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Welcome to the Techbrew Ride Home for Monday, December 1st, 2025. I'm Brian McCullough. Today, while Runway releases a new video model, let me break down the big analysis piece that had everyone concern trolling over Nvidia over the weekend. Why doesn't Netflix want you to cast to your TV anymore? And AI means less jobs in consulting, but more jobs in a specific type of construction. Here's what you missed today in the world of tech, Conducting business online can feel a little scary these days, especially with AI creating new opportunities for fraud. In fact, Sailent estimates that AI was behind roughly 20% of the fraud perpetrated in 2024. Spotting bad agentic AI, while allowing good agents to continue with their tasks isn't easy. Thankfully, Momoto Continuous Captcha can spot malicious agents pretending to be people at the point of account creation or registration. Unlike past Captcha solutions, it runs behind scenes with no puzzles. For users, Momoto offers techbrew Ride Home listeners early access with a special price for Momoto Continuous Captcha. Right now, our listeners can purchase a year of Momoto continuous captcha for $5,000, a 20% discount on their lowest price plan. To learn more, head to Momoto AI Ridehome. That's Momoto AI Ridehome. Well, we're back at it. And they're back at it with another new model. Runway has launched Gen 4.5, a new text to video AI model that produces HD videos from written prompts and excels at physics. Gen 4.5 already apparently tops Video Arena's leaderboard, quoting CNBC. Google's VO3 model holds second place on that leaderboard, and OpenAI's Sora 2 Pro model is in seventh place. We managed to outcompete trillion dollar companies with a team of 100 people, Runway CEO Cristobal Valenzuela told CNBC in an interview. You can get to Frontiers just by being extreme, extremely focused and diligent, Valenzuela said. Gen 4.5 was codenamed David in a nod to the biblical story of David and Goliath. The model was an overnight success that took like seven years, he said. It does feel like a very interesting moment in time where the era of efficiency and research is upon us, valenzuela said. We're excited to be able to make sure that AI is not monopolized by two or three companies. Gen 4.5 is rolling out gradually, but it will be available to all of Runway's customers by the end of the week, Valenzuela said. It's the first of several major release releases that the company has in store. It will be available through Runway's platform, its application programming interface, and through some of the company's partners, he said. End quote. Last week when I did that piece about Google maybe shopping its tpus around to outside companies as direct competition to Nvidia's GPUs, I remember thinking why aren't people talking about this? Why isn't this a bigger deal? Well, over the last week it's become a big deal weighing heavily on Nvidia's stock. And over the holiday weekend there was a lot of chatter about this piece from Semianalysis breaking down why people think this is potentially a big deal. The piece is long, so I'll do my best to summarize it for you, but I've also linked to it in the show notes so you can read the whole thing for yourself. Basically, semianalysis argues that Google's latest AI chip system, TPUV7 Ironwood plus a big shift in Google's business model, is the first serious structural challenge to Nvidia's dominance in high end AI hardware. They point out that in AI right now, hardware dominates costs. Buying and running chips is a much bigger line item than paying programmers, for example. That's a big change from the recent Software is eating the world decade because that means whoever can train and run models on cheaper, more efficient infrastructure has a real business advantage. Historically, Nvidia of course has owned this space with its GPUs and CUDA software ecosystem. Now two of the strongest AI models out there, Anthropic's Claude 4.5 Opus and Google's Gemini 3 run mostly on non Nvidia hardware. Google's TPUs and Amazon's Trainium. Google originally built TPUs as internal AI specific chips to avoid doubling its data center footprint when Deep Learning took off around 2013. For years they used it largely to power search and ads for them, and for years they were mostly an internal advantage with only half hearted cloud offerings to others. That is now changing. Google is turning TPUs into true merchant silicon that it sells or rents at scale not only via Google Cloud platform but also as physical systems to third parties, most notably Anthropic and likely potentially down the road Meta Xai, OpenAI and others. Those other deals may or may not happen, but the piece takes a deep look at Anthropic's big bet on TPUs. Recently, as a way to cut costs drastically internally for them, they've contracted 1 million TPU V7 chips, roughly 400,000 TPUs. About $10 billion worth of hardware will be bought directly from Broadcom, which manufactures the TPUs and Co designs them with Google to install those in Anthropic's own facilities. The remaining around 600,000 TPUs will be rented from Google Cloud. Semianlysis estimates this rental piece alone represents around $42 billion of backlog for Google and accounts for most of the recent $49 billion increase in GCP's reported remaining performance obligations. By itself, the very idea of external competition already weakens Nvidia's pricing power. Even before open OpenAI actually started using TPUs itself, the credible threat of moving some of its workloads off of Nvidia let OpenAI negotiate roughly 30% savings on its Nvidia fleet. The author's point is that TPUs can cut Nvidia's capital expenditure even when no TPU is powered on simply by existing as a realistic alternative. Competition is a hell of a thing. But there's more because just on raw specs, while previous TPU generations lagged Nvidia on advertised peak compute and memory, Google was prioritizing reliability and its own recommendation system workloads over headline FLOP numbers. Now, with the LLM wave, that philosophy has shifted for Google. TPU v6 and now TPUv7 move much closer to Nvidia's current GB200 and GB300 