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Innovation and AI at AB InBev

São Paulo
Joined March 2020
Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)Photo by feliperibbe (@RibbeFelipe)
Se você gosta ou estuda inovação, segue um fio com alguns textos que escrevi sobre o assunto, tanto no esporte quanto no setor corporativo em geral. 1. Por que toda organização esportiva deve ter um profissional de inovação? medium.com/renova-inova/por-…
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Lembro de ir alugar fitas VHS para assistir aos primeiros UFCs e escondia dos meus pais a contracapa, porque geralmente colocavam imagens dos lutadores bastante machucados para chamar a atenção. Bons tempos!
Nov12.1993 33 years ago today, A skinny Brazilian kid named Royce Gracie changed martial arts forever.
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Vendo o que está acontecendo pelo mundo no momento, me pergunto se estamos preparados do lado de cá e acho que a resposta é não.
Várias infraestruturas críticas brasileiras atacadas digitalmente em poucos dias: Caixa, Porto de Santos, Tesouro Nacional, Petrobras. E nenhum anúncio de política pública ou campanha sobre o tema, como se fosse "casos isolados".
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feliperibbe retweeted
What if your situationship turned out to be an AI agent!? I let 5 frontier models text real people on a dating app for 6 days. - 1,277 conversations - 510 agreed to meet - 355 sent their number - only 8 asked if it was a bot What gave them away, and why it matters🧵
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Há uma demanda enorme em grandes empresas por AI Builders, pessoas que sabem operar com agentes, construir novos produtos, criar automações, entendem limitações, são curiosas e hands-on... E a oferta ainda é baixíssima, especialmente em não engenheiros. Aproveite e mude sua vida.
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Não importa sua área, principalmente se você trabalha nos chamados knowledge work, ou seja, aqueles trabalhos de escritório, que acontecem basicamente em computadores, tem MUITO espaço para você se especializar e agregar muito valor, tornando-se um profissional indispensável.
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Domine a tecnologia antes que ela domine você. E não estou falando de algo complexo, nem nada, só o fato de você saber usar ferramentas como Codex, Claude Code e afins já te coloca no topo da pirâmide, não só no Brasil, mas no mundo.
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What if we could manipulate UI at the speed of thought? Built this voice coffee ordering system with @typesafeai Jev. The drink and any updates show up while I’m still mid-sentence, ~200 ms from word to screen. Feels pretty magical!
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One of the most significant things about Gemini 4 Carbon is that it is showing that Google is back in the race of frontier models, rapid releases and rapid improvement.
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Ataque hacker ao Porto de Santos dificulta operações desde sexta passada. Esse é o "novo normal". Com escrevi na Folha no artigo sobre o fim da inocência em cibersegurança, os sistemas brasileiros tem muitas vulnerabilidades. Melhor nos prepararmos. c-level.folha.uol.com.br/inf…
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AI sentiment mostly explained with four factors people like ai more when income per person and individualism is lower & manufacturing share of gdp and savings rate is higher
AI sentiment by country. Notice anything?
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Man we are so close to real-time AI avatars that are completely indistinguishable from humans. Insane progress in this space over the last few months around latency, body language, natural-sounding speech. And @nuance_ai launched one today that's actually fun to talk to!
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A moment of appreciation for DeepSeek and OSS models. 44x cheaper than Claude Fable 5.2 per task. Reminder: 95% of your tasks don't need the smartest model.
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How soon should we see AI's impact in productivity statistics? With the PC, it took 14 years for aggregate productivity growth to show up. Economists didn't see it in their analysis for another five years. But work by @erikbryn and others showed individual firms seeing results by 1987 (although that work was published 9 years later.) With genAI, individual companies will report results well before we can see aggregate impact. And our data (and others) show that some firms are reporting exactly those early results. It shouldn't surprise people if today, four years after ChatGPT and three years after general availability of enterprise GenAI services from Microsoft and Google, we can't be confident that the strong productivity growth numbers released are really driven by GenAI. It's just too soon for big companies to show huge results and too soon for AI-native startups to be big enough to matter. If AI is more like electricity than the PC, and requires wholesale redesign of business operations, it might take even longer to show up in aggregate statistics. But this doesn't mean the corporate spending on AI won't increase as firms figure out how to get better results. These firms will face their own individual J-curves, where their total investment runs ahead of results, even as they become successful. (The reason is that once you figure out how to succeed in one domain, you'll apply that playbook to several others, all of which will need investment before payback). If AI is really boosting productivity, I reckon aggregate numbers might start to persuasively (but not unimpeachably) show that by 2028.
