Larry Fink recently said exactly that. At an event in Waco, Texas in early May, he stated that building the massive AI infrastructure, data centers and power grids, will cost around $10 trillion over the next decade, and a big chunk of that money will have to come from Americans’ savings accounts and pension funds. He wasn’t saying BlackRock is seizing the money, he was talking about forcing investing from retirement savings, like 401(k)s and pensions, into these projects. BlackRock already manages over half its $14 trillion in retirement assets, so they’re in prime position to direct that capital. Fink framed it as a way for regular people to profit from the AI boom, but a lot of folks see it as him pushing to use other people’s money for BlackRock’s big infrastructure play. In his May talk in Waco, he said the $10 trillion needed for AI data centers and power infrastructure will largely come from private capital, specifically people’s long-term savings, pensions, and retirement accounts like 401(k)s. BlackRock clarified he meant investment accounts, not regular bank savings, and framed it as a way for everyday Americans to profit from the AI boom. The part about devaluing property, straining water and energy, or causing health issues isn’t something Fink mentioned. Those are real local concerns with data centers, they use massive amounts of electricity and water for cooling, can strain local grids, and raise worries about noise, air pollution from backup generators, and potential chemical runoff. Whether they actually lower nearby home values is mixed, some studies show little negative effect, others show communities push back hard. Fink’s pitch was basically “invest in the future to grow your retirement.” Critics see it as using regular people’s money to fund something that burdens the same communities.

Sam Altman, OpenAI’s CEO, says AI will become a utility like electricity or water. His exact words: “We see a future where intelligence is a utility like electricity or water and people buy it from us on a meter and use it for whatever they want to use it for.” He means you’ll pay only for what you use, like your power bill, heavy users pay more, light users pay less, instead of flat subscriptions. This was at the BlackRock Infrastructure Summit back in March. He says the real challenge is building enough compute power so it stays cheap and doesn’t just go to rich people. Altman said demand for AI is exploding, and if we don’t keep building compute infrastructure fast enough, either supply runs short or prices skyrocket, making it something only rich people can afford. He called it a big infrastructure challenge, basically saying the buildout has to keep pace with that rising demand to make AI truly like a cheap utility for everyone.

We’ve made huge strides in narrow AI: robots can assemble cars, sort packages, and perform repetitive factory tasks with impressive precision. But we’re still very far from replacing humans broadly. Today’s robots struggle with anything that requires common sense, adaptability, or working in unpredictable environments like a messy home. General-purpose humanoid robots are in very early stages, mostly research prototypes that still need a lot of human oversight. So while specific jobs are being automated, a full human replacement scenario is decades away at best. Already heavily impacted: • Junior coders, many entry-level programming tasks are now handled by AI tools like GitHub Copilot. • Data entry and basic analysis, AI handles routine spreadsheet work and report generation. • Basic customer support, chatbots manage simple queries 24/7. Tech jobs seeing big changes: • Software engineers spend less time writing boilerplate code and more time on complex architecture. • QA testers are being supplemented by AI that automatically finds bugs. • Technical writers are using AI to draft documentation. The pattern is clear: AI is replacing repetitive, rules-based tasks first. Creative problem-solving, stakeholder management, and strategic thinking are still very much human work. Most companies are using AI to make workers more productive rather than outright replacing them.

The field of artificial intelligence was officially born at the 1956 Dartmouth workshop, where John McCarthy coined the term “artificial intelligence.” Key founders include McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who presented the first AI program, the Logic Theorist. Alan Turing laid the theoretical groundwork earlier with his 1950 paper and the Turing Test, asking if machines could think. So it’s more a group effort than one inventor. The original founders didn’t complete it at all. They set up the field and built early programs that solved math problems or played checkers, but the tech hit big limits. There were two “AI winters” where funding dried up because results didn’t match the hype. What we use today, like ChatGPT, comes from deep learning and neural networks that really took off around 2012 with AlexNet. That work was led by Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, decades later. The Dartmouth group laid the vision, but modern AI is a completely different approach built on massive data and computing power they couldn’t dream of.