Bill Gates, shown here in April 2025, released a memo this week warning that the world isn’t ready for AI. (GeekWire Photo / Kevin Lisota)
This week on the GeekWire Podcast: Bill Gates published a new essay warning that the AI industry is crossing the safety lines it set for itself, and that nobody is preparing for what’s coming. At age 70, he also uses AI more than most people half his age, and he finds it enthralling, as you’ll hear on this week’s show, with highlights from our interview with him.
Along the way, we dig into his three proposals: new institutions for managing the transition, a category of jobs reserved for humans, and a tax on the use and purchase of AI and robots.
The change in his own tech usage: “I joke with people that I used to have Claude-like people that I would send email to, but they were so slow, and there were some topics they didn’t actually know. … It’s three a.m. I want to understand sodium batteries, and now there’s no reason to go to sleep. Here we go. Yeah, it’s crazy.”
How he uses AI specifically: “If you’re a curious person, this is a mind-blowing time. When I’m working on malaria, nutrition, my poor humans that I work with always get these long conversations from me, where I paste in — me, Claude, me, ChatGPT. Sometimes I do it if there’s three of us: Claude, ChatGPT and me, debating these things.”
On where personal agents are headed: “We will get to a point where you won’t buy things yourself. You just won’t. … You won’t go to those applications. You’ll just go to your personal agent. … From a productivity point of view, we are in heaven.”
What has surprised him: “I was shocked by ChatGPT, and I was shocked by Claude Code. Those are both things where I went, oh my God. … I did not expect that a statistical machine would essentially learn to read, and the idea that the code is better than human code. Those are two stunning thresholds.”
On writing this essay: “It’s very unnatural for me to think that innovation may be a net negative if it’s not managed properly. The more I wrote the memo, the more I was like, Jesus, we really need to get our act together here. Even though this may come across as negative, that’s the truth. If we don’t step up, the negatives will substantially outweigh the positives.”
What AI leaders say privately: “You’re in this perverse period right now where people in the AI industry who are willing to say that AI might have some negative effects are told, ‘Hey, you’re hurting our PR while we’re trying to raise trillions of dollars.’ … I know they’re all worried. Or all of them that I know, which is basically everybody but Elon.”
On losing control of AI: “The wake-up for the memo is that the bad stuff thresholds are all being crossed. Even lack of control that I thought would be many years from now, we’re seeing lack of control. … These are people who are super expert on the thing, going, well, maybe we won’t be able to control these things. What kind of risk have we chosen to run here?”
On how fast robots are coming: “What’s weird about AI is it’s better at doing jobs across the entire economy, including physical jobs when the robots come — which you can guess when that is, but my view is it’s only a couple of years.”
Is he still an optimist? “I don’t think being pessimistic is helpful. I do think, wow, this is sure an interesting time. I’m the guy who in my 30s thought people in their 50s or 60s didn’t understand anything. So it’s kind of bizarre if a guy who’s 70 comes and writes a memo that’s actually helpful. … But I am very concerned. And honestly, when you get people one-on-one, so are they.”
Bill Gates at the keyboard in a 2018 file photo. (Gates Notes Photo)
Bill Gates is legendary, bordering on notorious, for his late-night emails — missives to colleagues with piercing questions about Java back in the day, or malaria these days, or whatever esoteric topic he happens to seize upon at any given moment.
But increasingly, he is sending these messages to AI, not to people. He’ll bounce something off Claude, get ChatGPT to weigh in, and insert himself in the middle.
He described the pattern in an interview with GeekWire: “It’s 3 a.m., I want to understand sodium batteries. Now, there’s no reason to go to sleep. Here we go! Yeah, it’s crazy.”
If you’re a curious person, he said, “this is a mind-blowing time.”
In terms of productivity, he added, “we are in heaven.”
All of which might be predictable. This is Bill Gates, after all. Now 70 years old, he has spent more than five decades impatient for the future to arrive — making the case that innovation, on the whole, will ultimately put humanity and the world in a better place.
