🎯Empresas estão fazendo 'Shark Tank' pra decidir quem ganha tokens de IA
Aaron Levie, CEO do Box, passou a semana conversando com dezenas de líderes de TI e IA de grandes empresas. O retrato que ele trouxe é revelador: estamos saindo da era do chat (perguntar coisas à IA) para a era dos agentes - programas que usam ferramentas, processam dados e executam trabalho real. --- Mas o ponto mais surpreendente: empresas estão fazendo orçamento de tokens como capital de risco. Os custos operacionais de rodar IA não cabem no orçamento normal. Uma empresa criou literalmente um formato estilo "Shark Tank" onde áreas internas disputam quem ganha mais poder computacional. Outras tentam criar uma "hierarquia de necessidades" para racionar o uso de IA entre os departamentos. --- Outro detalhe importante: quase ninguém está falando em cortar vagas por causa de agentes. Os principais casos de uso são coisas que a empresa não conseguia fazer antes - modernizar sistemas legados, processar montanhas de documentos, automatizar processos que travavam outros times. Mais grana entrando do que custo saindo. E uma observação certeira de Levie: apesar de o Vale do Silício achar que a IA simplificou tudo, na vida real, skills, MCP e CLIs ainda são conceitos obscuros que precisam de gente técnica pra funcionar. Engenheiros não vão ficar sem emprego - só vão mudar o que fazem.
Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise. Some quick takeaways: * Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow. * Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated. * Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs). * Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these. * Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs. * Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy. * Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems. * Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been. One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise. This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.
— @levie View on X
Grandes corporações estão institucionalizando processos competitivos de alocação de recursos computacionais para inteligência artificial. Em uma das empresas consultadas pelo CEO do Box, Aaron Levie, áreas de negócio apresentam pitches internos no formato "Shark Tank" para disputar orçamento de tokens — evidência de que a IA deixou de ser experimento de laboratório para tornar-se linha crítica de infraestrutura com restrições orçamentárias reais.
Da conversa para a execução
O mercado empresarial está migrando da era do chat — onde usuários fazem perguntas a modelos — para a era dos agentes autônomos. Esses sistemas não apenas geram respostas, mas utilizam ferramentas, processam dados estruturados e executam tarefas em workflows operacionais. A mudança exige, contudo, uma reengenharia profunda: a maioria dos fluxos de trabalho corporativos não foi desenhada para receber agentes diretamente, demandando esforços massivos de change management e coordenação entre unidades de negócio.
O problema do "Tokenmaxxing"
Com orçamentos operacionais (OpEx) travados anualmente, empresas enfrentam trade-offs concretos sobre como distribuir capacidade de processamento. Além do modelo "Shark Tank", outras organizações estruturam hierarquias de necessidades para racionar compute internamente. Isso reflete uma maturidade técnica: gestores agora precisam justificar ROI não apenas de implementação, mas de consumo contínuo de tokens.
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