SITALWeek #432
Welcome to Stuff I Thought About Last Week, a personal collection of topics on tech, innovation, science, the digital economic transition, the finance industry, and whatever else made me think last week.
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In today’s post: examining the parallels between the predictive human brain and AI as the world may become more deterministic; Disney's bots outshine humanoids at GTC; the existentialism of Wim Wenders and Lou Reed; the absurd approach to regulating big tech companies while the mega platforms increasingly lock out the competition in AI; and, much more below.
Stuff about Innovation and Technology
Probabilistic Fortune Tellers
I’ve seen a number of AI and robotics companies recently make reference to AI as being able to “see into the near future”. In other words, their AI agents are good at predicting what is likely to happen a few moments into the future based on their models and observations. This makes sense based on the way LLMs work as language prediction engines. For example, Waabi is a self-driving startup using a generative lidar model that predicts where objects will be 5-10 seconds into the future. Covariant’s RFM-1 is a robotic reasoning model that predicts up to 3 seconds into the future how its environment will change (Covariant was started by the former robotics team at OpenAI). What interests me here is comparing this forecasting model to the human brain and energy consumption. I’ve written extensively about how our brain, rather counterintuitively, operates as a prediction engine rather than a reaction engine. The common perception is that we react to sensory input of things we see, hear, etc., which gives us a sense of agency. In reality, however, our brain is predicting what we will see or hear or feel – and then course correcting when it’s wrong. All this prediction and comparison is happening subconsciously. Why did evolution arrive at this model? It appears to be an effective way to minimize energy consumption: taking predictive shortcuts to fill in the blanks (rather than living exclusively in the moment and continually rescanning and reacting to our entire environment) is far less computationally intensive for the brain, and thus takes fewer calories and resources to accomplish. I learned a lot about this model of the brain from the work of Karl Friston and Lisa Feldman Barrett, which is covered in Outsmarting Your Brain (I also specifically discussed Friston’s work as it relates to AI back in #271.) I think this is a good model to conceptualize how AI will progress beyond human capabilities and, especially, how AI embedded in various robotic form factors will outperform humans. The larger and smarter these models get, the more they will be able to probabilistically see into the future. Eventually, they may become sufficiently powerful to see far into the future (and, perhaps, as in the sci-fi realm of a show like Alex Garland’s Devs, know the future because everything is determined by what precedes it!). I explored the topic of AI being a tool to see into the future in detail in Simulacrum, and I’ve suggested more recently in “Your Wish is Granted” that the real AI prize being sought is a global prediction machine. So powerful would such a machine be that AI’s pied pipers are seeking trillions of dollars to build the technology that underpins it. To the extent that bits and pieces of the future become easier to predict, this could conserve large amounts of energy by avoiding probabilistically fruitless paths and unlikely scenarios, similar to how the brain works. So, while AI may consume enormous amounts of energy (see Pushing Electrons), it could save far more friction and waste in the global economic system on a net basis. As I described in The Simulacrum, the effect of complex, interacting AIs may produce a deterministic outcome for the world as resources shift to manifest the predictions they come up with. All this is to say that I think it’s increasingly of note that many AI companies are now describing their technology as operating by seeing into the near future. This could be a coincidence, or it could be further confirmation that LLMs work very similarly to, if not exactly like, the human brain. Or it might be more appropriate to invert that to say the human brain works exactly like the AI transformer models that underpin LLMs.
I Like Cute Bots
My favorite adorable robots from Disney made their way onto the stage with Jensen Huang at Nvidia’s annual user conference last week. We knew that simulation training was the key to their rapid creation, and now we know that they were trained on Nvidia’s Isaac Sim platform and are powered by the Nvidia Jetson robotics chip. Here is the part of the keynote where an intimidating slate of on-screen humanoids were out sparkled by the Disney bots that walked the stage. The platform is called project GROOT, a general purpose foundational model for humanoid robots. The illustration that shows how GROOT works appears very much like the prediction/correction feedback loop I described in the preceding section.