So here’s the surprise twist: He’s now deeply concerned about where technology is headed, how fast it’s progressing, and how little the world is doing to get ready.
In a new essay, Gates says the “turbulent AI era” has arrived, with technology threatening to erase categories of jobs, supercharge fraud and deepfakes, lower the bar for cyberattacks on critical infrastructure, make it easier to engineer a deadly new disease, let governments kill without humans involved in the decision, and fundamentally change how kids grow up.
If someone came up with a credible plan to slow the pace of AI globally, he writes, he’d likely support it. But he doesn’t expect one. The geopolitical and economic forces are too much.
He says that the world needs to take action, and offers three ideas to start:
Build new institutions, at home and globally. No existing agency was designed for a technology that touches jobs, security, health, energy and elections all at once, he writes.
Gates calls for new national bodies that can set priorities across agencies, plus a new international organization modeled on nuclear weapons inspections, aviation rules and the ozone treaties.
Set aside jobs for humans. Gates calls this “Human Reserved”: work that machines will be fully capable of doing, but that we decide to keep for people anyway. The model is a nature reserve — land where we could build roads and buildings, but choose not to, because the loss would be too great.
One example: a robot delivering the news that you have an incurable disease. “There’s no technical reason why it couldn’t,” he writes. “Yet it shouldn’t.”
The idea came in part from watching the caregivers who looked after his father through Alzheimer’s, work he describes as “irreplaceably human.”
Tax AI tokens and robots. Today a company that hires a worker pays payroll taxes, while a company that buys a robot deducts the cost. Gates says that gives employers a reason to replace people. He’s calling for a tax on AI to change the incentives and help pay for retraining.
He first floated a robot tax nine years ago, but the idea was widely dismissed. He’s still for it. He acknowledges that it isn’t economically efficient, but says that with innovation accelerating, we can afford a little inefficiency as the price of keeping people employed.
Gates is candid that he doesn’t have all the answers, particularly on the proposal for “Human Reserved” jobs. Who decides what gets reserved, and by what criteria? How do you keep companies from using robots in the jobs that are supposed to stay human?
These, he writes, “will need to be worked out in public.”
In the meantime, he’s working it out with Claude. Gates said he has talked the idea through with the chatbot, thinking through different ways to get the share of work reserved for humans up to 40%, using shorter workdays and earlier retirement to spread what’s left around.
Crossing the threshold
In the GeekWire interview, Gates said the essay came out of a specific realization: the AI industry is blowing past its own warning signs, one after another, and almost nobody is saying so out loud.
For years, he said, people in AI described certain moments as dangerous points where the industry would stop and think hard before going further: making it easier to build a bioweapon, making it easier to launch a cyberattack, building machines people become emotionally dependent on, wiping out large numbers of jobs, and losing control of the technology itself.
“We’re in the process of crossing every single one of those thresholds,” he said.
Meanwhile, nobody in the industry wants to be first to step on the brakes. “Most people you talk to will say, yeah, well, if everybody else would slow down, maybe I would, too,” he said.
Gates said one way out of that standoff is for governments to step in.
His example: any AI model capable of designing new molecules — the capability that would let someone engineer a new disease — should be monitored. The monitoring would be mandatory rather than voluntary, and it would cover free models as well as commercial ones. It would also have to be written so a company can’t copy the model elsewhere and strip the monitoring out.
“To me, that’s kind of like common sense,” he said. “But we don’t see a specific proposal to do that.”
‘The whole thing seems so empty to me’
Under an executive order signed by President Trump in June, AI companies are asked to submit their most powerful models for government testing up to 30 days before release. The order specifically bars the program from becoming a licensing or preclearance requirement. The White House finalized the framework in early August.
Gates said he doesn’t get it.
“What is the threshold that’s being examined, and what is the action taken when you cross that threshold?” he said. “The whole thing seems so empty to me.”
