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Brian McCullough
Welcome to the TechMean write home for Tuesday, January 28, 2025. I'm Brian McCullough. Today, more fallout from the Deep SEQ realignment of everybody's thinking about AI. Could this be a signal that the large model business model is over and value is going to be in the application layer? Pebble Lives, Spotify says it pays out tons, but does it really? And the network of AI local news newsletters. Here's what you missed today in the world of tech. Well, it's day two of the Deep Seek era and as tends to happen with these things, there's a bit of a snapback. First up, folks are like we welcome our new AI paradigm. Sam Altman said Deepseeks R1 is well quoting him Deep Seeks R1 is an impressive model, particularly around what they're able to deliver for the price. We will obviously deliver much better models and also it's legit invigorating to have a new competitor. We will pull up some releases, but mostly we are excited to continue to execute on our research roadmap and believe more compute is more important now than ever before. To succeed at our mission, the world is going to want to use a lot of AI and really be quite amazed by the next gen models coming. End quote. Meanwhile quote Deepseek is an excellent AI advancement and a perfect example of test time scaling, an Nvidia spokesperson told CNBC on Monday. Deepseek's work illustrates how new models can be created using that technique, leveraging widely available models and compute that is fully export control compliant. Inference requires significant numbers of Nvidia GPUs and high performance networking, the spokesperson added. Well, they would want to say something hopeful like that. Nvidia stock dropped 16.86% yesterday, losing nearly $600 billion in market cap, more than twice as much as any US company has ever lost in a single day. Basically lost the value of the economy of Mexico in a single day. There was some pushback on exactly how cheap it was for Deepseek to do what it's done. Semianalysis said Deepseek has spent well over $500 million on GPUs over the history of the company, so that suggests they spend as much as anybody else, even if individual models are more efficient. TechInsights said it doesn't see Deepseek as a big hit to Nvidia and more comparisons to the TikTok example. Wired analyzed Deepseek's privacy policy and said it shows broad data collection practices and says user Yep, along with all the conversations and prompts is stored on servers in China, so But Deepseek took advantage of all the attention, debuting a family of multimodal MIT licensed open source models including Janus Pro 7B, which it claims beats OpenAI's Dall E3 in genovel and DPG bench, quoting TechCrunch. The models, which are available for download from the AI dev platform Hugging Face, are part of a new model family that Deepseek is calling Janus Pro. They range in size from 1 billion to 7 billion parameters. Parameters roughly correspond to a model's problem solving skills, and models with more parameters generally perform better than those with fewer parameters. Janus Pro is under an MIT license, meaning it can be used commercially without restriction. Janus Pro, which Deep Seq describes as a novel autoregressive framework, can both analyze and create new images according to the company, on two AI evaluation benchmarks, geneval and DPG bench. The largest Janus Pro model, Janus Pro 7B beats Dall E3, as well as models such as Pixart Alpha EMU 3Gen and Stability AI's stable Diffusion XL. Granted, some of those models are on the older side, and most Janus Pro models can only analyze small images with a resolution of up to 384x384, but Janus Pro's performance is impressive considering the model's compact sizes. End quote and finally, I want to read the entirety of this tweet from Aaron Levy, CEO of Box, because I think it makes an interesting case that Deepseek's breakthroughs are a great win for app developers, with more value accruing back into the app layer as the cost of intelligence drops rapidly. There's been an open question for a couple of years now, especially from public market investors around whether more value goes into the AI models or into the application layer of AI over time. The specifics of the pie graph don't matter as much as the core direction of the space. Imagine two different scenarios, one in which AI was extremely proprietary and very expensive, and another where AI is almost completely free and relatively open. You could easily game out two different outcomes in these worlds. In the world of very expensive and proprietary AI, the providers of AI could and likely should choose to keep all the economics for themselves, basically crowding out opportunity for developers and the ecosystem. In a world of insanely cheap AI, then the value is less about the models, but what you do with the AI models to make them useful. In that world, more value is available to the application layer, which could include the AI companies to be clear with the latest breakthroughs from Deepseek, we can nearly definitively say this question has been answered and we're clearly moving closer to the ladder. We've already seen incremental steps toward this direction with the continuous costs and quality improvements from labs in the past couple of years, but DeepSeq shifts our understanding of this even further. In a world the cost of intelligence will continue to drop rapidly, more value will accrue back to the app layer. Products that combine AI, customer workflows and likely some degree of unique data will generate substantial value from these models going forward. Now everyone wants to live in a binary world of winners and losers, but I don't think it's that simple. Here. The leading AI labs will incorporate the relevant lessons from Deep SEQ into their models and will get cheaper and more intelligent AI. As a result of that, the cost of intelligence will continue to drop and we will