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Call of Duty: Modern Warfare 4 PC Performance Analysis
Since I had pre-ordered Call of Duty: Modern Warfare 4, I benchmarked its campaign to provide you with a day-1 PC Performance Analysis.
Premiums for Employer-Sponsored Family Health Coverage Top $28,000 in 2026
Workers are paying on average $7,471 annually toward the premiums for employer-sponsored family health benefits this year, which is up 9%, or $621, from 2025, according to KFF’s annual benchmark health benefits survey of large and small employers.
Workers Now Pay $7,471 on Average Annually for Family Health Coverage, Up 9% or $621 from 2025, Benchmark Employer Health Benefits Survey Finds
Workers are paying on average$7,471 annually toward the premiums for employer-sponsored family health benefits this year, which is up 9%, or $621, from 2025, per KFF’s annual benchmark health benefits survey of large and small employers. The burden of family premiums hits particularly hard for workers at small firms with less than 200 workers.
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.
Human-Animal Bond Reaches New High as the Science on the Health Benefits of Pets Goes Mainstream
/PRNewswire/ -- The Human Animal Bond Research Institute (HABRI) today announced the results of its new benchmark survey of Americans showing that the health...
Is it true that … you need to take 10,000 steps a day to be healthy?
The fitness benchmark has surprisingly unscientific origins – yet it does contain more than a kernel of truth
Weekly Recap: Open-science AV partnership and benchmark performance pledge
Valeo SE (EURONEXT:FR) is partnering with KE:SAI to co-develop an open-science autonomous driving system for general vehicles, aiming to match and surpass major public driving benchmarks while committing to reproducible AI research and openly publishing results.Previous Week RecapThis is an AI-gene…
He Wants AI Benchmarking To Be a Science, Not a Dark Art
Moritz Hardt, author of a new book on benchmarks, believes these “standardized tests” for artificial intelligence are fundamentally broken — but not unfixable.
Redditor runs FurMark on NVIDIA RTX Spark laptop at a store, nearly gets kicked out
A Redditor installed FurMark on an ASUS ProArt P16 RTX Spark display laptop in France. The 1080p benchmark ran before the user was nearly kicked out of the store.
iPhone Duo's A20 Pro Chip Benchmarked Ahead of Launch
The iPhone Duo unsurprisingly offers performance similar to the iPhone 18 Pro, according to an early iPhone Duo benchmark shared on Geekbench 7. Both devices are equipped with Apple's 2nm A20 Pro chip with a 6-core CPU and a 7-core GPU. The benchmarked iPhone Duo (iPhone19,4) is running iOS 27.1, as expected, and it had a single-core score of 3986 and a multi-core score of 11067. That's in line with multiple benchmarks collected from iPhone 18 Pro and iPhone 18 Pro Max models.
New Payor Transparency Rules: Why Hospitals Should Pay Attention
New payor transparency rules could reshape hospital pricing, managed care contracting, competitive benchmarking, and site-of-care strategy.
Clive Barker's Hellraiser: Revival PC Performance Analysis
Powered by Unreal Engine 5.6.1.0, it's time to benchmark Clive Barker's Hellraiser: Revival and examine its performance on PC.
Netflix’s Summer of Bummer
(Welcome to the Entertainment Strategy Guy, a newsletter on the entertainment industry and business strategy. I write a weekly Streaming Ratings Report and a bi-weekly strategy column, along with occasional deep dives into other topics, like today’s article. Please subscribe.) Today, I’ve got a guest post from longtime friend of the newsletter, Brandon Katz. I’ve been trying to find time all summer to look at Netflix’s straight-to-streaming film slate, but between a pre-planned trip and Nielsen moving up their streaming data timeline, I’ve been swamped. So I reached out to Brandon, one of my favorite fellow analysts, to write about it. And I feel like he captured the tone and data focus of the newsletter perfectly. You can connect with Brandon on LinkedIn and Twitter. Enjoy! Don’t worry, folks. You’re in safe hands with me. To ease any anxieties about an interloper in ESG’s domain, I’ll start by stealing one of his best framing devices: headlines. Box office flops are covered in the same way the Teenage Mutant Ninja Turtles eye pizza: with cartoonish insatiability. I would know. I’m a former reporter who wrote nearly identical headlines: Yet, intentionally or not, streaming misses get treated with kids gloves, filtered through a rosy lens: In the last six years, U.S. streaming viewership has become nearly as transparent as the box office, but the data often arrives weeks later, in a number of less intuitive metrics, and with nary an official budget to be found. This plays into the Wall Street-whispering narratives major streamers have been cultivating for years. They get a pass in a way that theatrical studios simply don’t. Subscribe Netflix’s Summer Slate Not a single Netflix original film released between May and August this year opened above 20 million U.S. TV hours, per Nielsen. That benchmark usually serves as the EntStrategyGuy’s floor for a streaming hit on Netflix. None of these films enjoyed a hit-cementing single week of 20-plus million hours during their runs. Netflix’s two best films were true crime docs, Maternal Instinct (16.5 million hours) and The Crash (19.7 million), the best overall week for a film this summer. Great for most other streamers, but not necessarily what we’re used to seeing from Netflix. For an overly-dramatic comparison, Happy Gilmore 2 opened last summer to around 47 million hours (celebrity cameos, baby!). Zooming out, the picture looks even worse. In 2026, Netflix’s five biggest scripted summer films posted the lowest average (28.6 million hours) and median (28.8 million hours) eight-week viewership totals of the past four years. Now, it’s unrealistic and unfair to expect Netflix to deliver a _KPop Demon Hunters-_sized hit every year. But it’s also not ideal to see the size of their hits shrinking over the last few summers. Let’s quickly run through some of the notable summer misses. Jennifer Lopez and Brett Goldstein’s perfectly fine romcom, Office Romance, fell off the charts after 20 million hours in its first two weeks. Top films usually last for four-plus weeks. Starry titles ideally don’t decay that fast either. Homegrown star Millie Bobby Brown couldn’t prevent the snappy Enola Holmes franchise from diminishing returns. At 14.3 million U.S. hours, the third film posted roughly half of what the first two films did in their first two weeks. What’s on Netflix calculated that per-day global views were down nearly 60% from the second movie. The Whisper Man did okay with more than 36 million hours over its first four weeks. But I expected a little more juice from a film featuring Robert De Niro, Michelle Monaghan, and Adam Scott. Kevin Hart’s Ladies First collected just 11.2 million hours total across two weeks. That’s a big miss for one of Netflix’s