real but ten-year outcomes remain unpredictable. His technical objection is sharper: a feedback loop may converge, diverge, or asymptote, so recursive improvement alone “says almost nothing.”

AI currently compounds expertise more reliably than it replaces it. Enterprises have both better models and a healthier culture of verification; the relevant metric is review time versus doing the task manually. Expert engineers accept a “slot machine” because they can identify good output and still get “10x productivity,” while novices may deploy the losing pulls without recognizing them.

Prompts are becoming longer and more specialized because human intent cannot simply be inferred away. Levie says output remains correlated with input and sees “pages long” prompts outperforming vague instructions; Sinofsky explains that formal languages arose because experts needed efficient precision, while Levie calls jargon a formalized way for domain experts to communicate. The counter-AGI pattern is “more agents, not less, doing more narrow tasks.”

AI will redesign work by exposing which steps are genuinely sequential and which were serialized only by scarce human attention. A developer may manage agents by GitHub pull request, a lawyer may supervise 20 case workers, and an events lead may launch venue, invitation, and collateral tasks in parallel. The organizational implication is that humans become managers of agents, with workflows rebuilt for the tool.

The application opportunity expands as AI becomes domain-specific, data-bound, and economically selective. The panel expects vertical agents across departments and industries, arguing that model providers cannot out-execute “50 startups across 50 different domains”; post-training, reinforcement learning, proprietary data, permissions, and workflow ownership become the moat. Casado’s unit-economics filter matters: for many applications, “20% of the inferences are 80% of the cost,” so product value lies in choosing the costly, domain-specific calls worth making.

🔗 Original source & video: Aaron Levie and Steven Sinofsky on the AI-Worker Future

Enterprise AI is a change-management race, not a model-deployment sprint. ChatGPT reached consumers because the interface required “2 seconds to learn,” while enterprises still face legacy data, governance, liability, compliance, and decades-old workflows. Yet Levie sees roughly “five times” the early-cloud buy-in: leaders already assume AI will take over and believe “it needs to happen to us faster than it happens to our competitors.”

Agents initially look more like a sustaining expansion for SaaS incumbents than a full-stack replacement. API-first products let agents become “super users” of ServiceNow, Workday, and similar systems, growing usage where no human seat previously existed. The pressure point is economics: seat-plus-consumption pricing works, but “if the human literally is not a seat on the system,” recurring-license models face a genuine crisis.

The largest startup opportunity may be software spend created inside historically service-heavy, unstructured industries. Levie expects legal, healthcare, education, consulting, investment banking, and wealth management to become addressable because agents can finally work with ad hoc documents and language. His deliberately rough example: a legal-document market once below roughly $2 billion could become “many, many billions to double-digit billions.”

AI budgets can be absorbed from the enormous knowledge-work cost base without an immediate software-budget bloodbath. Against a new engineer costing roughly $125,000-$200,000, even $1,000-$2,000 of aggressive annual Cursor usage is around 1% of salary and can disappear inside attrition, delayed hiring, or annual compensation adjustments. Levie’s rough model puts US knowledge-worker spend in the many trillions; redirecting only a few percent could double enterprise-software expenditure.

The emerging job is to orchestrate, review, and audit agents rather than operate a computer one action at a time. Once typing emails, writing code, or producing marketing assets stops rate-limiting output, individual contributors may become “managers of agents.” The inversion matters: instead of AI correcting human work, “the human’s job is to fix the AI errors,” with expertise becoming more valuable as generated volume rises.

AI coding expands capability without making software engineering—or packaged software—disappear. Casado’s updated view is that AI benefits stronger developers most, while formal languages remain important because they provide the precision needed to formally describe software. Levie likewise rejects total homebrew software: vertical SaaS retains domain knowledge and operational defaults, even as vibe coding drives perhaps “10x growth” in prototypes, scripts, and long-tail internal tools.

The long-run outcome may feel anticlimactic precisely because productivity becomes normal. Companies will run dozens of agent-generated experiments in the time one campaign takes today; competitors may absorb much of the measured growth, while users receive better products, healthcare, and scientific discovery. Levie calls himself “98th percentile optimistic”: five to ten years from now, today’s two-week workflows may simply look incomprehensibly slow.

🔗 Original source & video: Aaron Levie on AI’s Enterprise Adoption

The panel broadly credited Trump’s first 100 days with sealing the border and pursuing a “reprivatization” of the economy, while treating execution volatility as the central risk. Chamath graded the period B+, Jason B versus a C- for Biden, and Sacks called the border result an A+; Aaron Levie’s bright spot was an unmistakably pro-innovation, pro-open-source AI posture. Chamath also cited committed foreign investment approaching or exceeding $1 trillion, while Sacks distilled the border claim as: “We didn’t need a new law; we just needed a new president.”

China–US trade has already suffered a demand shock, with Flexport seeing ocean-freight bookings fall 60%. Ryan Petersen said China’s initially announced 54% tariff escalated to 154%, while later describing the bear case around a 145% China rate; goods departing after midnight ET on April 9 now incur the duty upon arrival. He nevertheless rejected a point-of-no-return framing: “Don’t judge the cook while he’s cooking.”

