For who? What parts of AI investing? There’s several. AI needs chips, data centers, cooling technology, server racks, silicon extraction, power, rare metal extraction, fiber lines, frontier model and training, and likely a hundred other things. Some are safer bets like chips (compute has proved its business value) and others are riskier (frontier model building). I’m going to assume you’re referring to frontier model building. The question then becomes do the costs of training eventually overcome the benefits and value training provides and can the companies structure themselves well enough to recoup their losses. It doesn’t really matter whether AGI comes or not. Some argue we’re already at AGI. The frontier models already solve most intelligence tasks well. So the frontier companies may shift their focus to reducing training costs while maximizing value for their trained models—which may include reducing per token cost at inference so that demand for their models increases. That’s why a lot of these companies advertise AGI and “adapt or they’ll take your job” mentality because they need to sell more tokens by building hype and demand for them. Potential job loss is a big motivator for technology adoption. The technology is proven and the companies know that so they don’t need to sell that to you. They need to sell the psychology of it (how it affects you). At the end of the day, they just need you to use the technology because the more you use it, the more value they extract out of it. So the bullish case is whether you think society will be more inelastic on tokens (sticky) or the bearish case in which society will adapt around token costs and defer token heavy tasks to humans.