go-to stars. Plopping into the same bucket are Sunny Sandler’s Don’t Say Good Luck (10.3 million in two weeks) and John Cena’s Little Brother (17.5 million). Meanwhile, English-language originals Color Book (June 19), In the Hand of Dante (June 24) and Heartstopper Forever (July 17) never sniffed Nielsen’s Top 10 at all, despite needing only 2 to 5 million hours to land in the top ten most weeks. (By my count, eleven English-language scripted original films released between May and August did chart). It’s only fair to mention that Swapped (1-May) posted nearly 42 million U.S. hours over five weeks (the longest Netflix original run this summer) and is currently their eighth most-watched English-language film ever worldwide. Remarkably Bright Creatures (37 million, 4 weeks) and Voicemails for Isabelle (27 million, 4 weeks) weren’t bombs either. Still, overall, I think it’s fair to say this was a quieter summer season than we’re used to seeing from the market-leader. You might not realize that given the language commonly used in public analysis. The Twist Despite the starkly black-and-white tone of the coverage, here’s the twist: when many box office bombs arrive on streaming, their viewership looks a whole lot like many of Netflix’s supposedly successful summer releases. Hmm, where have we heard that one before? Masters of the Universe topped out at $65 million stateside against a $170 million budget_._ I’ll admit, Skeletor ripping sleeveless curls as a gym bro and bodyslamming Adam’s co-workers was funny. But, much to the chagrin of my bank account, my laughter doesn’t launch franchises. Yet the He-Man reboot opened to 19.6 million hours (from a Wednesday five-day opening instead of just one weekend), on par with Hoppers on Disney+ (19.4 million). It put up nearly 47 million hours over its first six weeks, bigger than all of Netflix’s summer releases. Pretty darn healthy and likely to land among the 25 most-watched movies on streaming this year. Sticking with Amazon, The Sheep Detectives just barely broke even at the box office with $133 million worldwide (and only $66 million domestic). I never expected cloven-hooved, cud-chewing mammals to be able to bring me to tears. Yet that’s exactly how I found myself at the end of this surprisingly affecting movie (#NoShame). It was likely the unexpected quality in a family-friendly film that powered its streaming over-performance. Sheep Detectives delivered a solid run for Prime video netting 26.2 million hours! How about franchise IP? Star Wars: The Mandalorian & Grogu had the lowest opening ($81 million) and lowest-grossing Disney-era live-action Star Wars film ($178 million domestic). One day before release, its Heat score (19.8%)—audiences who list their interest as a 7/7—fell behind blockbusters Wicked (21%), Superman (23%), Fantastic Four: First Steps (23%), Michael (23%), Toy Story 5 (26%) and Avatar: Fire and Ash (27%), according to Greenlight Analytics. The urgent enthusiasm just wasn’t there. On streaming, Mando opened to weeks of 12.1 million, 6.1 million and 2.7 million hours (21 million total) over its first three frames. Not the numbers Lucasfilm was hoping for nor the numbers of a streaming hit. But they’re in the same vicinity as some Netflix summer releases with $345 million at the global box office to offset a smidgen of the pain. Final Thoughts So why oh why does this nuance gap exist? Four key reasons: Data Literacy: A “$100 million opening” just makes sense. After decades of box office reporting, even casual movie fans are well-versed in the benchmarks of hits and home runs. There’s an immediate shorthand. But “16.5 million” hours doesn’t land nearly as cleanly. Despite leaps of progress, the streaming ratings era is still in its infancy compared to theatrical. The EntStrategyGuy and I met thanks to a late 2010s group chat of data nerds hungry to find a shred of certainty in nebulous streaming performance. I’m not surprised to see “No. 1 on Netflix” still getting misconstrued out in the wild. Timing: By Friday morning, we have the box office’s Thursday night previews totaled, allowing us to better project the weekend’s expectations. From there, ticket sales are reported daily. By Sunday, the film’s fate is usually finalized, at least in terms of public perception. In streaming, Nielsen recently improved its delay to…two weeks. The medium isn’t forced to contend with instant gratification as harshly. Financials: Theatrical film budgets are a mere Google away. Profit and loss is calculated in the cold and unforgiving naked light of day for all to see. But finding the vast majority of streaming exclusive movie budgets requires the forensic investigation skills of a Criminal Minds detective. Also, it’s very difficult to calculate how much revenue/value a straight-to-streaming films provides a streamer. This is why there are so few stories about streaming original movies “losing X amount of money”. On top of that, marketing budgets are far greater for theatrical movies than streaming exclusive movies, which leads to earlier and better awareness. This then translates to a wider pool of potential interest. Narrative Control: Netflix is the only major streaming service to publish weekly first-hand viewership data and annual engagement reports. Naturally, some headlines borrow their first-hand framing**.** Other streamers benefit from their comparative lack of transparency. They may occasionally announce vague performance platitudes such as Apple TV’s Mayday becoming its “biggest film debut on the platform to date over its first 18 days,” and that it ranked No. 1 with left-handed viewers in its first weekend. (Fine, I made up that second one). But, for the most part, they keep first-hand viewership shrouded in mystery to avoid bad press. Especially for outlets that demand multiple articles per day from their writers, it’s an easy (and understandable) way for some reporters to hit their daily article quota by just repeating what the streamers have told them. TL:DR version: Streaming-exclusive movies are asked to do different things than theatrical movies, but that doesn’t mean they should escape judgment. Both still need to draw enough eyeballs to justify their cost within the proper performance contexts. The more cleanly we can inject a little nuance into streaming analysis, the better we’ll understand the audience and content trends that drive this industry. And that’s what all of this is really about: knowing what audiences actually want! Brandon Katz is the Director of Insights & Content Strategy at Greenlight Analytics where he focuses on evaluating the ever-fluid media landscape to unearth understanding, opportunity and value. Greenlight Analytics is the entertainment intelligence consulting company redefining how Hollywood finds, understands, and activates audiences. Prior to joining Greenlight Analytics, he served as the senior entertainment industry strategist at Parrot Analytics, and as a full-time entertainment industry reporter covering the Xs and Os of Hollywood, most notably with TheWrap and the Observer.