The tariff bear case runs through small-business solvency rather than merely higher consumer prices. Petersen argued that companies remaining in China are buying its manufacturing quality and ecosystem, not cheap labor; businesses able to move had already received a powerful incentive from the prior 25% tariffs. Apparel founders were discussing layoffs within two to four weeks, and David Friedberg said layoffs had begun, although Petersen still expected the administration to avert the bleakest outcome.

The central policy dispute was whether strategic decoupling requires economic shock or could be achieved through predictable incentives. Levie advocated a 5% tax rate for building in America, immediate expensing, deregulation, automation and only surgical tariffs, warning against “chaos monkey[ing] the economy.” Chamath and Sacks countered that disruption finally exposed dangerous dependencies in batteries, AI, pharmaceutical APIs and rare earths—and that real-time correction may be the only feasible way to change a system this complex.

Amazon’s aborted tariff disclosure exposed a deeper marketplace-enforcement gap. Jason wanted retailers to itemize import charges and steer customers toward American goods; Sacks saw Trump’s intervention as “whack-a-mole” that could not protect hundreds of other retailers. Jason said he thought roughly 60% of Amazon sellers were Chinese-registered companies without US registration, creating opportunities to understate values, misclassify goods and evade meaningful product-safety enforcement.

AI agents expand software’s addressable market from employee seats into labor and previously unaffordable work. Jason cited prospective OpenAI agent pricing of $2,000–$20,000 monthly and a venture workflow that could compress roughly 5,000 hours spent processing 20,000 applications; Flexport already uses AI to call thousands of drivers drawn from 400,000 app users. Levie’s call was that perhaps 90% of future AI usage will perform work “that we don’t do today,” with only 10% replacing existing activity.

Enterprise adoption will be gated by error economics even as algorithms, chips and data centers compound rapidly. David Friedberg’s best single-pass test—500 documents and 40 requested fields—scored about 90%, inadequate for many regulated workflows without reruns, chunking and tuned reasoning. Sacks projected 3–4x annual progress across algorithms, hardware and deployed compute, while Chamath argued that probabilistic software makes “quality assurance and QA…now the only thing that matters.”

🔗 Original source & video: Trump’s First 100 Days, Tariffs Impact Trade, AI Agents, Amazon Backs Down

Enterprise AI demand is running far ahead of cloud-era enthusiasm, but production remains in “very early innings.” Cloud required reluctant companies to surrender physical infrastructure and trust unfamiliar vendors; AI instead has executives proposing “almost as many use cases as possible,” sometimes more than practical. Levie sees a relatively narrow window in which large technology companies, enterprise-software vendors, and new agent startups can capture that demand.

AI changes IT from a software-enablement function into an operator of digital labor. Business units will ask IT not merely to deploy CRM or HR systems, but to provision agents that run sales campaigns, review contracts and invoices, or execute onboarding. Borrowing Jensen at NVIDIA’s framing, “the IT department becomes the HR department of AI,” requiring much deeper business knowledge and strategic authority.

Box’s RAG advantage came from architecture it built before ChatGPT, partly through luck. Box Hubs lets users curate authoritative documents without copying or changing permissions, reducing the risk that draft-heavy corporate repositories contaminate retrieval when users query a bounded hub. Because enterprises lack the public web’s PageRank-like authority signals, Levie argues this human curation makes Box’s RAG “at least a hundred times better” than indiscriminate search across all company data.

The strategic destination is a new “system of intelligence” that combines structured control with probabilistic judgment. Box’s current agents include model access, tools, skills, instructions, and enterprise data, though Levie concedes many would have been called assistants two years ago. The larger prize is agents that review content, make context-dependent decisions, route work, and coordinate with Salesforce, ServiceNow, Microsoft, or humans.

Being human-equivalent or 40% cheaper is insufficient to overcome enterprise adoption friction. A buyer still faces 17 competing projects, AI-council approval, testing that might take six months, privacy and security concerns, and workforce transition; consequential workflows may also demand “99.99999% reliability,” not 98%. Levie’s commercial threshold is an order-of-magnitude gain in cost, quality, or capability, especially for work that was never automated before.

Incumbents should retain their natural application domains, while startups win in cross-platform or previously unserved workflows. An “AI-first CRM” must assume Salesforce becomes AI-first too, just as Workday and ServiceNow will defend their domains; thin layers over an incumbent or OpenAI are therefore vulnerable. Independent compliance, agent red-teaming, cross-application workflows, and products requiring substantial non-AI interfaces remain credible startup territory.

AI may reopen SaaS pricing while improving productivity before the aggregate statistics clearly register it. Levie expects outcome pricing, compute consumption, and subscriptions all to coexist after two decades dominated by per-seat SaaS; Box itself is still testing. Internally, coding-tool gains range from 5–10% for some engineers to possibly 50% for a new hire, but Levie would take the over on economy-wide productivity forecasts if the clock starts in a few years.

🔗 Original source & video: Aaron Levie, CEO of Box, on Box AI, Enterprise Enthusiasm, and the Evolution of SaaS