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‘It Was a Source of Great Joy’
A short profile of Tufts University faculty member Kristen Wendell
The One Chart That Explains Why Netflix Cleaned House
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) Yeah, I’ll be honest: I went with a clickbait-y headline today. One chart? That explains everything? That’s as clickbait-y as it gets. But let’s be real. I’m analyzing a dataset that I’ve built over six years, and c’mon, I never write articles which feature just one chart! If you think this article—which includes a level of data detail unheard of in journalism twenty years ago—is clickbait, fine by me. Really, what I’m doing is using data to explain Netflix’s recent strategy changes, since the story of the month is the turbulence at Netflix. First, head of content Bela Bajaria was fired. Then head of film Dan Lin followed her to the unemployment ranks. (Along with, sadly, 1,000 other Netflix workers.) And then Netflix delivered a “sub-optimal” earnings report, which continued their year-long stock price slide. Then came a flood of articles explaining why all this is happening. (Mine’s coming shortly, rest assured.) I realized I had the perfect chart to explain Netflix’s woes. Specifically, Nielsen’s weekly top ten charts. Usually I focus on one or two weeks in my Streaming Ratings Reports, but sometimes it’s worth stepping back to look at the larger story. And hoo boy, what a story. This article is my annual (plus three months) follow-up to one of my most popular articles of last year. (And a topic I’ve been visualizing for the last half decade.) Subscribe Netflix’s Share of The Nielsen Charts Continues to Decline
Joe Burrow has never hidden his political beliefs
Cincinnati Bengals quarterback Joe Burrow didn’t have much to say about Vice President JD Vance’s surprise visit to practice on Wednesday. But for a player who has rarely been shy about speaking his mind, his short response certainly caught some attention. Burrow said that he didn’t get a chance to meet Vance, noting that the…
Small business optimism dips in September NFIB survey
Small business optimism slipped in September as owners grappled with rising costs, higher interest rates and a persistent shortage of qualified workers, according to a monthly survey released by the National Federation of Independent Business. The NFIB Small Business Optimism Index fell 0.8 points to 97.9, just below the survey’s 52-year average of 98.0. The […]
'Mass Effect Universe' Trailer Leaks A Day After BioWare Shutters
A short clip teases what is essentially Mass Effect 5
UM Health-Sparrow offers $40,000 in loan repayment to nursing, respiratory therapy students
University of Michigan Health-Sparrow is working to address the growing shortage of nurses and respiratory therapists in Mid-Michigan by offering eligible students up to $40,000 in loan repayment.
Proposition 2 could double California's rainy-day fund cap
Voters will decide whether to allow California to save more money for future budget shortfalls
Through pioneers and slow progress, America's relationship to women's sports continues to evolve
LOS ANGELES (AP) — Athletes and fans of women's sports across the United States have found no shortage of reasons to celebrate: historic TV ratings, changing attitudes toward female players, high-profile wins for equal opportunities and pay.
Through Pioneers and Slow Progress, America's Relationship to Women's Sports Continues to Evolve
LOS ANGELES (AP) — Athletes and fans of women's sports across the United States have found no shortage of reasons to celebrate: historic TV ratings, changing attitudes toward female players, high-profile wins for equal opportunities and pay. After decades of disparity and sometimes outright scorn ...
Xbox reveals pricing for cloud gaming limits, starting at $3.99 for 10 hours
Cloud playtime packs provide ad-free cloud gaming, including shorter wait times and high-quality streaming
UTA College of Science dean discusses research opportunities, career skills
Morteza Khaledi met with students Wednesday to share resources and ask about their opinions and goals.
UTA Science Learning Center sets up laboratory-themed escape rooms
The event aimed to highlight resources for students and incorporated anatomy models, chemistry model sets and more.
Will Kings Defeat Clippers, Improve Season Opener Record?
Forty-one years after a historic debut, injury-depleted rosters collide as Sacramento’s new-look trio aims to snap an opening-night skid against a short-handed Los Angeles squad.
Tired of Being Tired? 7 Science-Backed Ways to Sleep Better
Daylight saving time starts in a few weeks, ushering in the longer nights and shorter days associated with winter. But for many people, the extra hours of darkness aren’t necessarily more restful. Experts say sleep deprivation is on the rise in the U.S. And the combination of stress, shift work and the false bravado of […]
Scrap exports deepen Europe's aluminum shortage
Aluminium is becoming scarcer and more expensive in Europe as growing volumes of scrap are exported abroad. Manufacturers are urging the EU to impose export restrictions.