Data & Business Process Services Stocks Q2 Results: Benchmarking SS&C (NASDAQ:SSNC)
The end of the earnings season is always a good time to take a step back and see who shined (and who didn’t). Let’s take a look at how data & business process services stocks fared in Q2, starting with SS&C (NASDAQ:SSNC).
Lower US Treasury yield view persists despite biggest quarterly surge since 1994
US Treasury yields are forecast to fall over coming months, according to fixed income strategists in a Reuters poll who clung to their long-held view despite the benchmark 10-year yield recording its biggest quarterly jump since 1994.
Gears of War: E-Day GPU Benchmark: 44 GPUs Tested
We tested 44 GPUs in Gears of War: E-Day at 1080p, 1440p, and 4K to see what it takes to run the game well, how 8GB cards...
This week in health care policy: September 28 – October 2, 2026
Drug pricing CMS finalizes GLOBE pilot. On September 30, CMS finalized the Global Benchmark for Efficient Drug Pricing model, a program that ties Part B drug rebates to prices paid in 19 countries. It will cover physician-administered drugs in randomly selected communities throughout the US, representing about 25 percent of Part B beneficiaries. It runs […]
Tencent Music Entertainment Group (NYSE:TME) Shifts To Hong Kong In Global Index Tracking
Tencent Music Entertainment Group (NYSE:TME) was added to the FTSE All-World Index through its SEHK listing while its NYSE line was removed. The index action shifts Tencent Music Entertainment Group’s inclusion from the US listing to its Hong Kong traded shares for benchmark purposes. This FTSE All-World rotation from NYSE to SEHK for Tencent Music Entertainment Group is one factor to weigh against the rest of our work. Check out 3 other big wins that Tencent Music Entertainment Group...
Governor Moore Convenes Maryland Innovation Summit, Releases New Maryland Business AI Benchmark
Governor Moore Convenes Maryland Innovation Summit, Releases New Maryland Business AI Benchmark
Gears of War: E-Day Performance Benchmark Review
Gears of War: E-Day is a prequel set 14 years before the original game. Built on Unreal Engine 5 with RTX Mega Geometry, DLSS 4.5, FSR 4, XeSS, and ray tracing, we examine graphics quality, VRAM usage, upscaling options, and how it performs across a wide range of modern GPUs.
New APTA Survey Reveals Disconnect Between Americans' Long-Term Health Goals and How They Address Movement
/PRNewswire/ -- Today, in recognition of National Physical Therapy Month, the American Physical Therapy Association (APTA) released findings from a benchmark...
AI safety measures widely backed by most Americans, poll finds
WASHINGTON — A vast majority of Americans favor requiring artificial intelligence companies to meet independent safety benchmarks for their models, even if those standards mean slowing development of the technology,
Vinik Sports Group, Cabot Citrus Farms unveil new premium hospitality destination at Benchmark International Arena
Multi-million-dollar transformation creates elevated Cabot Club experience for hockey, concerts and live events
Global benchmarks are mixed despite Wall Street's rally finish last week
Global shares are trading mixed Monday after a cooldown in oil prices last week helped U.S. stock close out their first winning week in the last three. Benchmarks are rising
Asian benchmarks are mixed despite Wall Street's rally finish last week
Asian shares are trading mixed Monday after a cooldown in oil prices last week helped U.S. stock close out their first winning week in the last three. Benchmarks rose in
Asian benchmarks are mixed despite Wall Street's rally finish last week
Asian shares are trading mixed Monday after a cooldown in oil prices last week helped U.S. stock close out their first winning week in the last three. Benchmarks rose in
Asian shares trade mixed as oil prices rise
Asian shares are trading mixed Monday after a cooldown in oil prices last week helped U.S. stock close out their first winning week in the last three. Benchmarks rose in
Asian shares trade mixed as oil prices rise
Asian shares are trading mixed Monday after a cooldown in oil prices last week helped U.S. stock close out their first winning week in the last three. Benchmarks rose in
WHO recognizes healthy, innovative cities across the Western Pacific Region
Nine Healthy City champions from across the World Health Organization (WHO) Western Pacific Region received special recognition at an awarding ceremony during the 11th Global Conference of the Alliance for Healthy Cities in Sydney, Australia.The WHO Regional Office for the Western Pacific has presented the Healthy Cities Recognition Awards every two years since 2004 to celebrate innovative and impactful city-led initiatives that improve the health and well-being of urban populations.Eight cities received awards for their best practices, and one National Forum received the WHO Regional Director’s Special Award in recognition of its long-standing leadership in advancing the Healthy Cities movement.Awardees were selected through a rigorous evaluation process and recognized for demonstrating measurable, innovative and sustainable approaches to health challenges faced by cities.Remarking on the significance of the awards, Dr Huong Tran, Director of the Division of Health Promotion, Disease Prevention and Control at the Regional Office, said that, “There is no single blueprint for a healthy city. What works in one context may need to be adapted to another. But by sharing our experiences, including what worked, what was challenging, and what we learnt along the way, we can help one another move faster and further.”Award-winning cities and initiativesThis year's awards recognize outstanding city-led initiatives that have demonstrated measurable impact, innovation and sustainability in advancing health initiatives for urban populations.Baguio City (Philippines) – Data for equity: informing action, improving healthDr Donnabel Tubera-Panes, City Epidemiologist – Medical Officer IV of the Health Services Office accepted the award on behalf of Baguio City. Photo credit: Maria BoyadgisBaguio