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Deirdre Bosa
@dee_bosa
Pronounced Dear-dra. Anchor & reporter @CNBC for 15 yrs. Now building something new. still a Leafs fan 🇨🇦
4.6K Following    76.1K Followers
The new Copilot is Microsoft’s bet that the AI race is moving from models to products. It doesn’t need to own the best model if Copilot can choose among them and keep the customer inside Microsoft. The potential secret weapon is Autopilot (what I’ve been calling Muse for Business). It knows your workflows, sits across the apps and data you already use, and keeps working even when you step away. It’s a preview of the battle to come in enterprise AI. OpenAI and Anthropic are racing from models into products. Can they build their own version of this? And can they match Microsoft on the boring-but-critical stuff: permissions, identity, auditability and control, as agents become more autonomous? My full conversation with @satyanadella. We also talk open vs. closed models, US-China Summit, regulation and the infrastructure buildout. 00:00 Microsoft’s new Copilot 02:02 Why not just give us Autopilot? 05:18 Who pays for always-on AI? 08:06 Copilot picks the model 10:27 Chinese models and OpenAI’s lead 12:04 Competing with OpenAI and Anthropic 13:17 US–China AI talks 16:11 Does AI need new rules? 18:05 The data center backlash 20:40 Is AI being overbuilt? 23:44 Keeping humans in control
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Another look at the open vs closed model shift: Chinese labs Moonshot + DeepSeek + now account for more inference spend than OpenAI on Vercel. and the shift behind the shift: that money not necessarily going to those labs. Fireworks, Together, Baseten and the clouds are serving the models and collecting the $$
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Open models were already winning on token share. Now they’re generating more inference spend than OpenAI (on Vercel) More pressure on frontier model margins
Excellent listen for anyone worried about the AI discourse right now. Mead makes the case that all the disagreement, competing motives and general messiness isnt necessarily a sign of decline… but may actually be part of what keeps America adaptive and powerful. The caveat is whether the social fabric can absorb that much stress.
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This was the natural progression of this narrative The more OAI and Anthropic argue that commercial pressure can push AI companies to move too fast, the harder it is for either to go public. Public markets just add more of that pressure: growth expectations, quarterly reporting, a stock price moving every second imagine telling investors: actually we need to slow growth Whether you think it’s the responsible thing to do or not, it’s also a very tough public company model.
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Would be fascinating to hear from Liang Wenfeng, Tang Jie and Yang Zhilin (DeepSeek, Zhipu, Moonshot)
More details on what I’m building: a daily, hour-long livestream in partnership with @YahooFinance Live, online, interactive and based in San Francisco. @sarafischer has the news here:
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welcome to the open source + model agnostic era
the Cursor/OAI drama feels like a pretty big opening for Cognition. still independent/model agnostic/has access to all the models turns out "we're not tied to any one lab" may be a pretty good enterprise pitch
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Lesson from the wrapper era: dont let your supplier become your kill switch OAI is now just 5% of Cursor traffic. It diversified suppliers and starting building its own models, including on top of open source
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In the early 2010s ppl were trying to figure out how smartphones and social media were changing their lives & portfolios. Now its AI... and the right format is moving beyond traditional TV. The great, Canadian Marshall McLuhan said it best: “the medium is the message.” I used that line 14 yrs ago when I was trying to get hired at CNBC and it still works today Can’t wait to jump in
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Lots of prominent ppl have had op-eds shaped or outright written by staff, comms ppl, ghostwriters, etc So not sure AI changes the underlying thing that much. If anything, might make the process more honest… Druckenmiller’s ideas, AI’s prose, and everyone knows it
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I know it’s been said many times… but just had Starlink on a flight for the first time and wow. One of those Apple-like moments where tech just *works* Hard to see ppl going back once they’ve tried it
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at what point does evaluating open source models become a fiduciary duty? (given price-performance gap)
open source AI is...happening? :) Anthropic & OpenAI better hope AT&T is the exception, not the rule
More of this pls. One of the biggest bottlenecks to AI adoption is comprehension... tools are shipping faster than most ppl can figure out what they're actually useful for. demystifying the tools is becoming as important as building them. (I'll post my use case later if I can get to anything good)
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Some ways people used Grok Bot this week:
Mutually assured intelligence as the new mutually assured deterrence. Logic in the nuclear era was: you won’t attack me bc I can attack back. If you apply that to AI, then the answer isn’t to prevent everyone except a few trusted labs from having powerful models… its to make sure other people have powerful AI that can defend against theirs
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For about 10 years now, I have argued that the *only* way forward is for AI technology to be widely available, shared, and open. Like the printing press and the Internet, AI amplifies human intelligence and efficiency by improving access to knowledge. To empower individuals, societies require a high diversity of AI systems with different value systems, linguistic abilities, philosophical/political biases, and specific expertise. We need diverse AIs for same reason we need a diverse press. Given the cost and complexity, this can only be achieved through open foundation models on top of which anyone can build systems with their languages, biases, expertise, and value systems. I have been more vocal about this over the last 4 years, since AI popped into the public discourse. I have made the argument in various forums: corporate C-suites, AI safety discussion groups, professional meeting, the US Senate, the UN Security Council, and the public sphere through media interviews, podcasts and social media posts. I totally agree with @finkd Mark Zuckerberg's recent piece in which he writes: "the notion that AI is so dangerous that the only safe path is an extreme concentration of power seems inherently problematic. Historically, hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened has not led to safe or positive outcomes.” When @DarioAmodei writes: “some may object that we can simply keep AIs in check with a balance of power between many AI systems, as we do with humans", he is talking about me, among (thankfully) many others. It is the only good path forward. There will be nefarious uses of AI, as there have been with every technology ever invented. But it will be your Bad AI against my Good AI.