Australia urged to put biodiversity at the heart of planning as global targets slip out of reach
The world is falling short of its biodiversity commitments because nature is still treated as an afterthought in decisions about housing, infrastructure, agriculture, energy, mining and conservation, ...
Hudson Williams says he’s writing a film but ‘getting tripped up’ over a “Heated Rivalry” parallel (exclusive)
The actor, who’s written multiple short films, talks about getting in his head about a character who’s an ex-hockey player.
Rochester colleges expand mental health workforce training amid shortages
Rochester colleges like Nazareth and St. John Fisher expand mental health training programs to address workforce shortages and improve community access.
25 Shortfalls Cruise Entertainment Teams See on Formal Evenings Among Guests on Almost Every Voyage
Formal night is supposed to be the glamorous peak of a cruise: gowns on the staircase, blazers at the bar, photographers waiting under a chandelier. Then the lobby fills up, and the same slip-ups show up sail after sail. A bathing suit at the dining room door. A perfect gown with beach flip-flops peeking out ...
NWTI, state office to host industry training luncheon on Thursday
Northwest Technical Institute (NWTI) in Springdale said Tuesday (Oct. 20) that it and the Arkansas Office of Skills Development will jointly host an event Thursday (Oct. 22) on short-term industry...
Hamden business owner says his shop has been broken into twice in a week
HAMDEN, Conn. (WTNH) — A Hamden business owner says his smoke shop was broken into twice in the last week, shorting him thousands of dollars. The owner of the Smoke Gallery says surveillance video …
Sphere Entertainment jumps as investors appear to bet on a short-covering rebound
Sphere Entertainment Co. (SPHR) is up 9.4% today. Here is some analysis on what might have caused this price movement.Analysis: There does not appear to be a single fresh company announcement driving Tuesday’s move, so the rally looks most likely tied to short covering and bargain hunting after a steep pullback earlier this month. Investors may also be revisiting Sphere’s underlying growth story, including strong performance from its Las Vegas ve
Former Netflix and Tubi Producer To Launch Shortee, A Vertical Streaming and Gaming App
Nelson Moses Lassiter, an Emmy-nominated producer known for his work on Netflix’s major international hits and for creating original films at Tubi, is launching his venture. Shortee, a mobile entertai...
Meeting Big Challenges with Big Ideas, Health Centers Pitch ‘Y Combinator-Style’ Solutions for Local Healthcare
With challenges looming like Medicaid cuts, workforce shortages, and resource gaps, health centers across the United States are being asked to do more with less, and it would be easy to picture a g…
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) Obviously, the big news of the week (if not month) is Netflix letting go of executive Bela Bajaria, their chief content officer. I’m going to cover this in my next “Most Important Story of the Week”, but I also have a data angle that I might publish before then. (Too much news this month, am I right?) But first, I have a bonus Streaming Ratings Report issue for you. Today’s article is a bit of a grab bag of topics I’ve wanted to write about, but couldn’t squeeze into the (already very long) Streaming Ratings Report issues. The NFL drives huge viewership numbers, so I’ve got a look at that. A whole bunch of TV shows returned to the traditional broadcast channels, and I have thoughts. And I have a quick look at adult animated shows, since a couple flopped earlier this month. Plus, the Emmys aired earlier this month, and we got some fun data bout how popular those nominated shows are and why I’m not optimistic about the show’s future. That’s a lot, so let’s dive right in. Subscribe Sports - Football Remains the King of Streaming Over the last two Streaming Ratings Reports, I sorta ignored the biggest “show” each week. (I was tempted to say “piece of content” but I know that word bugs folks.) In my database, I call these “specials”, and they sure are special. I speak of the king of American TV: football!!! Streaming football is back and, unlike some other sports, it routinely dominates all viewership. Probably the best way to show this is to update one of my favorite graphics, the “40 Million Hour Club”1 since football games have joined that club each week they’ve premiered: (Yep, that’s the data through 28-Sep, the first four weeks of the season. As a reminder, I take the average minute audience provided by Nielsen, factor out a percentage of linear TV viewers in home markets, and multiply the run time by the three-hour runtime.) Again, the numbers are huge. Let’s start with Netflix. They grabbed the rights to the bonus opening week NFL Kickoff game (which was on YouTube last year) and it put up 18.5 million viewers on average. That nets out to 47.4 million hours of estimated streaming hours. That’s good! Though, notably, it’s well below Christmas Day games from previous years. Compared to other streamers/broadcasters, that’s a bit of a mixed result. On one hand, Netflix’s 18.5 million average is about what YouTube