City was recognized for developing a pioneering Livability Index that provides village-level data across all 128 barangays (villages), enabling evidence-based planning and more equitable allocation of resources. By integrating community participation and institutionalizing the index within city planning and budgeting processes, the city has demonstrated how data can drive action to reduce health and social inequities.Eunpyeong-gu (Seoul, Republic of Korea) – Communities living in vulnerable situations: advancing health equity through community engagementVice Mayor Honourable Ahn Hyoung Jun accepted the award on behalf of Eunpyeong-gu. Photo credit: Maria BoyadgisEunpyeong-gu was recognized for its inclusive health equity programme supporting vulnerable populations, including isolated older adults and LGBTQI+ communities. Through peer-led community networks, social cooperatives and inclusive service design, the district has strengthened access to care, promoted social inclusion and advanced community ownership of health initiatives.Madiun City (Indonesia) – Adapting and mitigating climate-related crises: building heat resilience and preventing flood-related health risksMs Yuni Dwi Purwani, Secretary General of the National Forum for Indonesian Healthy Cities/Regencies, accepted the award on behalf of Madiun City. Photo credit: Maria BoyadgisMadiun City was recognized for its SUPERMIE (Sustainable Public Environmental Management Initiative) – a community-based environmental governance model that addresses urban flooding, climate resilience and environmental sustainability. The initiative contributed to reductions in flood-prone areas, greenhouse gas emissions, diarrhoeal disease and dengue fever, while at the same time improving food security through neighbourhood food gardens. The programme has already been replicated in 15 cities and regencies across Indonesia.Macao Special Administrative Region (China) – Protecting communities from tobacco and alcohol-related harms: tackling commercial drivers of riskDr LO Iek Long, Director of the Health Bureau, Macao SAR, China accepted the award on behalf of Macao. Photo credit: Maria BoyadgisMacao SAR was recognized for implementing a comprehensive and integrated approach to tobacco and alcohol control. Through legislation, monitoring systems, enforcement, public awareness campaigns and multisectoral partnerships, the programme has strengthened protection against major risk factors for noncommunicable diseases and established a model that is highly transferable to other urban settings.Phnom Penh Capital City (Cambodia) – Safe, active and health-enabling transport: advancing health and reducing inequities in urban mobilityH.E. Khoung Sreng, Governor of Phnom Penh Capital City accepted the award on behalf of Phnom Penh Capital City. Photo credit: Maria BoyadgisPhnom Penh received the award for its Safe School Zone Project, which combines infrastructure improvements, speed management, enforcement, education and stakeholder engagement to protect children and communities. The initiative has demonstrated measurable reductions in crashes and vehicle speeds while providing a scalable model for safer urban mobility across the country.Quezon City (Philippines) – Transforming food environments: addressing policy, market and urban design drivers of health and nutritionMs Jirah Asa M. Sideco, Chief of the Nutrition Section of the Quezon City Health Department accepted the award on behalf of Quezon City. Photo credit: Maria BoyadgisQuezon City was recognized for its public food procurement policy that promotes healthier diets through government-run institutions and feeding programmes. Supported by strong policy frameworks and monitoring systems, the initiative improves access to nutritious food for vulnerable populations while serving as a benchmark for other local governments.Samarinda City (Indonesia) – Preventing violence and promoting gender equality: transforming urban systems, norms and spaces Mrs Rinda Wahyuni, Chairperson of Samarinda Healthy City Forum accepted the award on behalf of Samarinda City. Photo credit: Maria BoyadgisSamarinda City was recognized for its multisectoral approach to preventing violence and advancing gender equality. Responding to local evidence on violence against women and children, the city has positioned violence prevention as a public health priority, bringing together health services, schools, social welfare institutions, law enforcement, civil society and community leaders to strengthen prevention, reporting, survivor-centred support and legal protection. Through inclusive policies, community engagement and sustainable city financing, Samarinda has developed a locally owned and scalable model for creating safer, more equitable and inclusive urban communities.Singapore – Age-friendly cities: transforming systems to bridge generations for a healthier tomorrowDr Tan Weng Mooi, Director, Integrated Health Promotion, accepted the award on behalf of Singapore. Photo credit: Maria BoyadgisSingapore received recognition for its Healthy Precinct Framework, which translates national healthy ageing priorities into neighbourhood-level action. The initiative integrates preventive care, social support, service coordination and age-friendly environments while actively engaging older persons, caregivers and community organizations. Its expansion across multiple precincts demonstrates its scalability and long-term sustainability.WHO Regional Director’s Special AwardNational Forum for Indonesian Healthy Cities/Regencies (Indonesia)Ms Yuni Dwi Purwani, Secretary General of the National Forum for Indonesian Healthy Cities/Regencies, accepted the award on behalf of the National Forum. Photo credit: Maria BoyadgisThe WHO Regional Director's Special Award was presented to the National Forum for Indonesian Healthy Cities/Regencies (FKKSI) in recognition of its long-standing leadership in advancing the Healthy Cities movement across Indonesia. As a national platform bringing together local governments, communities, academia and civil society, FKKSI has played a critical role in promoting