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Ex-CDC director: The last words my father spoke to me—and what they taught me about saving lives

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On September 11, 2001, I was deep in rural India, five years into a posting to help control tuberculosis. In the lobby of a small hotel, I watched on a flickering TV as the second tower of the World Trade Center fell. I was born in New York City, was trained as a doctor there, and ran its tuberculosis program. Watching my hometown under attack, I knew it was time to return. Three months later, with the rubble still smoldering, Mayor-elect Mike Bloomberg called to ask me to become health commissioner.

Before I started, I visited my father in his nursing home. He was a cardiologist. Quiet, kind, and a man of few words. Parkinson’s disease had silenced him almost completely. I sat at his bedside, holding his hand, and told him about my new role. “Dad,” I said, “I want to be the best health commissioner.”

He looked at me and softly spoke last words he would ever speak to me: “How would you know?”

It was a devastatingly simple question. How do you know if you’re doing a good job in public health? Being the best doesn’t mean giving the best speech, pushing through the boldest policy, or even working the longest hours. It means saving the most lives. But how could we measure that? At the time, tobacco was killing more New Yorkers than anything else. Yet we didn’t even know how many people smoked. No one was counting.

That changed when my colleague Dr. Farzad Mostashari launched a simple but powerful survey. We started calling 10,000 New Yorkers every year, asking if they smoked cigarettes. The results were sobering: 22% of adults smoked, with no progress in a decade. If nothing changed, tobacco would kill 400,000 New Yorkers and disable a million more with heart attacks, strokes, lung disease, and cancer.  Now we could see the crisis clearly. The next step was to act.

Raising the tobacco tax was the single most effective way to reduce smoking. It was a bruising political fight, but with Mayor Bloomberg’s leadership, the tax went up $1.42 a pack. The next year’s survey showed a major drop in smoking. Soon after, we passed a law making all restaurants and bars smoke-free. After that, the next year’s survey showed another drop.

I thought we’d cracked it. But the following year, the survey showed something alarming: progress had stalled. It was terrifying. We might fail at our top priority.

The data forced us to rethink. We tried raising the tax again, but the state legislature refused. Then came a risky option: spending $10 million, money that could fund clinics and staff salaries, on anti-tobacco ads. I wasn’t convinced. But the data gave us the courage to gamble.

The next year’s survey revealed the results: the ads worked. Smoking dropped again, especially in the communities we had targeted the most. Our gamble paid off in fewer smokers, longer lives, and healthier futures.

That’s how I could answer my father’s question. Monitoring—systematically measuring what matters—made invisible progress visible. Without it, we would have stumbled blindly, unsure if we were saving lives or wasting time and money. With it, we knew. Public health doesn’t usually make headlines. One life saved by dramatic medical care is celebrated as a miracle, but millions saved by prevention are often invisible. Monitoring shines a light on those anonymous lives, proving that prevention works.

Today, smoking is at record lows. Less than 4% of U.S. high school students smoke, the lowest rate ever measured.  I continued this work in later years, when President Obama named me director of the Centers for Disease Control and Prevention (CDC), where I served for eight years. Most people who smoked in the past have already quit. Progress is real and measurable. The last words my father spoke to me, How would you know, remain my guiding question. They remind me that good intentions aren’t enough. We need proof that our actions lead to real results.

This same approach, which I reveal in my forthcoming book, The Formula for Better Health: How to Save Millions of Lives—Including Your Own is to see what’s really happening, believe in the possibility of progress, and work systematically to create a better future. This is relevant not only to public health but also to personal health and to any organization trying to make a difference.For your own health, you might ask yourself a version of the same question: How do I know if what I’m doing is working? Are the medications you take controlling your blood pressure? Are you getting physical activity you can stick with for years? Are your efforts leading to a longer, healthier life? My father’s question pushed me to see progress, and failure, clearly. It’s a question that can guide us all toward longer, healthier lives and our organizations to faster, further progress.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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What happens to old AI chips? They’re still put to good use and don’t depreciate that fast

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New AI chips seem to hit the market at a quicker pace as tech companies scramble to gain supremacy in the global arms race for computational power.

But that begs the question: What happens to all those older-generation chips?

The AI stock boom has lost a lot of momentum in recent weeks due, in part, to worries that so-called hyperscalers aren’t correctly accounting for the depreciation in the hoard of chips they’ve purchased to power chatbots.

Michael Burry—the investor of Big Short fame who famously predicted the 2008 housing collapse—sounded the alarm last month when he warned AI-era profits are built on “one of the most common frauds in the modern era,” namely stretching the depreciation schedule. He estimated Big Tech will understate depreciation by $176 billion between 2026 and 2028.

But according to a note last week from Alpine Macro, chip depreciation fears are overstated for three reasons.

First, analysts pointed out software advances that accompany next-generation chips can also level up older-generation processors. For example, software can improve the performance of Nvidia’s five-year-old A100 chip by two to three times compared to its initial version.

Second, Alpine said the need for older chips remains strong amid rising demand for inference, meaning when a chatbot responds to queries. In fact, inference demand will significantly outpace demand for AI training in the coming years.

