Arvind Krishna: “I…think that many people when they hear this – I actually disagree with the way many economists and many people characterize it, that if you make somebody more productive, then you need less of them. That’s actually been false in history. If you are more productive, that means you have a natural economic advantage against your competition, which means you’re going to get more work, which means you’re going to need more people. And I think people forget that – they come from a zero-sum mentality to say it’s a zero-sum game… The world I live in, you’re more competitive, so that means you’re going to get more work, which means you need more people to do that work. So yes, certain roles will shrink because you don’t need so many people doing, maybe, email responses or phone calls, but then it will shift to maybe more applications will get done, or maybe you’ll be advertising to different markets that you previously could access. So there will be a shift – yes, the first bucket decreases, and everybody fixates on that. By the way, at our scale, that’s 3% of our entire employee population…I fundamentally believe we’ll get more jobs. There wasn’t an internet job in 1995. How many are there today, 30 million…? There was no CNBC.com in 1995. There was a television channel.”
Peter Coy: “[Charlie] Munger talked about “combinatorial effects” in which a variety of psychological forces, each mild enough in its own right, come together and reinforce one another to create good or bad lollapaloozas. The original success of Coca-Cola came from positive combinatorial effects, with the qualities of the product and the relentlessness of the advertising working together to habituate customers, he wrote. Conversely, he added, the ill-fated introduction of New Coke in 1985 suffered from a combination of several mistakes. To get the good lollapaloozas and not the bad ones requires two things, from what I can glean from Munger’s book and Berkshire Hathaway’s famous annual shareholder letters. One is patience to wait for good opportunities, and the other is plentiful available cash when those opportunities finally present themselves.”
NYTimes: “From 2012 to 2022, investment in private U.S. start-ups ballooned eightfold to $344 billion. The flood of money was driven by low interest rates and successes in social media and mobile apps, propelling venture capital from a cottage financial industry that operated largely on one road in a Silicon Valley town to a formidable global asset class akin to hedge funds or private equity. During that period, venture capital investing became trendy — even 7-Eleven and “Sesame Street” launched venture funds — and the number of private “unicorn” companies worth $1 billion or more exploded from a few dozen to more than 1,000. But the advertising profits gushing from the likes of Facebook and Google proved elusive for the next wave of start-ups, which have tried untested business models like gig work, the metaverse, micromobility and cryptocurrencies. Now some companies are choosing to shut down before they run out of cash, returning what remains to investors. Others are stuck in “zombie” mode — surviving but unable to grow. They can muddle along like that for years, investors said, but will most likely struggle to raise more money.”
Sundar Pichai on Gemini: “A specific part of what makes it exciting is it’s a natively multimodal model from the ground up. Just like humans, it’s not just learning on text alone. It’s text, audio, code. So the model is innately more capable because of that, and I think will help us tease out newer capabilities and contribute to the progress of the field. That’s exciting. It’s also exciting because Gemini Ultra is state of the art in 30 of the 32 leading benchmarks, and particularly in the multimodal benchmarks. That MMMU benchmark—it shows the progress there. I personally find it exciting that in MMLU [massive multi-task language understanding], which has been one of the leading benchmarks, it crossed the 90% threshold, which is a big milestone. The state of the art two years ago was 30, or 40%. So just think about how much the field is progressing. Approximately 89% is a human expert across these 57 subjects. It’s the first model to cross that threshold. I’m excited, also, because it’s finally coming in our products. It’s going to be available to developers. It’s a platform. AI is a profound platform shift, bigger than web or mobile. And so it represents a big step for us from that moment as well.”