collaboration, capacity-building, knowledge exchange and advocacy. Its efforts have strengthened and institutionalized healthy urban development nationwide and established a model for multisectoral engagement in the Region. Healthy Cities for allThe 2026 Healthy Cities Recognition Awards highlight how cities can lead transformative action on health by addressing climate change, inequity, ageing, nutrition, mobility, social inclusion, violence prevention and the commercial determinants of health. The recognized initiatives demonstrate that local governments, communities and partners can work together to create healthier, safer and more resilient urban environments for everyone. As urban populations continue to grow across the Western Pacific Region, WHO remains committed to supporting cities in advancing health, equity and sustainable development through the Healthy Cities approach and the Alliance for Healthy Cities. ###Note to editorsSince 2004, the WHO Healthy Cities Recognition Awards have provided a platform for cities to share innovative and effective approaches to improving health and well-being. The awards recognize achievements in areas including climate resilience, healthy ageing, health equity, gender equality, tobacco and alcohol control, urban mobility, nutrition and data-driven governance. In 2026, more than 150 submissions were received from cities and local governments across the Western Pacific Region. The titles and affiliations of awardees are presented as they were at the time the photo was taken.______________For more information, or to arrange media interviews, contact: WHO Regional Office for the Western Pacific: wprocom@who.int
Silent Hill: Townfall compared on RTX 5080 and PS5 Pro with DLSS 4.5 and PSSR 2 in 4K
An extended comparative benchmarking of psychological horror films has been published online. Silent Hill: Townfall, in which the quality of graphics and level...
Silent Hill: Townfall Benchmarks & PC Performance Analysis
Powered by Unreal Engine 5, it's time to benchmark Silent Hill: Townfall and examine its performance on PC.
Ace Combat 8 Wings of Theve requires at least 32GB of RAM to play comfortably.
We conducted detailed benchmarking of the Ace Combat 8: Wings of Theve flight simulator to measure the actual consumption of personal...
RTX 5080 and RX 7900 XTX showed almost identical FPS in Ace Combat 8 in 4K
We have carried out detailed benchmarking of the latest NVIDIA GeForce RTX series graphics accelerators and AMD Radeon RX in the flight simulator Ace Combat...
Ace Combat 8: Benchmarks of Graphics Cards and Processors
We ran extensive benchmarks of Ace Combat 8: Wings of Theve on graphics cards ranging from the RTX 3060 to the RTX 5090 and the RX 6750 XT to the RX 9070 XT in conjunction with CPU AMD and...
Talk of US export ban on diesel deepens US crude futures' discount to global benchmark
Washington's talk of a possible ban on diesel exports is widening the gap between US crude oil futures and the global Brent benchmark, a signal that markets expect US refiners to process less crude oil if their diesel output gets stuck at home.
Benchmark mortgage rate tops 7 percent
Welcome to The Hill’s Business & Economy newsletter{beacon} Business & Economy Business & Economy The Big Story Benchmark mortgage rate tops 7 percent The benchma…
Global benchmarks mostly rise after Wall Street's rally
Global shares are mostly higher, as the market mood turned positive from the overnight rally on Wall Street. Benchmarks are trading higher in early Tuesday trading in France, Germany and
The state of pediatric restorative dentistry
Nearly every pediatric clinician surveyed this year expects zirconia crown use to rise, but far fewer are placing them today. A 2026 benchmark report
AMD Ryzen 7 9800X3D Memory Performance Scaling
The AMD Ryzen 7 9800X3D set new benchmarks in gaming performance with its impressive 96 MB L3 V-Cache. But how does memory latency factor into this powerhouse's performance? Our latest analysis dives deep into memory performance scaling, and could change how gamers approach system optimization.
The M5 Ultra Mac Studio tears through our benchmark tests
So much power, we’re gonna keep cooking on this one.
Already-operating battery storage projects are key to making the business case for the next assets bankable
Existing assets provide crucial benchmarks for sizing and other crucial battery storage design decisions, writes Ashutosh Vats at 3E.
Snapdragon 8 Elite Extreme Gen 6’s Engineering Prototype Video Surfaces Before Official Launch, Showing A New Packaging, Improved Benchmark Scores & More
An engineering prototype of Qualcomm's Snapdragon 8 Elite Extreme Gen 6 has leaked ahead of launch, revealing a new SoC packaging design, higher benchmark scores, and key hardware upgrades
Vietnam joins FTSE Russell emerging market benchmark
HANOI, Sept 21 (Reuters) - Vietnamese stocks were added to FTSE Russell's emerging market indexes on Monday, marking a milestone for the country's stock market after years of reforms aimed
‘Backrooms’ Surpasses $400 Million at Global Box Office
“Backrooms” has hit $400 million at the global box office, a remarkable benchmark for the low-budget indie sensation. After nearly four months on the big screen, “Backrooms” has generated $197.5 milli...
Federal Reserve rate hike reflects new world of sticky inflation and faster growth
President Donald Trump has renewed his attacks on the Federal Reserve after it hiked its benchmark interest rate Wednesday, but the Fed matters less than broader economic trends when it comes to longer-term borrowing costs, economists say.
Fed rate hike reflects new world of sticky inflation, faster growth
President Donald Trump has renewed his attacks on the Federal Reserve after it hiked its benchmark interest rate Wednesday, but the Fed matters less than broader economic trends when it comes to longer-term borrowing costs, economists say
China keeps benchmark lending rates unchanged for 16th month in September
China kept its benchmark lending rates unchanged for the 16th straight month on Sunday, in line with market expectations.
