Everyone talks about what AI will do. But almost nobody asks who will pay for the machines. Taking advantage of AI will require an extraordinary amount of capital, from data centers to new power plants. Will that capital be around? I try to address that question in the first draft of my new paper “Saving for the Machines.” To show how important capital is to fuel the transition to an AI-based economy, I document how the same technology path that generates 11.7% annual growth when households choose their saving rate optimally delivers only 2.2% when the saving rate remains fixed. In other words, if we experience slow growth over the next few years despite fast improvements in AI, this slow growth may reflect too little capital accumulation instead of a technological bottleneck. The key to this result is that AI raises the capital share by moving tasks from labor to machines, which requires that those machines are built. In comparison, ordinary technical progress makes labor and existing machines better at the tasks they already perform. To inspect the mechanism, I start from an accounting identity: the saving rate is the capital share times the ratio of investment to capital income. If I take the evolution of the capital share as given, I can ask how much the latter ratio moves in response to that evolution. Crucially, with an optimizing household, this ratio barely moves. As machines take over tasks and the return to capital rises, the household responds by letting consumption grow faster, by the intertemporal elasticity of substitution times the rise in the return. To pay for that faster consumption, capital must also grow faster by the same amount. So, the household keeps investing about the same fraction of what the machines earn. And since machines earn a larger share of output, the saving rate also goes up. With a fixed saving rate, the same accounting identity tells me the opposite: the ratio falls in proportion as the capital share rises. Will the U.S. and other advanced economies then generate enough capital? For many reasons, I doubt it. We might end up in a situation where many of the opportunities offered by AI are left on the table. This is particularly important for all who think that AI might get us out of many problems, such as fiscal troubles (like in France) or ultra-low fertility. We need to save for the machines, and it is not clear we can do that. Anyway, this paper has been long in the making (many months of hard work), but I have learned much from it. In particular, working through the model changed my mind about many issues related to AI. Again and again, my intuition said X, but the math said Y. And math always won in the end. That is why we write formal models. A nice example is Proposition 5, whose proof is the one I like the most in the paper. With improving machines and strong task complementarity, there is no fixed balanced growth path because the target itself keeps moving. The nice part of the proof is to realize that, after the right normalization, the moving target becomes a fixed point, and the saddle gets steeper as machines improve. So instead of falling behind, the economy tracks the moving BGP more and more closely. A fair amount of ODE work to get there, but a very simple economic idea underneath it once you get it. The paper is here: sas.upenn.edu/~jesusfv/Savin… and the slides are here: sas.upenn.edu/~jesusfv/Slide…

Found a dude on Instagram recording his adventures smuggling diesel off east Africa

Poppi's founders just learned the most expensive lesson in M&A! When they sold to PepsiCo, the deal was for $1.9B plus a $300M earnout (a future contingent payment based on the performance of the brand for some period after closing). And PepsiCo just announced that it is valuing that $300M at a “paltry” $12M. That stings! I've negotiated a lot of earnouts in my career. Here's what most sellers don't understand until it's too late: 1. An earnout is a future bet on a business you no longer control. After closing, the buyer sets budget, pricing, hiring, marketing, etc. Every lever behind whether or not you get your earnout belongs to someone else. 2. The incentives can flip the day you close if you’re not careful. As a seller, you want to do everything to hit the targets. But as the buyer, you pay less if they're missed. Ideally, the earnout is structured so that both parties win or lose together. If performance tanks, the buyer earns less overall so it pays less to the seller. If performance improves, the buyer earns more so it pays more to the seller. Incentives are aligned. What you absolutely cannot allow to happen is an earnout structured so that performance can improve allowing the buyer to earn more, but still pay less to the seller. How on earth could that happen? I'm so glad you asked... 3. The accounting decides the outcome. How you define "revenue", "EBITDA" or any other measurement metric used to determine the earnout is what ultimately drives the payout. So things like how corporate overhead gets allocated or what happens when the Seller’s product is folded into a larger brand with competing products becomes unbelievably important. Again, remember the incentives. With a poorly structured earnout, a company like PepsiCo could be incentivized to buy a brand like Poppi and purposely underinvest in the brand in order to eliminate a key competitor to its other products. So PepsiCo could earn more by driving more revenue to products other than Poppi, while simultaneously reducing the earnout payable to Poppi because Poppi's direct performance declines. PepsiCo wins -> Poppi loses. Sellers have to be very careful about how the accounting for the earnout is defined and whether it causes misaligned incentives. 4. The reasons above are why earnouts are among the most litigated terms in M&A. Sellers often sue buyers claiming the buyer sandbagged performance. And Buyers defend by saying “The contract gave us full discretion." Courts will generally enforce whatever terms the parties agreed to in writing, so an argument that Buyer acted in “bad faith" rarely saves a seller who didn't negotiate adequate protections. 5. If you agree to an earnout, protect it at all costs! > Tie it to metrics like revenue, which are subject to less manipulation than metrics like Adjusted EBITDA. > Include strict operating covenants around things like required budget, sales support, and no diverting customers to other products or business lines. > Push for audit rights and a dispute mechanism. > Demand acceleration of the earnout if the buyer or product line is sold, merged, shut down, or deprioritized. > Keep the measurement period short! The longer the period, the more opportunity it gives a Buyer to game the system. To be transparent, I don’t have any background into what happened here. I have no idea if the earnout was poorly structured or if PepsiCo acted in a way to purposely reduce the earnout. That's not the point of this post. What I do know is that a $300M earnout that is written down to $12M is a tale as old as time and almost certainly to end up in litigation. And even if not the case in this particular instance, in most cases where this happens, there's often things that could have been done in the transaction documents to avoid it. My advice to every seller I represent who is considering agreeing to an earnout is simple: Value the deal based on cash at close and treat the earnout as a bonus. If you get it, great. But don’t count on it!

as vezes macarrao com brocolis e linguiça sentada no chao do box durante o banho é café da manhã e ta tudo bem

4/ APT stands for Apple Persistent Threat

🇧🇷São Paulo's Belle Époque — The Anhangabaú Park in the 1920s

RELEASE DAY After almost 10 years of hard work, tireless research, and a dive deep into the kernels of computer science, I finally realized a dream: running a high-level language on GPUs. And I'm giving it to the world! Bend compiles modern programming features, including: - Lambdas with full closure support - Unrestricted recursion and loops - Fast object allocations of all kinds - Folds, ADTs, continuations and much more To HVM2, a new runtime capable of spreading that workload across 1000's of cores, in a thread-safe, low-overhead fashion. As a result, we finally have a true high-level language that runs natively on GPUs! Here's a quick demo:

Lunch time

First on 60 Minutes: Newly declassified video shows service members from U.S. Joint Special Operations Command rescuing Bravo, one of the two U.S. Air Force officers shot down over Iran in April. Bravo was stranded for two days – alone and badly wounded, behind enemy lines. cbsn.ws/4rcO6UK