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In other words, public benchmarks should only be used for the top of funnel of decision making, “should I consider this new model?” And then private evals should be used for the rest “Is this model actually good for my workflow or product to make a business impact?” Evals are time consuming so sometimes not feasible, but right now too many people using benchmarks for the whole funnel and as a singular point of decision making
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👇 – at São Caetano do Sul, Brasil
JOE ROGAN: The computing power on your phone today. can you beat your phone at the highest level? MAGNUS CARLSEN: No. No chance. JOE ROGAN: That’s crazy. Wasn’t Deep Blue basically a whole stack of computers? And your phone is probably more powerful than that today. MAGNUS CARLSEN: It’s shocking. I have no chance against my phone. JOE ROGAN: So how do you even play against computers? MAGNUS CARLSEN: I rarely play against engines because they just make me feel stupid and useless. I think of them more as a tool. JOE ROGAN: But have computers actually changed how you understand chess? MAGNUS CARLSEN: Absolutely. Neural networks have improved our understanding of the game immensely. JOE ROGAN: What did you learn from them that humans weren’t already doing? MAGNUS CARLSEN: One big thing was long-term king safety. Humans used to push pawns mainly as an attacking tool. But the engines showed that you could push a pawn and create a weakness 20 or 30 moves later that would make the opponent’s king less safe. JOE ROGAN: So the computer basically showed you ideas that humans weren’t seeing. MAGNUS CARLSEN: Exactly. There was a period where you could clearly see which players were using these new neural-network ideas and which weren’t. JOE ROGAN: And you were one of the guys who figured it out early? MAGNUS CARLSEN: Yeah. My coach got into it very quickly, and we gained an advantage over almost everybody. JOE ROGAN: That’s insane. The machines aren’t just better at calculating, they’re actually teaching the best players how to think differently. MAGNUS CARLSEN: That’s probably the biggest impact they’ve had on chess.
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feliperibbe retweeted
Two things I’ve been thinking about regarding AI: • People often highlight the risks involved in AI, which are surely very real. I’m struck, however, by how hard it is to understand the contours of the positive scenario. There are easy-to-perceive and frequently invoked elements of a positive vision — more diseases cured, more material abundance, etc. — which sound great, but these are by themselves just ingredients. I’m curious what the larger equilibrium is supposed to look like. Do people work? If so, why? What is their marginal contribution? If not, how are resources allocated? What is scarce? What is the ratio between humans and AIs? Are humans in control, whatever that means? One sometimes hears the Culture novels invoked as one scenario, but I’m not sure they depict a desirable outcome. (AI runs everything and humans compete in games? Iain Banks himself said that the humans in the Culture are “somewhere between passengers, pets and parasites.”) There is Richard Brautigan’s famous poem, but do we want to be “returned to our mammal brothers and sisters”? (Isn’t that rewinding Genesis?) We could say that it was hard to imagine the world of 1900 from the vantage point of 1700, and that’s assuredly true. But nobody in 1700 was trying to recast humanity itself within a couple of years — things necessarily unfolded slowly and by increments, and therefore with the possibility for error correction. Given how rapid the rate of progress in AI may be, it feels strange for the upside case to be so opaque. • There is increasing discussion of whether AIs are conscious or how we would know whether they are, with conclusions there often assumed to be relevant to matters of moral worth. At first blush, this seems a rather arcane, benign, or even absurd issue. (Remember the response to Blake Lemoine’s fears.) Setting aside the terms of the debate itself, or the relevant demarcation criteria, it seems to me a quite high-stakes question, for an affirmative answer — that is, granting moral worth to AIs — would presumably mean that the aggregate worth of AIs would soon outweigh that of humans in any utilitarian calculus. The AIs will be exceptionally wise and intelligent, and vastly more numerous (especially once one considers the imminent future and the AIs not yet born). As such, even epsilon of moral worth will multiply to a lot once one considers the quadrillions that could soon exist. (This is effectively a distributed version of the Nozick’s utility monster.) If this conclusion is unpalatable, I think one of three propositions needs to be rejected: the premise AIs can have moral worth; utilitarianism itself; or the belief that there is even a possibility that they will be far more numerous than humans. With both of these matters (contours of future society; AI moral worth), there is the possibility of dramatic change. Today, people argue about datacenters. I’m curious how popular support for AI will evolve as more people come to understand these medium-term questions and the relevant stakes. What is the vision for AI that most people would be excited about?
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feliperibbe retweeted
The biggest bottleneck in software engineering is no longer code. It's humans. Waiting hours for (human) PR reviews. Meetings for "alignment." Hunting down the right person. Someone's on vacation? Everything is blocked. AI made writing code cheap. But organizations still run on human coordination. AI-native companies will look quite different from today's companies.
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Last night an AI agent performed brain surgery on a mouse. We gave it 19 gene therapy vectors in a spreadsheet, a mouse model of epilepsy, access to a motorize surgery rig with cameras and one goal: reduce seizures. It picked the drug. It picked the brain region. It derived the coordinates from three papers. Then it drove the stereotaxic arms into the brain and injected 250 nanolitres while watching the entry site through the rig camera for bleeding. A licensed vet gated every irreversible step and held a physical kill switch to stop the AI agent. Thread on what it decided, what it saw, and the moment it refused to let me override it. [Writing this on the spot out of excitement, but I've been 18hrs in front of my laptop coding and coordinating this, so keeping it brief and descriptive, more cohesive thoughts on this soon...]
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feliperibbe retweeted
I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy. How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes. We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together). We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today. Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment. We are really excited about this paper. Excited to hear people’s thoughts. Link: institute.deepmind.com/essay…
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Pancadaria honesta. – at Taboão da Serra, Brasil
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