M5 Ultra and M6 Chip Benchmark Results Reveal Graphics Performance
The first GPU benchmark results for Apple's new M5 Ultra and M6 chips have surfaced, ahead of the new Mac Studio and Mac mini models launching on Tuesday. If you are interested in CPU results, we shared those earlier this week. So far, the M5 Ultra has achieved up to a 59% higher Metal score (366,744) on Geekbench 7 compared to the M3 Ultra's average score of 230,420, and the chip's OpenCL score is roughly in line with NVIDIA's GeForce RTX 4080.
National Legal and Business Media Cover Arrival of Chicago Partner Andrew Barrios
Law360, Global Legal Chronicle, Corporate Counsel Business Journal, Benchmark Litigation and Pulse 2.0 were among the legal and business news outlets that covered the arrival of McGuireWoods partner Andrew Barrios,...
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
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Chartbook 464 The"China squeeze" - the politics of development counterfactuals.
There are three types of China shock discourse. Each has a distinct temporal register. China shock 1.0 was a phrase coined by US academics in the 2010s to retrospectively summarize the impact on particular US labour markets, which they identified through painstaking empirical investigation. China shock 2.0 on everyone’s lips in Europe in the mid 2020s is an alarm raised by trans-Atlantic policy thinkers warning of a coming disaster which they project onto Europe’s ailing car industry. The third type of China shock discourse - commonly known as the “China squeeze” launched most recently by Indian economists Shoumitro Chatterjee and Arvind Subramanian in the pages of Foreign Affairs - mobilizes a different, more speculative temporal register: the counterfactual realm of alternate history. The China squeeze argument asks not what has happened, or what is going to happen, but what might have been. Specifically, what options for economic development might have been open to Global South economies like India or the economies of sub-Saharan Africa, had it not been for China’s spectacular industrialization since the late 1990s and the persistent competitiveness of China’s manufacture in low-skilled segments. Chatterjee and Subramanian’s argument is that whilst China has built a powerful position in electrical machinery and other high-skill manufacturing, which is now delivering China Shock 2.0, it also continues to run a trade surplus in low-skill, labour-intensive manufacturing. This surplus is large and has remained largely unchanged in fifteen years. Indeed, in the 2010s, in trade with low-income countries, China’s surplus rapidly increased. On their reading, this squeezes low-income producers in low-skill sectors in which they “should” be able to compete. Source: “China’s Mercantilist Squeeze on Developing Countries” Shoumitro Chatterjee and Arvind Subramanian. Peterson Institute for International Economics 26-7 May 2026 Chatterjee and Subramanian’s wording is telling: (emphasis mine) The canonical trajectory of economic development in the post-World War II period is for countries to transition from agriculture to manufacturing, first in low-skilled manufacturing and then later on to high-skill manufacturing (Herrendorf, Rogerson, and Valentinyi 2013). At the global level, this implies that high-income countries (HICs) that once specialized in low-skilled activities cede manufacturing export space to poorer countries that come along to occupy that space. In 1965, advanced economies accounted for about 60 percent of global value-added exports in four key low-skilled manufacturing sectors, i.e., apparel, leather, textiles, and footwear. Over time this share declined and began to be occupied first by the East Asian tigers and, beginning in the late 1990s, by China. Today China accounts for about 50 percent of the global market. The question is whether China, with its rising prosperity, is continuing to behave like its richer counterparts and ceding export space to poorer followers or to behave unlike them and continuing to occupy space. In terms of the conceptual maneuvers it performs, of the three China shock discourses, the “China squeeze” argument is by far the most ambitious. After all, it bases its critique of China on a counterfactual imagining of the world. Like any counterfactual argument, to construct this alternative reality it must rely on a heuristic model. To make their argument Chatterjee and Subramanian mobilize two key claims. The first reference point is the actual history of rich-country industrialization, what they refer to as the “canonical path”. The idea is that countries transition from agriculture to light industry and low-skilled manufacturing and from there up the value chain. To make this useful as a standard of comparison and a means to construct a normative standard, they adjust sectoral export shares for two factors: 1. the underlying level of economic development of the exporting country, as measured by national income and 2. the degree to which the sector in question is internationalized. The idea being that for a high income country to have a high export share in low-skill sectors is anomalous and even more so if that particular product is highly internationalized and thus suitable for being “conceded” to lower-income competitors. Where should this process end? Here Chatterjee and Subramanian introduce their second benchmark. Their normative idea of a global development path is one in which, in due course, low-skill manufacturing exports are distributed in proportion to a country’s share in the global labour force. They imagine, in other words, a world of proportional, polycentric global development without much national specialization in low-skill manufacturing. A useful benchmark is that, in labor-intensive manufacturing sectors, a country’s share of world exports should be broadly commensurate with its share of the world’s low-skilled labor endowment. … In the Heckscher-Ohlin model, this would be true if value added per worker and export propensities, i.e., fraction of output that is exported, are the same across countries. It is this pleasant liberal vista of orderly global development, progressing towards a general distribution of low-skilled manufacturing against which they judge reality and conclude that the low-income countries are suffering a “China squeeze”. China is not exiting low-skill sectors fast enough Allowing for its gdp level and the degree of globalization, China’s share of low-skill manufacturing exports is far higher than it “should” be. Relative to population, its low-skill share of exports is far too high. That in turn yields the conclusion that huge numbers of jobs - the authors suggest tens of millions - are being hoarded by China. For the four sectors captured in figure 4, this excess in value-added exports is $110 billion in 2022. For all low-skilled sectors overall captured in appendix figure D.1, excess value-added exports amount to $365 billion. The crowding out of LMIC exports by China is therefore substantial. Both these claims have in common that they are strong structuralisms. The first is that development paths ought to resemble each other. The second is that broadly speaking as far as low-skilled manufacturing is concerned, all countries are alike. If