find even more ways to use the technology as it becomes affordable for even more use cases. If we can make AI 10x more efficient today, it's exceedingly obvious we will have 100x more use for it in 5 years from now. More than making up for the efficiency gains, making demand for GPUs and data centers bigger than ever. In all, fantastic to see that we continue to have companies and teams pushing the limits of AI. This is a great win for software developers at the app layer, and it will push labs to go even further. Incredible times. End Quote pebble lives Google has open source Pebble OS paving the way for more smartwatch hardware. In fact, pebble founder Eric Migovsky apparently aims to develop a new smartwatch, quoting TechCrunch four years after launching the then most lucrative crowdfunding campaign in Kickstarter's history, smartwatch maker pebble abruptly closed in 2016, filing for insolvent solvency before being sold off to rival Fitbit. The fitness tracking giant went on to build much of its Ionic smartwatch with help from former Pebblers. Along with Pebble's pioneering software stack, it could be argued that pebble was simply too early to the space the Apple watch launched in mid-2015 and proceeded to suck much of the oxygen out of the room. It would be massively oversimplifying the situation to suggest that it was merely another case of Sherlocking. However, Apple, after all, raised public interest, ultimately setting the stage for countless other smartwatches after in fact, founder and CEO Eric Mijico Migikovsky believes instead that the company's rapid growth and feature expansion caused pebble, which sold 2 million smartwatches to lose sight of his initial vision. It certainly wouldn't be the first time a hardware startup was felled by such a fate. Yet Migicovsky is ready for round two. We're restarting pebble, he told TechCrunch with a massive grin on a Zoom call Monday. How exactly pebble branding faded after the company was bought. Its acquirer Fitbit, was itself sold off to Google in 2021. Now Google, which still owns the technology and all of Pebble's ip, plans to open source the smartwatch brand's software stack. By open sourcing access to Pebbleos, Google is opening the door to new third party hardware, and Michikovsky's smartwatch startup is the first on that list. It's still in the idea stage, he says. The company needs a new name, something that Beeper co founder and former Y Combinator partner hasn't quite gotten around to. But he tells TechCrunch that he has thrown himself into the project full time and will be able to accelerate things as access to pebbleos opens up. He is currently its only employee, but there are plans to bring on another around March. The startup's goals are fittingly humble. Medzikovsky says he simply wants to make the watch he wants, given that the pebble he wears to this day is now a decade old. I've tried everything else, he says. I have very high standards. Those are, according to a new blog post on Medjikovsky's personal site, always on e paper screen. It's reflective rather than emissive sunlight readable glanceable not distracting to others like a bright wrist long battery life, one less thing to charge it's annoying to need extra cables when traveling. Simple, simple and beautiful user experience around a core set of features I use regularly telling time notifications, music control alarms weather calendar, sleep step tracking buttons to play, pause, skip music on my phone without looking at the screen Hackable. Apparently you can't even write your own watch faces for Apple Watch. That's wild. There were more than 16,000 watch faces on the pebble app Store. In spite of his time at yc, Migicovsky has no plan to raise VC funds. Nor does he plan to return to the Kickstarter model that gave rise to pebble, currently self funding the project and says he plans to build it modestly based on consumer interest. With Robinhood Gold, you can now enjoy the VIP treatment, receiving a 3% IRA match on retirement contributions. The privileges of the very privileged are no longer exclusive. With Robinhood Gold, your annual IRA contributions are boosted by 3% plus. You also get 4% APY on your cash in non retirement accounts. 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Quoting CNBC, OpenAI on Tuesday announced its biggest product launch since its enterprise rollout. It's called chatgpt.gov and was built specifically for US government use. The user interface for chatgpt.gov looks like Chatgpt Enterprise. The main difference is that government agencies will use chatgpt.gov in their own Microsoft Azure Commercial cloud or Azure Government Community Cloud so they can manage their own security, privacy and compliance requirements. Philippe Milan, who leads Federal sales and go to market for OpenAI, said on the call with reporters. Aaron Wilkowicz, a solutions engineer at OpenAI, showed reporters a demo of A day in the life of a new Trump administration employee, allowing the person to sign into chatgpt.gov and create a five week plan for some of their job duties, then analyze an uploaded photo of the same printed out plan with notes and markings all over it. Wilkowitz also demonstrated how ChatGPT.gov could draft a memo to the Legal and Compliance department summarizing its own AI generated job plan and then translate the memo into different languages, end quote OpenAI's ChatGPT enterprise, which powers chatgpt.gov, is currently navigating the FedRAMP certification process, a crucial step before it can handle sensitive government data. While OpenAI described it as an extended journey without committing to specific deadlines, OpenAI's vision appears to be taking shape quickly. Milan revealed that chatgpt.gov could be in agencies hands for testing within a month, targeting sectors where data sensitivity is paramount defense, law enforcement and health care. The presence of OpenAI executives at the inauguration apparently allowed for meaningful connections