claimed last year, though Nielsen used a bespoke methodology at that time, so I trust this viewership figure more. So that’s good. (The game also shifted from a Friday to a Thursday and that could have helped the viewership as well.) And Netflix continues to best Prime Video in total viewership—as you’ll see next—though Netflix has had much higher-profile (opening week and Christmas Day) games. NBC/Peacock’s opening game the night before averaged 25.1 million average viewers, which is just much bigger than Netflix (or Prime Video). That’s the big caveat to all streaming viewership records: even in success, they can’t compete with traditional linear TV. Previously, I called this my “hierarchy of viewership”, and the data keeps supporting that thesis. The downside for NBC is that this game’s viewership was down compared to previous years, though it may be a counting issue. Here’s a great chart from Sports Media Watch: Indeed, looking at all the opening week games across all windows, Netflix still lags the other primetime windows, but does well compared to the afternoon games: I saw a few folks make the case that streamers still lag traditional broadcasters in reach, and I’d add they really lag compared to “broadcast plus streaming” options like NBC/Peacock, ESPN/Disney+ and CBS/Paramount+. The best data I saw for this came from Samba TV, who charted reach through the 2025 season, and, wow. (Samba TV’s numbers are historically lower than Nielsen’s, so don’t compare their household figures to Nielsen’s hours, but use them as a proxy for the relative reach of the streamers.) The good news is Prime Video is NOT having a down year. Their opening Thursday Night Football game reached 18.6 million viewers (about what Netflix got), but that’s a high for Prime Video on Thursdays and the biggest Thursday night game since 2015. The TNF games the weeks of 21-Sep and 28-Sep continued this streak, getting 43.1 (16.8 million average viewers) and 42.5 total hours (16.6 million average viewers), which were both up from last year. Still, that continues the (typical) season slide: (For those new-to-streaming-football-numbers folks, the viewership declines for Thursday Night Football as the year goes along.) Overall, it appears as if Prime Video is now part of the “football habit”, be it for customers at home or out at places like bars and restaurants. And its viewership is slowly catching up to broadcast. One last chart shows this. Last season, Nielsen started adding out-of-home viewership to the charts, so I try not to do season-to-season comparisons as I did for the first few years. And while Nielsen added Big Data viewing this year, I’m going to compare the last two seasons. Here’s that chart, which shows the season-over-season gains: When it comes to streaming football, we have to keep a few seemingly contradictory thoughts in our head… First, streaming doesn’t come close to matching broadcast’s reach and, thus, viewership. Second, even with that gap, football is still the biggest “thing” on streaming. It’s very, very popular. Third, while some methodology changes have likely helped Prime Video’s numbers, their viewership does appear to be growing. Lastly, yeah, the NFL started the season off poorly, with week one dragging down the numbers in year-over-year comparisons. However—and again, methodology changes may have helped this—the viewership grew compared to previous seasons over the next couple of weeks. So football is still king. On streaming and off. Television - Adult Animation Continues to NOT Make the Streaming Charts In the last two reports, two adult-animated shows whiffed: Netflix’s animated comedy, Dang! Paramount+’s Sega-video-game-inspired fantasy/comedy, Golden Axe. Neither made the charts, but that’s usually the case for animated shows. I mean, here’s literally every animated show that’s made the Nielsen Original charts since the start of 2025: And one of those shows is G_abby’s Dollhouse_! A kids show! The problem with charts like this is that they don’t give you a sense of how many shows miss the charts. Netflix had at least eleven animated flops, including comedies like Big Mouth (a show everyone assumed was popular in the pre-Streaming Ratings Era), Long Story Short, Haunted Hotel, Mating Season, Strip Law, and Alley Cats, and action shows like Blood of Zeus, Castlevania: Nocturne, Devil May Cry, Splinter Cell: Deathwatch, and Tomb Raider: The Legend of Lara Croft. Prime Video had at least six animated misses, two in the same universe (Hazbin Hotel, Helluva Boss), three more comedies (#1 Happy Family USA, The Second Best Hospital in the Galaxy and Kevin) and an action show (The Mighty Nein). Disney+ had MCU and Star Wars animated misses (Eyes of Wakanda, Marvel Zombies, Star Wars: Visions, Star Wars: Maul – Shadow Lord, and Star Wars: Tales of the Underworld) but I can’t tell how many of those shows were geared towards adults. HBO Max had Harley Quinn and Hulu had Solar Opposites. None of these shows made the charts! That’s a lot of misses! If I remade the chart with all those misses, here’s how it would look: Listen, I know the counter arguments. First, it’s cheaper to produce animated shows than live action. Fair enough, but with viewership this low, even