the world currently diverges from the distribution of trade implied by these assumptions, if China is not following the advanced economy path and has huge exports, this must be the result of “policy”, which the authors then put in question as illegitimate or unfair. Thus the naturalistic construction of a canonical path leading to a “level” distribution of low-skilled manufacturing reveals itself to be a polemical tool. From the point of view of China shock polemics, this discourse is no doubt welcome. But as an account of economic history, it must surely be regarded as nothing short of bamboozling. Development paths do not follow a regular, natural sequence. As David Fishman put it aptly, “development is not a queue”. They are always and everywhere, through and through political. Development is not so much a process, as a project. Each such project is more or less well adjusted to its context. As Gerschenkron taught uneven and combined development implies differentiation rather than simple imitation on a single parth. It may be true that Europe and the US exited relatively early from low-skill work. But clearly technology has changed. As C&S themselves point out, China no longer enjoys a low wage environment. Do they really believe that such huge differences can be accounted for either by industrial policy, which as they admit does not operate powerfully in low-skill sectors, or by exchange rate manipulation? What is overwhelmingly dominant in explaining the persistence of “low-skill” manufacturing in China are surely the network effects of complex supply chains. Indeed the label of “low-skill” may miss the point. The degree of globalization which Chatterjee and Subramanian decide to conjure away in their model of the development path - to allow for the fact that Europe and American textiles exporters did not enjoy just-in-time logistics in the 1950s - obscures the point that the networks through which Chinese factories supply the world are themselves key parts of the low-skill industrialization project. China isn’t knocking out 1970s style “cheap”. It has redefined the entire product cycle. C&S’s casual reference to “T-shirts” is telling. In today’s “fast fashion”, there is weight on both terms. Fashion and fast. China’s success in delivering on both at unprecedentedly high speed and at unprecedentedly low cost, isn’t a low-skill operation at all. If we see development trajectories not as the serial repetition of the same, but as distinctive, crafted projects that cumulate in complex agglomerations of skill, technology and labor, then this explains why the assumption that low-skilled manufacturing should follow the distribution of population flies in the face of all experience. As countless cases attest, low-wage, low-income developed economies are not by themselves the makings of a competitive production base. As Leon Liao puts it quite fairly: “International trade has never been allocated according to population. A country’s population gives it no natural claim to an equivalent share of global exports." And the remarkable fact is that Subramanian alone and with co-authors has long been making this point, not to pillory China, as is the case of the tendentious _Foreign Affair_s and Peterson pieces, but to highlight the great counterpart to China’s industrial success, namely India’s relative failure. The much-quoted Foreign Affairs piece showed low-skill export shares only for China, not for India. If you put both countries in the picture as Chaterjee and Subramanian have done on other occasions, the picture looks like this. China is far above the line, but India, almost as large in terms of population, is no less anomalous, just in the opposite direction. As far as global populations are concerned, and the imagining of tens of millions of “squeezed” jobs, it is this Sino-Indian pair that account for a huge share of the action. What this graph reveals is not China’s cheating, but India’s “greatest development failure”, at least this is the argument made Arvind Subramanian, this time in co-authorship with Devesh Kapur, in their major book, A Sixth of Humanity. Independent India’s Development Odyssey which appeared with Oxford University Press earlier this year. In this highly critical work on India’s development, China is repeatedly evoked, not to criticize but as a benchmark against which to illuminate what India’s political economy has not delivered i.e. large-scale formal employment for hundreds of millions of citizens. Kapur and Subramanian leave no doubt, if you want to understand why China, not India was the main driver of manufacturing globalization from the 1980s, don’t look to slots opening up in the division of labour thanks to the liberal mindedness of advanced economies, look to the contrasting political economy of the Maoist PRC and India’s post-independence political economy. As Kapur and Subramian note: The great divergence between China and India did not begin in 1978 with the Deng reforms—it was well underway by then. In one of the great ironies of modern history, the conditions created by Maoism turned out to be a great launching pad for industrial globalization. As the comparison of India and China in 1980 makes clear, to imagine manufacturing would be equally distributed when populations are so differently endowed in terms of educational and nutritional is fanciful. Here is the data from Kapur and Subramanian, data which I explored back at the very beginning of Chartbook. As Kapur and Subramanian insist, the striking fact is that even as India’s growth accelerated from the 1980s, structural change has not. Formal employment in India lags woefully behind. Why? As Kapur and Subramanian note, the path-dependent impact of India’s poor early start was devastating. The list of factors they mention is long and weighty: We argue for a new five-fold explanation applicable at different stages of Indian history: in the initial stages, the legacy of two key pre-1980s policies, the neglect of agriculture and the strangling of the domestic private sector, were critical. The failure of broad-based agricultural growth militated against rural industrialization, which would have been devoted to satisfying local needs that would have necessarily been labour intensive. Then, when the boom came after 1980, the thicket of regulation and broader labour protections that were legislated prematurely in a nascent democracy, underpinned by a deep ideological aversion to private capital in general and big capital specifically, became a barrier to attaining the size that would be necessary to compete globally. Similarly, the reforms after 1991, while wide-ranging, did not quite encompass non-tradeable inputs critical to manufacturing, namely, land, logistics infrastructure and power. A combination of low investment (logistics infrastructure), regulation (land) and the continued monopoly supply by state governments (power), combined with residual socialism, kept costs at 75–100 per cent above efficient supply. These factors were compounded on the supply side by the government, which dominated the formal labour market and established exorbitantly high wages at the lower end of the skill spectrum. This made their employment in the labour-intensive sector by the private sector less attractive. Finally, when