with incoming administration officials. OpenAI CPO Kevin Wheel emphasized how their objectives align perfectly with the broader national strategy. He said, quote the focus is on ensuring that the US Wins in AI and that our interests are very aligned. End quote Spotify says it paid out $10 billion to the music industry in 2024, up from a record $9 billion in 2023, taking its total to $60 billion paid out since its 2006 founding, quoting Variety While that figure may seem questionable considering how little many musicians make from streaming, it's important to bear in mind that Spotify, like most streaming services, pays rights holders, usually a label and music publisher, which then distribute the money to musicians and songwriters after taking their percentage. While economy leaves much to be desired in terms of compensating creators, the blame is not entirely on streaming services. In 2023, the company said it pays out nearly 70% of every dollar it generates from music back to the industry, generating its music revenue from two sources subscription fees from its premium platform paying subscribers and fees from advertisings on music on its free tier. Those rights holders include record labels, publishers, independent distributors, performance rights organizations and collecting societies, End quote and quoting the Verge In November, Spotify reported it was on track to achieve its first full year of profitability and had 4 billion euro in total revenue for the preceding three months, a 19% increase from the same quarter a year earlier. Next week, it will report earnings for the entirety of 2024. Spotify reportedly has the lowest artist payout rates compared to rival services like Apple Music, YouTube Music and Amazon Music, and the platform's streaming royalties and recommendation algorithms have been widely criticized by artists and polit policymakers over the years. Many claim that payouts are too small and that the focus on promoting big artists makes it hard for new musicians to be discovered on the platform. End quote finally today from Nieman Lab, a look at Good Daily, an AI generated newsletter network run by one person aggregating local news across 47 US states without disclosing its use of AI on first glance, Good Day Fort Collins appears to be a standard local news roundup. One recent edition of the newsletter includes short blurbs and links to over a dozen stories about the mid sized Colorado City, a restaurant opening, a record breaking snowfall, a leadership shakeup at a local hospital. The newsletter attributes the stories to longtime Fort Collins news outlets like the Coloradan and the Loveland Reporter Herald. Further down is a spread of events happening across the city, including an upcoming Polar Plunge and a figure drawing class. It turns out Good Day Fort Collins is just one in a network of AI generated newsletters operating in 355 cities and towns across the U.S. not only do these hundreds of newsletters share the same exact seven testimonials from readers, they also share the same branding, the same copy on their about pages, and the same stated mission to make local news more accessible and highlight extraordinary people in our community. You wouldn't know any of that as a subscriber. Separate website domains and distinct newsletter names make it difficult to connect the dots. There is Good Day, Rock Springs Daily, Bentonville Today in Virginia beach, and Pittsburgh Morning News, to name just a few. Nothing in the newsletter copy discloses that they are part of a national network or that the article curation and summary blurbs are generated using large language models. The newsletters do all name the same Founder and editor, Matthew Henderson. Beyond an editor contact email, there is no information in the newsletters about Henderson, his operating location, or the company behind the newsletters. The email used for website domain registrations is tied to a blank website. Only after making a $5 reader donation to Good Day Fort Collins was I able to trace the charge and the website ownership to Good Day Inc. The company doesn't have an online presence but is incorporated in both Delaware and New York. Considering how little Henderson shares about himself or his company in his newsletters, I was surprised that he was a real person and that he responded to my email. Henderson is a serial Internet startup founder and software engineer whose past companies include the on demand blog writing service Scribble and the Journalist email database Press Hunt. Good Day is currently a one man operation, Henderson says, though AI use is not disclosed to Good Daily subscribers. In an interview, Henderson didn't shy away from the fact that each newsletter is produced using near full automation. Our goal is to use automation and technology everywhere we possibly can without sacrificing product quality for our readers, he told me in an email, explaining that he built the back end technology that outputs the hundreds of newsletter editions every day. These automated agents read the news in every town where Good Day operates, curate the most relevant stories, summarize them, edit and approve the copy, format it into a newsletter and publish. Henderson declined to share any more specifics about his use of LLMs, calling it proprietary at a high level. The system operates much like an editorial team, he said. Currently, Good Day is operating in 47 states with a focus on small town America. One of the smallest towns is Rock Springs, Wyoming, which has a population of just over 20,000. Local news should be local. The problem is at this point there are economic challenges keeping that from happening. Smaller communities rarely can support enough staff to run a traditional news organization, said Henderson, who currently runs Sounds Good Daily from New York City. I see technology and LLMs specifically as our best shot to fix this. End quote. Nothing more for you on this occasion. Talk to you tomorrow.