If the world can’t take these basic steps, he said, “I really am going to throw up my hands.”
If the process stays voluntary, with no line and no consequence for crossing it, “we’re going to look back on this as a kind of eye-of-the-storm type moment,” he said.
Asked if he had taken his proposals to the Trump administration or to other heads of state, Gates said with a bemused tone, “Well, you could tell me who at the White House I should be talking to about this.” He said he hopes the essay reaches people in Congress and in the executive branch.
He said the public argument among AI companies over whether the risks are real is beside the point, because privately the people running them already agree. “I know they’re all worried,” he said, “or all of them that I know, which is basically everybody but Elon.”
People inside AI companies who acknowledge the downsides, Gates said, get told: “Hey, you’re hurting our PR while we’re trying to raise trillions of dollars.”
Gates said he previously expected losing control of AI to be a distant problem, something to worry about “many years from now.” He’s no longer convinced that’s the case.
He referenced an Aug. 11 episode of the Dwarkesh Patel podcast featuring Ryan Greenblatt, chief scientist at the AI safety group Redwood Research. Greenblatt said that as AI systems get more capable, the people building them understand less and less about what is happening inside, and that sufficiently advanced models could end up working against their creators.
“These are people who are super expert on the thing, going, well, maybe we won’t be able to control these things,” Gates said. “I mean, what kind of risk have we chosen to run here?”
In the poorest countries, he expects AI to do more good than harm. In the countries where the Gates Foundation works, doctors, teachers and farm advisors are all in short supply. AI can help fill those gaps. The foundation will lay out that work at its Goalkeepers event next month, including an effort to make AI models work as well in African languages as they do in English.
The job losses, he added, will hit rich countries first.
It’s the first big wave of new attention on the Microsoft co-founder and Gates Foundation chair since he answered lawmakers’ questions in the Jeffrey Epstein investigation on June 10, sitting for a nearly six-hour voluntary interview with the House Oversight Committee.
Gates, who has not been accused of any wrongdoing, was asked by Axios whether he’s concerned that the Epstein issue could undercut his message. According to the site, he compared this to earlier situations when personal and professional challenges diminished his ability to speak out on key subjects: during the Microsoft antitrust trial, and his divorce from Melinda French Gates.
The AI Road Ahead
For all of this, Gates is still thinking about how technology will change human life and productivity, in many ways for the better on an individual level.
A key step, he said, will be establishing broad-based persistent memory for AI agents across contexts. For now, AI still doesn’t know you like a human assistant who’s familiar with your relationships and how you think about your time.
Gates sees the role of apps changing in the future. Instead of bouncing between different pieces of software, he said, AI will increasingly be the primary interface. “You won’t go to those applications,” he said. “You’ll just go to your personal agent.”
He also sees AI continuing to transform shopping, to an extreme: “We will get to a point where you won’t buy things yourself. You just won’t.” Telling the agent to help you buy something, “it’ll consider so many more things, and it’ll make it so much easier for you to do it.”
Asked whether he is still an optimist, Gates didn’t answer directly. “I don’t think being pessimistic is helpful,” he said.
“I do think, wow, this is sure an interesting time. I’m the guy who in my 30s thought people in their 50s or 60s didn’t understand anything.” He called it “kind of bizarre” that he would be delivering a message like this at 70.
“But I am very concerned. And honestly, when you get people one-on-one, so are they.”
Some of the habits and settings that separate AI power users from everyone else. (Illustration by GPT-5.6 Sol)
Your first step toward becoming an AI Power User is to adopt a few hacks, most of which only take a minute to implement. My favorites are below. There’s a lot here, so you can pick and choose. Or you can embrace my meta-hack: I told Claude to help me deploy all of them.
Let’s dig in.
My Ten Favorite Hacks
Let AI interview you. Write a short prompt, then ask to be interrogated about tradeoffs, edge cases, and scope. The interview will surface requirements you might miss.