Miscellaneous Stuff
Komorebi
There was a brief profile of director Wim Wenders in NME earlier this year that I returned to after finally seeing his latest movie, Perfect Days. I’ve discussed Wenders' movies in the past, notably 1991’s Until the End of the World. There is an interesting contrast between these two movies, made more than thirty years apart: Until the End of the World transports us to a near future of digital technology, neural links, and VR that grips and consumes our psyches, while Perfect Days features a protagonist seemingly averse to the fast-paced digital world who is obsessed with living in an analog version of the past/present. Both movies heavily lean on thematic rock music. And, that is one particular focus of the NME article: the criticality of Lou Reed’s music, which Wenders proclaimed saved his life. I liked Perfect Days because I am a sucker for anything that is pure existentialism – in this case, the pain and the joy of trying to get through the day while carrying a lifetime of existence with you. Both movies revolve around trying to grasp the past, whether it be with analog cassette music that evokes an emotion from a lifetime ago, or a sci-fi brain interface that places you precisely back in time in a memory or dream. The latter is very similar to the Apple Vision Pro’s feature I previously discussed that allows you to time travel into the past by virtually inhabiting a 3D image/video you’ve previously captured. It’s one thing to hear a song and remember a ghostly reflection of a time long since passed; it will be quite another to fully relive our actual recorded past experiences through VR, unable to shake their vivid hold on our present attempts to simply exist. Perfect Days was conceived as a documentary before morphing into an entirely fictional tale. The original title was Komorebi, a Japanese term that describes sunlight filtering through trees. For me, there are very few activities more existential than watching the the play of shadows and light leaking through a canopy of susurrating leaves.
Stuff About Demographics, the Economy, and Investing
Copycat Trading
BI reports on Autopilot, an investing app that allows you to automatically copy trades of Congress members and other successful investors in your own brokerage accounts. There is of course a time lag of up to 45 days for Congress members and potentially up to 3-4 months for 13F filings from public market investors, which should render it somewhat ineffective. But, returning to the opening section of this week’s newsletter, the interesting question is: how long will it be before AI models know what we will desire to buy and sell before we know it ourselves?
United States v. Big Tech
Five years ago, we wrote an essay called Regulating an Information Based Business that discussed the differences between digital and legacy analog industries when it comes to characterizing and regulating monopolies. The gist of the argument is that there are natural power laws that form around network effects that make dominant companies preferential, in many cases, in the digital realm. That’s not to say the government shouldn’t regulate or closely examine everything large tech platforms do, but there is perhaps greater risk in breaking apart or over regulating tech giants than in letting them be, and sights should be set on providing consumer-protective guard rails instead of deciding how the platforms can vertically integrate and horizontally expand. Moreover, given the wildly mismatched pace of innovation and regulation, the government’s use of legacy regulation models from the industrial era seems unlikely to accomplish its purported goals (or anything useful, for that matter).
Lately, the convoluted impotence of US regulators has been on full display when it comes to big tech. A recent DoJ lawsuit against Apple alleges the company worked to lock customers into their hardware and software to the exclusion of rivals. The primary rival, of course, being Google’s Android. The DoJ is simultaneously suing Google for an alleged search monopoly and, separately, an online ad monopoly. Further, the two companies are being litigated by the government over potentially colluding with each other in a way that limited consumer choice. So, effectively, the government is trying to prove in court the absurdity that it wants consumers to be able to more easily navigate between two allegedly illegal monopolies. Meanwhile, it was reported by Bloomberg that Apple is rumored to be doubling down on their overall deal with Google by licensing the latter’s Gemini AI model to run on Apple devices. It will no doubt further irk regulators that the two platforms are continuing to enable each other’s dominance. When it comes to ecommerce, the FTC is bringing a long awaited trial against Amazon, alleging the company did not allow third-party sellers to offer products for lower prices on competing platforms; but, this case won’t start until 2026. And, don’t get me started on missing regulations for Shein and Temu.