the services sector and its exports boomed, wages soared even further via a Dutch Disease effect: one successful tradeable sector killed and cannibalized another less-skilled tradable sector, namely, manufacturing. Thus, scarcity-inducing policies of the planning era, precocious democracy and a Gandhian aversion to size; inefficient, monopolistic supply of critical non-tradable inputs; the behemoth of government; and super-successful skill-intensive services successively undermined India’s unskilled labour employment opportunities. The employment failure seems to have been over-determined. … Less quantifiable but no less important are some of the sociological factors that have undermined manufacturing and factory jobs. Take the impact of the success of the IT sector—located in urban areas with better working conditions, such as air-conditioned offices, good schools for children and attractive entertainment options. It became a more attractive place for engineering and management graduates to flock to. … Consider manufacturing and female employment. Indian society makes it difficult for women to work, reflected in India’s unusually low female labour force participation (elaborated in Chapter XIII). Indian society, in particular the routine harassment of women in public spaces, makes it even more difficult for them to travel long distances to take up work.37 Combined with onerous regulations on creating hostels and dormitories for women—which, in turn, prevents multiple shifts of operation—factories need to be located in areas with a large female labour pool. While firms can take apparel plants to female labour or bring them to the plants by building large-scale dormitories, this raises costs. If there was a case for subsidies to encourage labour-intensive manufacturing, housing subsidies to build dormitories for women workers would be a good place to start. All of this meant, that when opportunities did open up, in part thanks to adjustments in China, India was in no position to take advantage. Here is the account of the 2020s from Kapur and Subramanian. It stands in dizzying contrast to the one-sided, “China squeeze” argument delivered in the Peterson and Foreign Affairs pieces: In 2022, for example, India’s share of global exports in clothing (the canonical labour-intensive sector) was 3.1 per cent compared to 32 per cent for China. This is extremely disappointing because China started vacating export space in this sector after the Global Financial Crisis as its wages started rising; it then vacated further space in the aftermath of geopolitical developments that led investors to seek alternative investment locations. Between its peak and today, China lost about 7.5 percentage points of global export share (worth about $43 billion in 2022); over this period, India’s export share remained stagnant at 3 per cent, while that of Vietnam and Bangladesh rose by 3.5 to 4 percentage points. China’s loss did not translate into any gain for India in terms of low-skill exports and employment. Put differently, when the conditions for dynamism and prosperity were established after 1991, and even more tantalizingly when the global export opportunities opened up, it was as if India was a high-wage economy and only skilled labour could benefit from the dynamism. It had converted its physically abundant unskilled workforce into an economically expensive resource, penalizing its use. It is not my mission here to argue for or against Kapur and Subramanian’s interpretation of India’s development trajectory. The point in this context is simply that the “China squeeze” such as it is can only be understood, in relation to the heavy burden of domestic constraints that Kapur and Subramanian so unsparingly enumerate. I am concentrating here on India because it is the great counterpart to China and the unspoken reference point of the “China squeeze” discourse. But the point is more general. Can anyone with a straight face suggest that the main problems afflicting the development of South Africa or Nigeria, the great centers of mass underemployment in sub-saharan Africa, are cheap competition from China driven by Beijing’s industrial policy and an undervalued RMB? Likewise, is the main problem of the American working-class the threat of Chinese competition or the manifest crisis of social reproduction in the United States itself? The point here, as in the discourse around China shock 2.0 in Europe, is to insist that we should not normalize the world and pathologize China, without actually investigating what is going on in America’s industrial wastelands, the German car industry, or India’s lopsided labour market. One-sided pathologization should be understood for what it is, a polemical gesture. Concepts like China shock and China squeeze are, what Reinhart Koselleck, following Carl Schmitt, would have described as “asymmetrische Kampfbegriffe”, or “asymmetrical counter-concepts” or “asymmetrical combat concepts”. If the function of arguments like the “China squeeze” is to alert elites in the rest of the world of the need to rejuvenate and energize their development projects, so be it. But let us see the resulting discourses as the mobilizing interventions that they are. Interestingly, a significant slice of China’s public sphere - the so-called industrial party - have been obsessed for the last twenty years with precisely these questions of counterfactual economic history. Since the mid 2000s, a massive online discourse developed around an alternative history in which Chinese techno-patriots were asked to imagine how they would proceed if they had the possibility of traveling back in time to transport modern technology into the Ming dynasty (1368-1644) thus averting the “great divergence” of the 18th and 19th centuries. It is a fantasy of course, but also a goad. The point was not directed at Westerners. The alternate history is dramatically nationalist, but few n the West had even heard of it until recently. The adherents of the industrial party are not liberal moralists. They don’t project natural ladders of international development and ask why others don’t cede space. They take the Realpolitik of global economic development for granted. Their pointe was directed against fellow Chinese. The rallying cry of the alternative narrative was this: Now that we are beginning to gain steam and push forward on our national development path, now that we are breaking out of the inferior position assigned to us even in the early years following WTO accession, do not lose yourself in humanistic imaginings about better futures. In the spirit of Deng, they called on China to embrace the hard truth of development. Perhaps one might of the Industrial Party as a cultural trend born out of desire for symbolic revenge against those who had inflicted the century of humiliation. In terms of the various temporalities of the “China shock” - retrospective, prospective and counterfactual - one might think of the Industrial Policy discourse as truly speculative, conjuring up the China shock that never was. Or perhaps one might better think of it as imagining a world in which a powerful China was no shock at all, but simply a defining fact of modern history. I love putting out Chartbook. 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