“For inference, the latest hardware helps but is often not essential, so chip quantity can substitute for cutting-edge quality,” analysts wrote, adding Google is still running seven- to eight-year-old TPUs at full utilization.

Third, China continues to demonstrate “insatiable” demand for AI chips as its supply “lags the U.S. by several generations in quality and severalfold in quantity.” And even though Beijing has banned some U.S. chips, the black market will continue to serve China’s shortfalls.

Meanwhile, not all chips used in AI belong to hyperscalers. Even graphics processors contained in everyday gaming consoles could work.

A note last week from Yardeni Research pointed to “distributed AI,” which draws on unused chips in homes, crypto-mining servers, offices, universities, and data centers to act as global virtual networks.

While distributed AI can be slower than a cluster of chips housed in the same data center, its network architecture can be more resilient if a computer or a group of them fails, Yardeni added.

“Though we are unable to ascertain how many GPUs were being linked in this manner, Distributed AI is certainly an interesting area worth watching, particularly given that billions are being spent to build new, large data centers,” the note said.



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‘I had to take 60 meetings’: Jeff Bezos says ‘the hardest thing I’ve ever done’ was raising the first million dollars of seed capital for Amazon

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Today, Amazon’s market cap is hovering around $2.38 trillion, and founder Jeff Bezos is one of the world’s richest men, worth $236.1 billion. But three decades ago, in 1995, getting the first million dollars in seed capital for Amazon was more grueling than any challenge that would follow. One year ago, at New York’s Dealbook Summit, Bezos told Andrew Ross Sorkin those early fundraising efforts were an absolute slog, with dozens of meetings with angel investors—the vast majority of which were “hard-earned no’s.”

“I had to take 60 meetings,” Bezos said, in reference to the effort required to convince angel investors to sink tens of thousands of dollars into his company. “It was the hardest thing I’ve ever done, basically.”

The structure was straightforward: Bezos said he offered 20% of Amazon for a $5 million valuation. He eventually got around 20 investors to each invest around $50,000. But out of those 60 meetings he took around that time, 40 investors said no—and those 40 “no’s” were particularly soul-crushing because before getting an answer, each back-and-forth required “multiple meetings” and substantial effort.

Bezos said he had a hard time convincing investors selling books over the internet was a good idea. “The first question was what’s the internet? Everybody wanted to know what the internet was,” Bezos recalled. Few investors had heard of the World Wide Web, let alone grasped its commercial potential.

That said, Bezos admitted brutal honesty with his potential investors may have played a role in getting so many rejections.

“I would always tell people I thought there was a 70% chance they would lose their investment,” he said. “In retrospect, I think that might have been a little naive. But I think it was true. In fact, if anything, I think I was giving myself better odds than the real odds.”

Bezos said getting those investors on board in the mid-90s was absolutely critical. “The whole enterprise could have been extinguished then,” he said.

You can watch Bezos’ full interview with Andrew Ross Sorkin below. He starts talking about this interview gauntlet for seed capital around the 33-minute mark.

This story was originally featured on Fortune.com



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Google cofounder Sergey Brin said he was ‘spiraling’ before returning to work on Gemini

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Google cofounder Sergey Brin thought retiring from Google in 2019 would mean quietly studying physics for days on end in cafés. 

But when COVID hit soon after, he realized he may have made a mistake.

“That didn’t work because there were no more cafés,” he told students at Stanford University’s School of Engineering centennial celebration last week, Business Insider reported.

The transition from president of Google parent company Alphabet to a 40-something retiree ended up not being as smooth as he imagined, and soon after he said he was “spiraling” and “kind of not being sharp” as he stepped away from busy corporate life.

Therefore, when Google began allowing small numbers of employees back into the office, Brin tagged along and put his efforts into what would become Google’s AI model, Gemini. Despite being the world’s fourth-richest man with a net worth of $247 billion, retirement wasn’t for him, he said.

“To be able to have that technical creative outlet, I think that’s very rewarding,” Brin said. “If I’d stayed retired, I think that would’ve been a big mistake.”

By 2023, Brin was back to work in a big way, visiting the company’s office three to four times a week, the Wall Street Journalreported, working with researchers and holding weekly discussions with Google employees about new AI research. He also reportedly had a hand in some personnel decisions, like hiring. 

Skip forward to 2025 and Brin’s plans for a peaceful retirement of quiet study are out the window. In February, he made waves for an internal memo in which, despite Google’s three-day in-office policy, he recommended Google employees go into the company’s Mountain View, Calif. offices at least every weekday, and that 60 hours a week was the “sweet spot” of productivity.

Brin’s newfound efforts at work may have been necessary as OpenAI’s release of ChatGPT in 2022 caught the tech giant off guard, after it had led the field of AI research with DeepMind and Google Brain for years.

To be sure, Google for its part has been rising in the AI race. Analysts raved last month about Gemini 3, the company’s latest update to its LLM, and Google’s stock is up about 8% since its release. Meanwhile, OpenAI earlier this month declared a “code red,” its highest alert level, to improve ChatGPT. 

Brin added in the talk at Stanford that Google has an advantage in the AI arms race precisely because of the foundation it laid over years through its neural network research, its custom AI chips, and its data center infrastructure.

“Very few have that scale,” he said.



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