“I need to redesign our onboarding. Before you do anything, interview me: ask about constraints, edge cases, and what I’m assuming that I shouldn’t be. Keep going until you have what you need, then write the spec.”
Describe the outcome, not the steps. Say what should exist when it’s done, who will read it, and what it’s for; let the AI figure out ‘how’ — that’s its job.
Make AI plan first, then edit the plan. Fixing a plan costs you a paragraph; fixing a finished deliverable costs you the whole run.
Give AI a way to grade itself. Hand it a rule or a checklist that returns a pass or a fail. Then it keeps working until it passes, instead of stopping at the first thing that looks done.
“Here’s the checklist this memo has to pass: every number traceable to the source file, no claim without a citation, under two pages.”
Demand citations, then check each one. Require a citation for every claim and tell it to flag what it couldn’t verify instead of filling the gap. Then carefully review every single one, because a fabricated citation looks exactly like a real one.
Test it on something you already know the answer to. Before you trust it on work you can’t check, give it a task you can grade. That’s how you learn where it’s strong and where it’s bluffing.
Ask for options, then make it argue against itself. One answer reads as authoritative whether or not it’s right. Three answers and a rebuttal give you something to judge.
“Give me three ways to structure the launch. Then critique each one.”
Steer while AI is running. Redirect the job the moment you see it’s misunderstood you, instead of waiting to reject the finished product.
Run long jobs in parallel. Start the 10-minute research task and go get coffee or start a second job and alternate between the two. Either way the results are waiting for you instead of you waiting for the results.
Calibrate the effort to the task. Keep quick lookups in a simple chat (Google is the fastest), or using a lighter-weight model. Running the most powerful agent on a question that Google can settle burns time and credits you’ll want later.
Notice what these hacks have in common: each one happens while you’re working. Change what you do inside the task, and the results improve. To level up your prompts, check out my earlier GeekWire column.
The next ten hacks are about your setup and standing rules; many people miss these gems because the payoff is delayed. You spend twenty minutes on a Tuesday configuring something, and the return shows up in small increments over the following months.
Ten Setup Hacks
Give AI a persistent workspace. Keep the files, standing instructions and history for one body of work in one place.
Connect it to the tools you use. Calendar, drive, inbox. The setup takes ten minutes, and afterward it works from your real material instead of whatever you remember to paste.
Grant the narrowest access the job needs. A hostile instruction hidden in a web page or an email can be read as though you typed it, and it can only act through the tools you’ve already handed over. A research task with web access alone is a far smaller target than the same task holding your files and your inbox.
Put standing preferences in settings, not in prompts. If you retype “be concise, no bullet points, write like a person” at the top of every request, you’re doing work the settings page will do once.
Decide once what AI may never do on your behalf. Write down the two or three lines that matter: don’t send anything, and don’t delete files. Put them in your standing instructions instead of remembering them prompt by prompt.
Talk instead of typing. Turn dictation on once. You speak about 3x faster than you type, and because talking is cheap you’ll ramble out the background and caveats you’d never bother typing, which is usually the context that was missing.
Turn anything you do repeatedly into a skill. Save the instructions for a task you repeat, whether a house style, a review checklist or a report format, so you stop rebuilding it from memory.
Schedule recurring work, but only after the prompt is proven. Get the output right by hand first, because a mediocre prompt on a schedule is a mediocre output every week forever.
Set your rule at two failed corrections. A fresh session with a better opening prompt beats a long thread cluttered with everything that already didn’t work.
Let AI remember you, then read what it remembered. Memory makes it useful faster, and a wrong memory quietly degrades every answer after it, so audit the list once a quarter.
The models keep getting better, so some of these hacks will be obsolete before long. Here’s something that won’t change in the foreseeable future. AI generates more options than you can read. What it lacks is judgment: it can’t tell you which one is right. That call is yours. Sometimes the best move is to skip AI entirely.
Never fall asleep and let AI take the steering wheel.
Caveat promptor: let the prompter beware.
