Aaron Levie

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Aaron Levie

Aaron Levie

@levie

ceo @box - your business lives in content. unleash it with AI

Bay Area Katılım Mart 2007
787 Takip Edilen2.7M Takipçiler
Aaron Levie
Aaron Levie@levie·
We’re in a period where everything feels like it’s getting jumbled up across roles because AI lets you explore the adjacencies of other functions more easily. We all collectively have to figure out the new form of definition of what these jobs look like in a world of agents, and certainly many will look different from what they did before. But there are some immutable laws that will eventually re-emerge over time and become clear again. As an example, when you’re scaling, product managers should be spending an insane amount of time with customers and getting feedback on the product and thinking through what to do build next, how to design it so it’s usable, and so on. Engineers should be understanding the business objectives, and building systems that scale and are secure, even as feature velocity increases by 10X. Now both can do a bit more of the others role, and this can temporarily get conflated as doing the whole thing, but eventually the work adds up to be enough that it makes sense to specialize again. Similarly, in GTM, the product marketer can certainly generate a working design and video for a launch, but the specialist is always going to (or should) have an eye for quality that delivers a better outcome. My bet is that AI enhances specialization even further, even if a few roles collapse into each other, and the future toolchain and craft of the specialist will be much higher leverage and output far greater than anyone else as a hobbyist in that function.
Lenny Rachitsky@lennysan

Engineers don't write code. PMs are shipping to production. The design process is dead (there's no time). Marketing can ship their own campaigns. SDRs are being replaced by AI. Everyone's a data scientist now. What a time to be alive.

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Aaron Levie
Aaron Levie@levie·
He just spent a year building scaffolding for his agent harness. Now release a new model update that makes all of it obsolete.
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Eric Vishria
Eric Vishria@ericvishria·
I’m so fucking proud of this team. They took an extraordinarily difficult technical swing with wafer-scale and connected on the first try. Then they spent years grinding through packaging, cooling, compilers, frameworks, early customers, and everything else required to turn a technical breakthrough into a real company — swinging and missing and learning and trying again. Most importantly, they stayed clear-eyed about what they had (a technical marvel) and what they didn’t (enough advantage in training), saw the opportunity emerging in inference, and adapted. That kind of persistence — not to be confused with stubbornness — is incredibly hard to describe, but absolutely essential in the unstable substrate of AI. The requirements of AI today will not be the requirements of AI tomorrow. But this team will keep figuring it out. And I’m here for it.
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Aaron Levie
Aaron Levie@levie·
If I were a college career counselor or in career services, I’d quickly be figuring out how to get students to understand these forward deployed engineer jobs exist and how to get them. The requirements are a mix of deep technical skills, often CS majors or minors. You must be great at understanding problem solving, how to have systems thinking, and have a strong business acumen. The kicker, of course, is to make sure you’re very deep in AI agents; you need to have fluency in coding agents, MCP, CLIs, Skills, and so on. Hundreds (thousands?) of technology companies will be hiring for these roles, same with any consulting and IT services company, and the vast major of mid-size and large enterprises will be hiring for this talent internally as well. One great example of opportunity for highly technical talent out there.
nader dabit@dabit3

Forward Deployed Engineer is the hottest, and one of the most in-demand, jobs right now. Every major AI company is hiring including companies like @OpenAI @cognition @AnthropicAI and @Google If you possess a combination of soft skills (good communication), have an engineering background, and are up to speed on the latest and greatest in agentic coding you're probably able to land one of them. They pay well and offer a foot in the door to some of the fastest growing companies in the world.

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Aaron Levie
Aaron Levie@levie·
Forward deployed engineers, or equivalent, are about to become one of the most in-demand jobs in tech. And one of the most important functions for AI rollouts. Deploying agents is far more technical of a task than most people realize, often far more involved than deploying software. Software generally works the same way every time, and generally for the past few decades has been updated versions of an existing technology or concept (which basically means easier for the enterprise to update their workflows on a newer system). With agents, you’re actually deploying the equivalent of work output within the enterprise. The customer is effectively using you as a professional services provider for a task, which they expect to get solved nearly end-to-end now. This means you need to actually deeply understand the business process as a vendor, and get the customer from the current to the end state seamlessly. Companies need help figuring out which models will work best for their workflows, they need extensive evals setup often, they need change management support for workflows, they need to get their data setup for the agents, and constant tuning of the agentic system for their process. Massive role in tech now. And another example of the kind of highly technical work that AI is creating.
First Squawk@FirstSquawk