GPU systems in peak compute and bandwidth while still shipping. Slightly later, TPUV7 uses high end HBM3e memory and nearly matches Nvidia's top chips in theoretical performance. But the authors stress that what matters is effective performance per total cost of ownership tco, not the headline FLOP numbers. They argue that Nvidia and AMD inflate peak FLOP figures using clock speeds and test conditions that are not sustainable in real workloads, realized utilization can be a fraction of the theoretical maximum. Google's TPUs, by contrast, have more conservative internally driven specs and when paired with Google's system level engineering and good compiler work, can reach higher effective utilization. Combined with procurement economics, Nvidia takes very high margins on the full system. Google does have to pay Broadcom, who in turn makes margin, but that is still less than Nvidia's stacked margin. TPUV7's full system total cost of ownership is estimated to be around 44% lower than a GB200 server for Google itself, and still roughly 30 to 40% cheaper per hour for large external buyers like Anthropic. That translates into more than 50% lower cost per effective training flop for Anthropic versus Nvidia's GB300 based systems, even if TPUs run at somewhat lower utilization. The article then zooms out to Google's networking design. TPU V7 systems are built as 64 chip 3D cubes wired into a torus pattern within Iraq, and many such cubes can be connected with optical circuit switches into very large world sizes, up to 9,216 TPUs tightly coupled for training and up to about 147,000 TPUs on a broader data center network. This scale is far beyond the 64 to 72 GPU pods common in the rest of the market. Optical switching also lets Google reconfigure clusters, flexibly route around failures and expand capacity without tearing up the entire network. The result is lower latency, better locality, and lower networking costs per unit of compute at very large scale. A separate section describes how this push reshapes the NEO cloud market, smaller cloud providers and repurposed bitcoin miners that rent out GPU clusters. Datacenter power is now the bottleneck and crypto miners already control power contracts and electrical infrastructure. Google's workaround for its own slow contracting process is to give these neoclouds an off balance sheet credit backstop, essentially an IOU that Google will take over capacity if the NEO cloud can't pay its lease. That template reduces financial risk for both data center builders and NEO clouds and is likely to spread throughout the industry, creating a path for many more crypto to AI conversions. The last big theme is software. Everybody knows that Nvidia's real estate moat is Cuda and the vast ecosystem of libraries and tools therein, not just the chips. Historically, Google's TPU software focused on internal frameworks like Jax and TensorFlow. External developers had a clumsy pytorch on TPU experience with non standard APIs which limited adoption. Google is now shifting hard, however. It is putting major engineering effort into making TPUs a first class Pytorch backend with proper eager execution and native distributed APIs and into integrating TPUs with widely used inference stacks like VLLM and SGLang. It is also open sourcing optimized TPU kernels for attention, mixture of experts, et cetera, and investing in kernel languages like palace and related compiler work to make it easier for others to reach high utilization. The bottom line from this piece TPUV7 already offers roughly similar raw performance to Nvidia's leading hardware materially better economics for very large buyers and unique system level advantages in networking and scale. If Google continues to improve and opens up the TPU software stack, especially its compilers and large cluster management tools, the Cuda Mote could narrow, forcing Nvidia to compete more on price and reducing its ability to extract outsized margins from the AI boom. Nvidia's fat margins could very much be Google's opportunity. 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Thus, the cynical thinking goes, they've been pursuing these so called circular deals to sort of build a too big to fail moat around themselves, throw your money around investing in your customers so they can continue to be your customers again. That's the cynical view, not necessarily one I subscribe to, but well, Nvidia has acquired $2 billion worth of synopsys common stock and has unveiled a strategic partnership with them to accelerate computing and AI engineering products, including deploying cuda. Quoting cnbc Synopsys offers services like silicon design and electronic design automation that helps its customers build AI powered. CEO Sassin Ghazi told CNBC that its partnership with Nvidia will help IT take workloads that used to run for weeks and reduce them to hours. We're going through a platform shift from classical general purpose computing running on CPUs to a new way of doing computing, accelerated computing running on GPUs, Nvidia's Jensen Huang said. That old way of doing is going to continue to exist, of course, but the world is shifting to this new way of doing computing. End quote. Nvidia and Synopsys have a long standing relationship, so Monday's announcement builds on their existing partnership and OpenAI has taken a stake in Thrive Capital's Thrive holdings and plans to embed AI agents in the companies in that portfolio, which already includes an accounting and IT business, quoting the times. For OpenAI, the hope is that working more closely with Thrive holdings will demonstrate how companies can harness the technology behind OpenAI's ChatGPT chatbot and create buzz to entice other potential customers. What we're trying to do with this partnership is really prove out ways that we can accelerate that type of transformation, Brad Lightcap, OpenAI AI's chief operating officer, said in an interview. Thrive Capital created Thrive holdings this year with an initial $1 billion in funding. The vehicle aims to do serial deal making, known in financial lingo as roll ups in relatively humdrum industries that it says would benefit from AI, a strategy that other venture capital