that cheaper price point starts to not matter. Second, just look at the acquired charts. Hit animated comedies can generate viewership for years! Shows like The Simpsons, South Park, Family Guy, and Bob’s Burgers do really, really well. Fair enough, but streaming has failed to recreate the dynamic that Fox has mastered so well. That’s a problem for streaming still. Television - Emmys! The annual Emmys broadcast was a few weeks ago, and I’ve got data on how the nominees performed on streaming, plus hot takes on this year’s viewership (and the big news about the broadcast’s, er, streamcast’s future). We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… The top Emmy-nominated shows… ...why I’m worried about the future of the Emmys telecast... ...the pros of broadcast television ...and the cons... ...please subscribe! We can only keep doing this great work with your support. Coming Soon! Next week, we’ve got a double issue (since the weeks of 21-Sep and 28-Sep were so light), but then I’ll be all caught up after a deluge of streaming ratings data from Nielsen in September. In the meantime, I’m going to try to take a look at Netflix’s stats to explain why Bela Bajaria is out. But even light weeks give me plenty to talk about. The Great British Baking Show returns to Netflix and Apple TV has a buzzy new star-studded comedy. And Netflix has a controversial-but-appears-to-be-popular new game show. Plus big films like Toy Story 5, Backrooms and The Breadwinner head to streaming. And is East of Eden a hit? After that, The Kardashians return to Hulu, Prime Video has a reboot of Carrie, and Netflix has a horror show, Below. Disclosure Day heads to Peacock, The Housemaid heads to HBO Max_,_ a new Ben Affleck film, Animals, lands on Netflix, and Apple TV has a new John Cena action film based on, checks notes, Hasbro’s Matchbox toys. Okay. Longterm, some interesting TV production news. HBO Max ordered How to Survive Without Me, starring Kaley Cuoco, Joshua Jackson, Ray Romano and Rita Wilson, for fifteen episodes! I love that some streamers (or, at least, HBO Max) are finally ordering TV shows with more than eight episodes. In less wise production news, Landman does not appear to be coming back this year, since it just started production. Frankly, I don’t love this (get your shows out on time) but Landman is so popular, I doubt it matters.
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Can AI solve our mental health access problem?
Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
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Plague is stoppable. The next health threat may not be.
The word plague is scary. Centuries ago, the Black Death killed tens of millions of people and changed the course of history. Plague still kills people around the world, and the word still evokes catastrophe. New fears of plague surfaced on October 2, when a lab worker in Irkutsk, Russia, died of pneumonia. Reports suggested that she may have contracted plague through a laboratory exposure. Understandably, people were alarmed. On October 7, the World Health Organization called for the Russian Federation to be transparent about what it knew. WHO issued an update on October 10. Russian authorities told WHO that testing did not identify plague and that investigations found no laboratory release. WHO said confidence in its risk assessment remained limited and that further clinical, epidemiological, and laboratory information was needed. The cause of the woman’s illness remains unknown, and WHO says a laboratory-associated exposure cannot be excluded. As I explained in a recent interview, this incident is very unlikely to lead to a pandemic. Plague is still active, including in the U.S., which sees a handful of cases each year. But it is a disease we can prevent and treat. Plague doesn’t spread readily, and it responds to antibiotics. The standard response—isolate the patient, trace contacts, and administer antibiotics and monitor for symptoms among contacts for seven days—works. We have even planned for antibiotic-resistant plague. At CDC, we developed a playbook for antibiotic-resistant plague and ran exercises on how to find and stop it. CDC guidance anticipates engineered resistance and recommends two different classes of antibiotics if there’s reason to suspect it. There is no evidence that resistance—or even confirmed plague—is involved in this incident. We have thought through how to address plague, but we’re not ready for the next major health threat that emerges from a lab or from animal spillover to humans. What should concern people is how little stands between the world and the next pathogen to get loose. Since peak COVID, epidemic preparedness has fallen off the political agenda, but the risk is greater than ever. More labs handle dangerous pathogens with more ways to make those pathogens deadlier, and more people live and work in close contact with animals. There are actions we can take now: We need better lab safety in every country. The number of labs that work with dangerous pathogens has increased worldwide over two decades, but there’s no enforceable international standard and no inspection regime; the Biological Weapons Convention, fifty years on, still has no