GOOGLE TO RECRUIT HUNDREDS OF ENGINEERS TO ASSIST CLIENTS IN EMBRACING ITS AI – THE INFORMATION

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Aaron Levie
Aaron Levie@levie·
Agents are quickly moving from coding to the rest of knowledge work. But to do this we need ways of bridging the advanced capabilities of the AI models with the real-life workflows in the enterprise, by industry and line of business. The models will remain general purpose, but we’ll move to ways of aligning agents to the unique work that gets done in legal, financial services, insurance, healthcare, life sciences, and more. Each industry has its own set of workflows, domain specific context, and data sources that agents need to have access to and be familiar with. Claude just launched an updated set of plugins and skills for the legal industry, including Box. You can now take any of your enterprise contracts or documents and securely work with them in a headless fashion via Claude in a legal workflow. This is just the start of what industry-specific adoption of AI will look like, and equally shows what the future of headless software interaction will look like in the future.
Polymarket@Polymarket

JUST IN: Anthropic rolls out new Claude tools aimed at automating legal work for lawyers & law firms.

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Aaron Levie
Aaron Levie@levie·
@bpizzacalla @staysaasy I think you can automate *a lot* (e.g. account plans, renewal notices, etc.) but you’re not going to automate meeting the 23 different groups at JPMorgan that all are potential buyers of your product.
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Brandon Pizzacalla
Brandon Pizzacalla@bpizzacalla·
@levie @staysaasy We stopped trying to hire reps who could manage all those relationships perfectly and built an agent that does it instead. Every account gets the same playbook. First time we ran it we caught three renewals that had gone completely dark and the reps hadn't noticed.
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staysaasy
staysaasy@staysaasy·
Dumbest mistake I’ve seem many smart people make is thinking they can replace somebody else’s product with 1/10th the team size. Almost never, ever happens. You can redefine a category with less people. But people ALWAYS overestimate their ability to win a footrace against much more resourcing (no matter how big and slow they seem).
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Thariq
Thariq@trq212·
HTML is the new markdown. I've stopped writing markdown files for almost everything and switched to using Claude Code to generate HTML for me. This is why.
Thariq@trq212

x.com/i/article/2052…

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Timothy Wang
Timothy Wang@timwangyc·
Introducing Ponder: the agentic video editor. It’s a new paradigm for filmmaking, where powerful creative agents and humans collaborate to tell world-class stories. We're also announcing our $2.5M pre-seed, led by Liu Jiang from Sunflower (@seedtosunflower), with @Joshuabrowder and @MattHartman. Joined by @levie (Box), @emerywells (Frame), @JaredLeto, @CommaCapital, the @nyuniversity venture fund, @cory, @darian314, @shiffman, and many more incredible founders, investors, and creators.
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Aaron Levie
Aaron Levie@levie·
The need and opportunity for professional services and FDEs to deploy agents right now is massive. Every tech wave offers a new era of consulting and tech services requirements. Moving from analog to digital led to a massive wave in the 90s. Moving from on-prem to cloud did the same in the 2000s. But this is going to be at a scale far greater than the others. The reason is that agents fundamentally change the underlying workflows of an organization. Unlike most prior eras of technology, where it was a change in medium of the service being delivered (on-prem CRM to cloud CRM), agents rewire the business process itself. And unlike upgrading a tech system, business processes are full of idiosyncrasies. Every industry will have its own variants, and every department within those industries will have variants as well. Not to mention the bespoke difference between firms. Bringing agents to marketing in CPG will look different from marketing in healthcare. Bringing agents to sales in a B2B software company will look different from a car dealership. And none of the change is easy technically. You need to first modernize your infrastructure and data and make sure it’s ready for agents; access controls, entitlements, and permissions need to be mapped in a way that works for agents and people; you need to make sure agents have the right context to work with; you need to consistently eval and maintain the agents when there are model upgrades; and you need to drive the change management of the process itself to figure out which parts the people do and what agents do. That’s an insane amount of technical and domain-specific process work to be done to make this all happen. Huge opportunity for new service providers, as well as internally teams and roles to emerge, to help drive this change.
OpenAI@OpenAI

Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. openai.com/index/openai-l…

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Aaron Levie
Aaron Levie@levie·
YouTube and SoundCloud should have done the same in music; instead, it simply expanded the total amount of talent that could eventually be discovered, and ultimately channeled into Hollywood who had all the infrastructure and distribution to produce and commercialize the content. Same will happen for movies as well even as the cost of production goes down.
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scott belsky
scott belsky@scottbelsky·
@cryptopunk7213 new tech is exciting, but I think this is like suggesting, if you give everyone a basketball they could be in the nba. tools don’t make world class stories, people do. and exceptional original IP is a deeply human/scarce asset that has nothing to do with the ubiquity of tools
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Ejaaz
Ejaaz@cryptopunk7213·
officially calling it (again): hollywood is cooked in 2 years (probably less) the quality of AI-animation has equalled/ surpassed) studio quality movies. this took 7 iterations to make costing 100x less. for 99% of people this looks indistinguishable to pixar. many people are coping but it’s true. and it’s not just animation: > the same quality you see here will eventually reach parity to human-acted movies. we’re 1-2 model generations away > this will blow up the hollywood studio structure. IP will be created and accessible to everyone. the next hit director is probably sitting in their underwear in mums basement right now.
Marko Slavnic@Markoslavnic

The quality of animation you can create on your own is truly amazing. We really are just limited by our imaginations at this point. Go tell your story! Made in @runwayml in a few hours and a handful of gens.

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Aaron Levie
Aaron Levie@levie·
As advanced agents move from coding to the rest of knowledge work, it takes a real amount of work and know-how to get right. You need to ensure agents have the right context and data to work with, wire up systems to agents in a safe and secure way, ensure that the agents are producing quality output, design the end-state workflow where and how humans will be in the loop, maintain the agents when there are model and system upgrades, and more. This isn’t a side project or something you can just do on nights and weekends. You need to design and develop robust agents that will be used in mission critical workflows. It’s a highly technical job, very much akin to a forward deployed engineer for internal functions. This is why, at Box, we’re starting to hire for AI automation engineering roles. This a technical role that will partner with the business directly and help augment how they work to drive even more output, and deliver better experiences for employees and ultimately customers. This is just one example of the kind of role that AI will start to open up in the future. I expect most companies will have many flavors of this going forward.
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Aaron Levie
Aaron Levie@levie·
This is a great list, but I’d say the vast majority of software spend in an enterprise meets the criteria of stuff not to build yourself. Security, compliance, data integrity, feature updates, network effects, employee learning curves, opportunity costs, external integrations, maintainability, and about 10 other reasons means you won’t ultimately get *that* far rolling your own software. Fun for first day, annoying a year later.
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Aaron Levie
Aaron Levie@levie·
For everything we’ve seen about agents so far, it’s clear that they will make it far easier for people to get into previously extremely complicated fields. That will most certainly mean far more people will build software, explore creative work, research spaces they couldn’t do before, and so on. Yet, equally, we’ve seen that people with experience in every one of those fields have a huge edge with the right judgment and historical context to leverage these tools in ways that exceed the output of the novices (if they choose to). They know when the agents are making catastrophic mistakes, can give the agents the right context to do the job better than they otherwise would have, and so on. The combination of these two facts essentially means that we will continue to get the same lift as we’ve seen in any other technological revolution. More democratization, but similarly greater output from the experts. This then makes the experts continue to be in higher demand because over time our expectation for what we can get out of any field will just go up. This is going to be true in essentially every important field. You’ll trust a lawyer using an agent for legal advice over someone who’s never had to experience how well a contract holds up. You’ll trust an engineer developing and running software over someone who’s never seen a production system. You’ll rely on the important instincts of a designer using agents over the average prompter. The quality and volume of output we expect from these functions will certainly go up meaningfully, but the person with experience will always have a leg up, which is why the jobs don’t go away.
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Aaron Levie
Aaron Levie@levie·
@MichaelThiessen Yes. Cost optimization will become a major factor in harness engineering and will likely favor agents optimized for vertical and domain specific tasks as they can be tuned for certain use-cases.
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Michael Thiessen
Michael Thiessen@MichaelThiessen·
Harness engineering can also help you save tokens while getting the same or better output
Aaron Levie@levie