firms have embarked on. Thrive holdings two current operations, the accounting business Creit Professionals alliance and the IT services provider Shield Technology Partners, have more than 1,000 employees in total. Thrive holdings has committed $500 million to Creit, which the trade publication Accounting Today described this year as one of the fastest growing accounting firms in the United States. Creit, for example, has been working to use the technology to automate tasks such as data entry and processing tax returns to help free up accountants to work more directly with clients. End quote. Hmm. Netflix has quietly killed support for casting from its mobile app to most modern TVs and streaming devices, including Chromecasts, regardless of your subscription tier. Quoting the Verge, Netflix has removed the ability to cast shows and movies from phones to TVs unless subscribers are using older casting devices. An updated help page on Netflix's website, first reported by Android Authority, says that the streaming service no longer supports casting shows from a mobile device to most TVs and TV streaming devices, and instead directs users to navigate Netflix using the remote that came with their TV hardware. The change seems to have rolled out in the last few weeks, with one user on Reddit reporting that casting support was removed on November 10 with zero warning. My colleague Dom Preston also found that while he was able to cast to a TV from an older version of the Netflix app, the casting option was no longer available after the app was updated. Casting support is still available on older chromecast devices or TVs that support Google cast natively, according to Netflix's support page, but only for subscribers on pricier ad free plans, which start from $17.99 per month. Netflix users with an ad supported subscription at $7.99 per month will be unable to cast from their phones even if they own legacy Chromecast devices. The casting changes announced on Netflix's support page do not explain why the feature has been removed. It follows a similar move in 2019 when Netflix removed airplay support. Citing a desire to ensure our standard of quality for viewing is being met, we have reached out to Netflix for comment. Finally today, two contrasting stories of the AI impact on jobs. First, McKinsey and other top consultancies have apparently frozen graduate payoffers in 2026, making this the third straight year they've done so as AI is reshaping the industry and threatens its so called pyramid model. Quoting the ft, firms were seeking more mid career specialized staff as they spent less time on traditional strategy consulting and more helping companies implement technology and AI, said a representative for McKinsey. It is harder to staff a 23 year old on those kinds of projects versus someone with experience, the spokesperson said. Some industry executives say that the more conservative hiring practices are in anticipation of productivity gains from AI, not because those gains are necessarily being realized. Yet two senior executives at big four firms estimated that across the UK's largest consulting and accounting firms, graduate recruitment would be down by about half in the coming year. Some of that is commercial because the market's tougher, but Some of that is anticipation of the impact of AI. Employment costs are going up because of national insurance, the minimum wage, etc. And you might be in a better place investing in AI and offshoring than in people, one of the executives said. The upheaval means that the traditional pyramid, in which a firm employs thousands of junior level employees and thins out the ranks with an up or out promotion culture, could be set to change, according to experts. Some are betting on an obelisk structure with fewer layers and less reliance on junior staff, while others predict an hourglass pinched in the middle as AI automates mid level routine tasks. End quote Meanwhile, conversely this is from the Journal the AI boom has led to high demand and more pay for the construction workers that build data centers. A trade group says that there is a shortage of around 439,000 workers in North America. Data centers don't employ many workers once they are actually built, but during construction they are a hive of workers pouring concrete walls and foundations, wiring electric panels and installing equipment such as power generators and chillers to ensure servers are cooled to a precise temperature at all times. Given such complexity and high demand, workers who move into the data center industry in roles ranging from electricians to project managers often earn 25 to 30% more than they did before, said Jake Rasweiler, senior vice president of data centers at Kelly Services, a staffing and recruitment firm. It's like the gold rush, chambliss said. In Hermiston, Oregon, Mark Benner, 60, arrives in the pre dawn hours at a data center construction site and lines up with scores of workers for a series of synchronized stretches. After that, he spends the day making the rounds ensuring electrical safety. These are lucrative skills at the electricity gobbling sites, and Benner makes $225,000 a year, boosted in part by $100 in daily incentive pay for all workers on site. Right, it's my American dream, said Benner, who has been helping build data centers for 15 years, including the ones now powering AI. Demand for such workers is colliding with a long standing shortage of skilled tradespeople that has pinched the construction industry. The Associated Builders and Contractors trade group estimates that the construction industry is short roughly 439,000 workers, mostly among skilled workers who do things like lay pipe and wire electrical panels. The effects are starting to pile up. A survey by the Uptime Institute of data center equipment manufacturers, engineers and construction companies found that 52% said staffing shortages on sites had caused business disruptions, up from 43% last year. Contractors working on data centers have an average backlog of 10.9 months of work, compared with eight months for their peers, according to data from ABC. End quote. Nothing more for you today. Talk to you tomorrow.