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Really good/fascinating exchange but… don’t quite buy that this is just a generalized crisis of trust that’s part of a broader souring on tech Social media yes, but ppl *like* gmail and iPhones (my Taiwanese immigrant mom thinks google and youtube are pure magic bc its knowledge at her fingertips she never imagined possible). On AI specifically, American labs made it about an abstract race to AGI (something my mom doesn’t care about) while China positioned it as cheap, useful technology that helps ppl build things and do their jobs better right now.
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2/2 Second, on the messaging around AI.  I do not agree that my messaging has been disproportionately negative.  In fact it has been about equally balanced between risks and benefits: I’ve written one major essay about each, and even in interviews where I discuss the risks, I make sure to frequently mention the incredible benefits as well as proposing possible solutions to the risks (short clips from my interviews that end up on social media tend to be disproportionately negative, as that gets clicks).  In fact, I wrote Machines of Loving Grace because I didn’t feel the AI industry was painting an inspiring enough picture of how the technology could radically transform the world for the better.  The bulk of the essay is devoted to refuting skepticism of AI’s potential in health and biology, and showing why I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people and frankly to biologists as well (I used to be one!).  And, if you read my most recent essay (Policy on the AI Exponential), I discuss concrete proposals for how to streamline the FDA process to make sure the deluge of AI-accelerated drugs isn’t slowed down by the regulatory process.  I feel the urgency here: I lost my father to Hepatitis C only a few years before the development of direct-acting antivirals (sofosbuvir), which cure 95% of patients and probably would have cured him. I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks.  I think it is fundamentally a crisis of trust.  I think that ordinary people don’t trust companies, governments, or the tech industry and always suspect that we are cooking up some new way to screw them over.  The causes of this go back decades and AI is just the latest iteration of it.  I don’t think that a glitzy marketing campaign with a positive spin (which some have advocated that Anthropic do) is the way to win back that trust — at this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive.  The thing that will work is *actually curing cancer*.  I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world.  That is totally on us, and I think it’s the criticism you should be making, instead of all this stuff about messaging and marketing. We are however doing our best to fix this: Anthropic is ramping up its efforts very quickly in biology and medicine, and we hope to have incredible results in the coming years and some early glimmers in the coming months.  When we’ve actually accomplished something real, the whole world will hear about it, as loudly as possible, you have my word on that.  But until then I don’t want to make empty promises, and in the meantime I feel compelled to speak honestly about the very real risks of AI and how to address them.  Honesty is the right thing on the merits, and in terms of public credibility and trust it is no worse than, and may in fact be better than, an approach that ignores or distracts from risks which people instinctively understand are real.
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What a difference a year makes A year ago, frontier basically meant the big three US closed labs - OpenAI, Anthropic and Google. Now a credible list includes xAI and multiple Chinese/open weight labs.
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3 frontier models in one day! - Grok 4.6: Fable 5-level, but 85% cheaper. - Qwen3.8-Max: 2.4T params, 95B active. Weights are out. - DeepSeek-V4-Pro-0813: weights could drop any time. Heard it’s good, not just benchmaxxing. Competition is great for consumers and businesses.
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.@lqiao is great. Here she is on the question every AI company has to answer: what is the ROI? if what a customer pays doesnt cover the compute they consume, growth can just deepen your losses. So you can find product market fit.... and still “scale into bankruptcy”
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Super excited that I will be interviewing @lqiao CEO and founder of @FireworksAI_HQ at the @tomorrowxsummit in Austin, TX November 17-18. Lin’s vision of specialized intelligence is compelling and she is making it a reality. Fireworks is at the forefront of helping enterprises take an open source model, post-train it with proprietary data and then combine it with a frontier model behind a router to get frontier intelligence at a much lower cost. And per Bessemer they went from 100m in ARR to 1b in 16 months, which was faster than any company they have tracked other than Anthropic. 40 trillion tokens per day as of July!
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My conversation with @ericvishria of Benchmark. Eric has spent a decade investing across software and hardware, backing companies like Fireworks, Sierra, Sunday Robotics, and Cerebras. This one is about what history teaches us about the current moment, and a dispatch from inside the AI buildout through his companies. We discuss: - What AWS tells us about how big AI can get - How the goalposts have moved for every software company - Lessons from a decade with Cerebras - China and the energy bottleneck - Benchmark's return to growth investing - Robotics Enjoy! TIMESTAMPS 0:00 Intro 3:36 What Cloud Teaches Us About AI 12:52 Sierra, Sandcastles, and AI Products 17:35 The New SaaS Competitive Frontier 28:28 AI Demand and the Energy Bottleneck 31:28 The Cerebras Story and AI Chips 45:10 The Future of Robotics 52:53 Eric’s Venture Investing Philosophy 1:07:30 Going Public, AI Value, and Jobs
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