A common trend emerging in larger enterprises is token budgeting as a major topic. As agents can do more and more long running tasks, and thus take vastly more compute, allocation of tokens across teams becomes a very real thing in the enterprise. Companies spend a meaningful amount of time deciding how much to spend on talent, marketing campaigns, events, laptop setups, and even the cost of lunches. Tokens will be no different. Tokens will similarly need to be excruciatingly well-managed because you’ll need to ensure you don’t blow up your budget, and you’ll need to ensure that the tokens are flowing to the highest and most useful parts of work. You don’t want to find out you burned your monthly budget on something relatively low value and then be blocked on the much higher value task later. Doing this at large company scale is extremely hard as you have layers of abstraction on data and visibility into the digital work being done by agents in any central way. This is going to mean that agentic spend will increasingly will expand beyond the confines of the IT budget, and end up in organizational budgets like other expenses. Ultimately team and org leaders will have to be given budgets for this, but even they don’t have adequate visibility and controls in most cases. We’ll need all new software just to solve this problem, and it’s probably an opportunity for startups in its own right. Going to be an all new era of enterprise resource allocation, especially while we compute constrained.

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Aaron Levie
Aaron Levie@levie·
A common trend emerging in larger enterprises is token budgeting as a major topic. As agents can do more and more long running tasks, and thus take vastly more compute, allocation of tokens across teams becomes a very real thing in the enterprise. Companies spend a meaningful amount of time deciding how much to spend on talent, marketing campaigns, events, laptop setups, and even the cost of lunches. Tokens will be no different. Tokens will similarly need to be excruciatingly well-managed because you’ll need to ensure you don’t blow up your budget, and you’ll need to ensure that the tokens are flowing to the highest and most useful parts of work. You don’t want to find out you burned your monthly budget on something relatively low value and then be blocked on the much higher value task later. Doing this at large company scale is extremely hard as you have layers of abstraction on data and visibility into the digital work being done by agents in any central way. This is going to mean that agentic spend will increasingly will expand beyond the confines of the IT budget, and end up in organizational budgets like other expenses. Ultimately team and org leaders will have to be given budgets for this, but even they don’t have adequate visibility and controls in most cases. We’ll need all new software just to solve this problem, and it’s probably an opportunity for startups in its own right. Going to be an all new era of enterprise resource allocation, especially while we compute constrained.
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Aaron Levie
Aaron Levie@levie·
When AI makes one thing easy to do, it’s always good to assume that will equally be the case for everyone else. If it’s the case for everyone else, then that means competitive forces will ensure that resources move to new or other areas that create differentiation. If AI makes building software easier, then there will be a relative increase in resources going into sales, marketing, and customer success, because standing out or going deeper with customers else becomes even more important. This will also apply to lots of other areas of work. If you automate getting financial advice and insights, then the differentiation is in client engagement. And on and on. Just ask yourself: if everyone else does exactly what I do with this technology, how will I stand out from everyone else? That’s what happens next.
Gergely Orosz@GergelyOrosz

Starting to REALLY see how reaching potential customers is becoming a massive pain point for software startups - esp w AI! I get so much more messages about software that founders built rapidly that they think will solve some important problem (usually eg AI+context/trust/security). But how will anyone know about it? It was fast to build, but getting the world to know about it / care about it is increasingly hard/expensive/time-consuming. And the irony is: the "easier" it is to build, the more the only differentiation is marketing/advertising! (Because the easier it is to build, the more teams build something similar in parallel, and racing to win the market becomes key!)

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Aaron Levie
Aaron Levie@levie·
SpaceX as a vertically integrated AI compute company makes an insane amount of sense.
Claude@claudeai

We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity. This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.

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Aaron Levie
Aaron Levie@levie·
Both Anthropic and OpenAI have new initiatives to help enterprises deploy AI agents within their organizations. This is a trend that’s early but going to get very big fast. As agents enter knowledge work beyond coding, there is very real work to upgrade IT systems, get agents the context they need, modernize the workflows to work with agents, figure out the human-agent relationship in the workflow, drive adoption and do change management, and much more. While AI models have an incredible amount of capability packed into them, there’s no shortcut to getting that intelligence applied to a business process in a stable way. This is creating tons of opportunities across the market for new jobs and firms, and the labs are equally recognizing the criticality here.
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