verification mechanism. Labs that study dangerous germs need strong safety systems, training, and oversight. We need to reduce spillover events. Three in four emerging human infections start in animals, and there’s a lot we can do to reduce spillover risk, but the world is doing very little of it. Live-animal markets are one place to act. For instance, closing live-bird markets helped control H7N9 influenza in China. Reducing opportunities for people to encounter infected animals can prevent infections before they become outbreaks. In The Formula for Better Health, which is now out in paperback along with the hardcover, Kindle, and audiobook versions, I argue that labs do too many risky experiments with too many dangerous pathogens in too many institutions, and that every experiment should first answer one basic question: Is the likely benefit greater than the risk? Spillover risk can also be reduced: In the book, I discuss how Brazil cut its deforestation rate by 70% from 2004 to 2012 while increasing agricultural production and reducing risky contact between people and forest animals. We need faster detection and response. Delay costs lives. Strong surveillance, good labs, and trained responders can turn a single case into a small cluster of infections instead of an epidemic. Resolve to Save Lives has worked for nearly a decade to help countries find and stop outbreaks faster. The 7-1-7 target, which Resolve to Save Lives developed with partners in Uganda and elsewhere, provides a clear standard: detect within 7 days, notify within 1 day and complete all essential early response actions within 7 days. In Uganda’s Kasese district, for example, a review of a measles outbreak using 7-1-7 identified shortages of accessible funds, fuel, and vehicles that delayed the response. District leaders established funds they could access quickly. When, a few months later, the Crimean-Congo hemorrhagic fever appeared, the district detected it in three days, notified authorities in one, and mounted an effective response in two. Only one case was confirmed, and the patient survived. Because funds were available before the emergency, authorities were prepared and able to act. This kind of preparation can’t wait until the next crisis begins. Plague is an old disease we that know how to stop. The next threat may be harder to recognize, harder to treat, and easier to spread. Safer labs, less risk of spillover, and faster outbreak detection and response need sustained investment now, while we still have time to prevent the next emergency. Share
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Speaker: Danielle Williams, Ph.D. Title: Computational Models as Liminal Models in Neuroscience Short Bio: Danielle is a philosopher of mind and science whose research examines the role of computation in our theories and explanations of the mind and brain. Her work brings together philosophy of mind, cognitive science, neuroscience, and artificial intelligence, with particular interests in computational theories of mind, computational modeling, and the relationship between different explanatory and modeling strategies in the mind sciences. She also investigates philosophical questions raised by contemporary AI, including the nature of opacity in computational models and its implications for explanation in cognitive science. Alongside this work, she is a historian of cognitive science and AI, examining how the development of computer technology has shaped our conceptions and models of the mind and brain. Abstract: Scientists rely on various types of models to make sense of their phenomena of interest. This has led philosophers to take up a range of semantic, ontological, and epistemological questions about how models represent, what kinds of things models are, what we learn from models, and how they explain. One type of model that has been of particular interest to philosophers is the computational model. Computational models are commonplace in the mind sciences in general and neuroscience in particular, with many asking how computational modeling can help us understand the nervous system and its contribution to cognition and behavior. These questions are particularly pressing in neuroscience, as computational models have become a dominant framework for making sense of cognition. Thus, old questions about model-based science have re-emerged within the context of neuroscience, with many philosophers looking to established frameworks to understand how computational models represent, explain, and contribute to theory development in neuroscience. These established frameworks, however, characterize computational models as a homogeneous type which cannot capture actual modeling practices in neuroscience. In this talk, I argue that computational models in neuroscience are best typed as a kind of liminal model, a type I introduce to capture how the status of a computational model is determined by the epistemic context in which it is deployed. Indeed, the category to which a computational model belongs shifts according to the role it plays within a given context. ASL-English interpreters have been requested. Light refreshments will be provided.
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