While the government concerns itself with Apple, Google, and Amazon, regulators are sleeping on some aggressive bundling and apparent anticompetitive maneuvers that Microsoft is making in plain daylight. Regulators are circling the company’s deal with OpenAI, which was treated as an investment/partnership but seems to be in large part functioning as an acquisition (e.g., Microsoft was ready to hire everyone at OpenAI during the latter’s recent boardroom saga). And, just last week, Microsoft hired essentially the entire team of Inflection (maker of Pi.ai, which I just wrote about in #430 and #431) and announced that Inflection would migrate to Microsoft Azure. This “hiring event” is obviously an acquisition of a meaningful competitor to OpenAI and Microsoft Copilot (formerly Bing Chat). In return for hiring the employees, Microsoft is paying $650M to Inflection’s investors, including Microsoft board member Reed Hoffman and Bill Gates. Let’s call a spade a spade: this is an acquisition that should at the very least be reviewed by the government. A co-founder of Google’s DeepMind, who also co-founded Inflection, Mustafa Suleyman, will take over all of Microsoft’s consumer AI businesses. Many of those consumer AI products are being bundled into the core Windows operating system and apps to the exclusion of other AI companies in a move so reminiscent of the Internet Explorer case, which created a decade-long consent decree against the company (which I believe caused Microsoft to miss the entire mobile phone OS and hardware market), that I have to wonder how in the world Microsoft thinks they can get away with the same maneuvers today. For example, as a Windows user, I am currently forced to use Copilot since I don’t have the option to integrate Google’s Gemini (or any of the other AI models from various startups) into Windows. This situation would be akin to Google prohibiting access to Bing.com from their Android operating system or Chrome browsers (the counter example here is also true: the only AI assistant I can integrate into Android today is Gemini). Given how integral AI will become, users should have free choice of which AI they want to integrate into their devices, just like we can choose our browsers, search engines, and other apps.
I believe that the vast majority of decision makers at the large tech platforms are genuinely trying to create value for their users in ways that are not anticompetitive. Further, there is good logic for bundling hardware and software that benefits users and grows the ecosystem faster. But, clearly, there should be limits. The overall transition of the global economy from analog to digital is being enabled by these massive platforms, and the reality is that customers largely do have choices and there is competition, but cases of complete lock-in need to be regulated. As I noted at the start of this section, the nature of progress in the Digital Age is for power laws to emerge due to network effects that benefit all users. Naturally, these are going to appear to be monopolies, but they might just be dominant companies that are reducing prices and creating more value for the overall economy. Thus, I don’t believe there is (in general) much validity to any of the announced litigatory cases, with a couple of exceptions: 1) I think more choice and freedom in app stores would drive take rates down and increase the overall app economy materially to the benefit of all, without sacrificing users’ security and safety; and 2) it should be easier for companies and consumers to move their data between products and services. There is a legitimate question as to whether the companies spending the tens of billions to build AI infrastructure and/or dominate the mobile operating system/app stores should also be allowed to own all of the AI models. Perhaps there should be a regulatory framework that allows open competition for AI, much like we saw with the Telecom Act of 1996. While I’ve focused on US regulators here, I’d be remiss to not mention parallel efforts in the EU, which, in some cases, could drive some positive changes for consumers and app builders, but time will be the judge of that.
These flashy cases against the digital giants who, quite literally, gave the world the Internet (accompanied by price deflation and step-function gains in productivity for the vast majority of consumers, largely accomplished via positive sum business models) seem to be aimed at grabbing headlines rather than materially benefiting consumers. And, the misplaced and ill-fated focus on tech companies is taking resources away from oversight of the thousands of other product and service categories that have experienced rampant consolidation. As a result, certain sectors have seen significant price inflation and a plague of inferior goods and dismal customer service, leading to vulnerable single points of failure in the economy. But, I know, I should be careful what I wish for since nearly all government regulation leads to regulatory capture that favors the incumbents long term, whether it be the tech giants or any other business that catches the eye of bureaucratic watchdogs.
✌️-Brad
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