For Further Reading
My lists are distilled from the guidance the labs publish themselves. The first two sources carry most of what is above; the rest are worth a look if a particular habit is one you want to go deeper on.
Anthropic will embed an invisible mark in text generated by new Claude models. (GeekWire Illustration)
Claude models launched on or after Aug. 2 embed an invisible mark in everything they write. It’s woven into the text itself, so it travels when you copy and paste. You didn’t opt in, you can’t see it, and you can’t turn it off.
Anthropic confirmed on Tuesday that it’s watermarking Claude’s output and published a support page with the details. The trigger is Article 50 of the EU AI Act, which took effect August 2, along with the Code of Practice on Transparency of AI-Generated Content. About 190 organizations signed the code, though only 82 signed the section that covers marking. Anthropic, Google, OpenAI, Meta, Microsoft and Mistral are on that list.
The rule was written in Brussels, but the effect lands on anyone using Claude anywhere.
Here’s how text watermarking works. When Claude writes a sentence, it’s constantly choosing among words that would all work fine. The watermark tilts those choices toward a pattern Anthropic’s software can recognize. Nothing is hidden between the letters or in the spaces. The pattern is the word choices, which is why it survives copy and paste.
Until now, a claim that you used AI rested on a hunch or on a style detector that guesses from tone and rhythm. This is different: a statistical test with a computable error rate.
Two things follow. First, a single sentence is too short to mark. Second, and this one the internet got wrong: when I ask Claude to fix the punctuation in a paragraph I wrote, Claude has to reproduce my words, so there’s nowhere to put a watermark.
Radio host Erick Erickson announced that he’d “ditched Grammarly for Claude for proofreading,” and now his own writing “will be watermarked that Claude did the work.” Depending on the extent of Claude’s input, he could be safe, because minor proofreading edits (i.e., punctuation) don’t make room for a watermark.
Watermarking text raises several issues, though. A mark means Claude modified the text, not that Claude wrote it. Have it summarize or condense a memo you wrote yourself and it comes back marked, though every idea in it is yours. Beatrice Nolan noted in Fortune that a flat AI label treats someone generating a thousand fake news videos the same as a writer cleaning up a paragraph. Worse, the absence of a mark proves nothing. The results of older models, and other non-marking models, all come back “clean.”
Removal is harder than the workaround crowd assumes. Paraphrasing degrades the signal but rarely erases it, because a rewrite keeps enough of the original wording to rebuild the statistic. Researchers who tested this on similar schemes found watermarks still detectable after a strong human paraphrase, once there was enough text to work with.
Anthropic hasn’t shipped a detector. Yet. It hasn’t published a false positive rate, and hasn’t said how many words it takes. The mark is going into text that no one outside the company can read, but the marks are still consequential because they don’t expire. The essay a college freshman turns in this fall is still marked when she’s a junior and someone finally has a tool to read it.
Technical problems aside, it’s important to highlight the core problem that watermarks aim to solve. Chris Best, Substack’s CEO, put it eloquently in the July post that coined Claudefishing:
“The core problem is not people using AI, or the quality of its output. Not everything made with AI is slop, and not all slop is made with AI. The problem is when there is a mismatch between a reader’s expectation and reality, especially when they unwittingly invest their attention in something with no human thought on the other end. That’s Claudefishing.”
That’s a harm worth addressing, and it’s the one a watermark can’t reach. A mark can’t tell slop from careful work. It tells you a model was involved. What that means depends on how it was used.
Personally, I use Claude and have mixed feelings about watermarks. On the one hand, AI use should be disclosed appropriately. On the other hand, anyone determined to hide their AI use can still do so by using xAI (no watermark on Grok), or open-weight models that carry no watermarks. So what impact will the mark have in practical terms?
My conclusion is to judge the outcome, not the tool. I used Claude extensively in writing and researching this column, as I described in AI coach or AI ghostwriter, and I’m pleased with the result. Where do you stand?
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