SITALWeek #380
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: it's been raining hard in California, so, with water on my mind, this week we cover sprinklers, rivers, and urine: smart Wi-Fi irrigation, rivers as an analogy for understanding positive and negative feedback in the analog-to-digital economic transition, and toilet-based smart sensors to diagnose health issues; app store revenues have flatlined, and developers are moving on to the next mega growth platform of chatbots, which in turn will likely create many new mobile apps; and, much more below...
Stuff about Innovation and Technology
AI Narrators
Microsoft has a new speech generation model that can recreate a person’s voice with just three seconds of audio. VALL-E was trained largely on recordings of audiobooks. For now, the sample used to create the generative voice model needs to sound similar to one within the training set, which limits its use – unless you talk slowly and enunciate your words like a narrator. However, you can see the power of the model in requiring such a short input sample for replication. As these voice models quickly evolve, audiobook narration may soon become a dwindling profession.
PeePal
Withings, maker of smart health tech, introduced a connected urine analysis device at CES in Las Vegas. The "U-Scan" resides within your toilet bowl to automatically collect data, with "Stream ID" tagging who’s using the toilet. Withings notes that urine contains over 3,000 metabolites that give insights into a variety of health issues, especially if monitored over time. The only icky part of it is replacing the data-collecting cartridge every three months. Interfacing with the U-Scan app, the Nutri Balance cartridge, for example, “shows an analysis of specific gravity, pH, vitamin C and ketone levels. The combination of these measurements helps people monitor their metabolic intake to optimize their daily hydration and nutrients. The ‘actionable’ part of that is that the system can recommend workouts, offer dietary suggestions and recipes — all to help health-conscious users achieve their goals.” U-Scan has not cleared US’ FDA approval yet, but the device is set to launch in Europe, where apparently regulators aren’t stressing about what you might learn from your pee, for €500 with one three-month cartridge.
Smart Sprinkler
Another interesting CES connected device that caught my eye is the new Moen sprinkler clock that uses wireless in-ground moisture sensors to control automatic sprinkler systems, saving water, guesswork, and hassle. I’ve been using Moen Flo water monitors for a couple years and really like them. They can pick up imperceptibly small leaks in your house (like a dripping faucet or leaking hose) and also automatically shut off the water in the event of a major leak. A lot of smart home technology has largely stalled as the complexity overwhelms the goal of the products, but I think we will slowly see rollout of more interesting and useful devices.
River of Progress
In 2004, there was a large air travel interruption because a critical piece of FAA software failed to receive a manual reboot, which was required every 49.7 days (the employee in charge of flipping the switch forgot). The fix at the time was essentially adding an alert to remind people to reboot. Apparently, the FAA still hasn’t learned its lesson about the dangers of relying on fallible legacy tech. Last week’s complete, nationwide ground stoppage of flights due to a system issue at the FAA highlights the ancient hardware and software systems that still run critical infrastructure. This failure is another example (akin to Southwest Airlines’ weather-related holiday software meltdown) of the negative feedback of heavy lifting in the messy, analog world, which I wrote about last week in When Positive and Negative Feedback Loops Collide. I was talking to Brinton this week trying to come up with a good analogy for this ever-changing dynamic between accelerants to progress and resistance to change, and he suggested a river. We can think of progress as a boat floating down the river, traveling more quickly in narrow and/or steep sections but slower in broad, open spaces. Turbulence also comes into play, with heavy rapids, confluences, and deluge inputs accelerating progress (sometimes at the risk of capsizing!) interspersed with stagnant stretches, where we make nearly imperceptible headway. The course is ever shifting and prone to unpredictable behavior, so you never know when a dam might become a waterfall. We’ve been in a period of fast moving Information Age digitalization, propelled downstream by low interest rates and globalization. However, we now appear poised at an inlet to calmer waters. Yet, under the placid surface, there can be swirling undercurrents and hidden bogs forcing us backward, as well as deeper channels coursing ever onward. As I talk about in the next section, we just have to learn to read the river and adjust our navigation. Can you tell that I love extended metaphors and also live in California’s currently flooded Central Coast!? In our 2014 paper, we also noted the following about rivers:
In complex systems, volatility leads to emergent behavior, which improves the nature of the system over time. Imagine a river: it needs the sharp bends and narrow straights to vary the flow of water and carve out depths to support various ecosystems. If a river ran in a straight line with a constant flow and at a constant depth, it could not support near the diversity enabled through volatility. Complex systems embrace volatility.
Follow the Developers
One of the tried-and-true paths to making money in the technology industry over the last forty years has been to follow the software developers. They generally gravitate toward the fastest growing, highest non-zero-sum platforms that are rapidly expanding their potential revenue, customers, and services. Surely the epitome of developer enthusiasm was Steve Ballmer’s famous “Developers. Developers. Developers!” spasm in 2006. Reflecting on the event in 2021, Ballmer noted: “‘Developers, developers, developers,’ yeah, people got it, we’re only going to exist if we can get application support behind our platform. There was no question the thing that established the PC was the set of work that developers did on top of the platform.” A couple years later in 2008, Apple launched the App Store on the nascent iPhone and iOS platforms. The developers followed the money, user growth, and development tools to iOS. Since the launch of the App Store, Apple has paid developers an astounding $320B, and that number doesn’t include the enormous economy of ads, apps, and services that is many multiples larger. In 2022, Apple paid out $60B to developers. That’s a huge number, but App Store revenues did not grow from 2021, and app store payouts for recent months might be in decline, led by weakness in mobile gaming, overall post-pandemic screen fatigue, and increased utilization of free apps like TikTok (see Gaming Weakness in #356). Google’s CFO summarized the app headwinds on their Q32022 earnings call: “Play revenues were lower due to a number of factors, including a decline in user engagement and gaming from the elevated levels seen earlier in the pandemic. Among other factors, this shift in user behavior also created downward pressure on our advertising revenues, with lower revenues from ad promo spend on YouTube, Network and Play Ads in Search and Other.” Advertising is a particularly sticky problem as privacy changes at Apple have made it much harder to target customers, rendering the majority of ads less relevant/valuable.
In one sense, the mobile app ecosystem that is monetized by Apple and Google has shifted to zero sum. In order for developers and companies to grow their businesses, they would need to take share from wherever else consumers are spending their time and money. Beyond monetizing off platform (without Apple or Google taking a cut), app developers still have options to grow, such as: raise prices, introduce new pricing tiers, and leverage first-party data to sell higher priced ads. We could see a wave of cost-cutting developer consolidation, with unprofitable apps and games exiting the app stores. Smartphones are, of course, going to remain the dominant platform for years, or possibly decades. The iOS and Android duopoly appears unassailable today, and they might also anchor the next phase of spatial computing as AR technology matures (see Meta-mess). And, we will at some point lap the post-pandemic screen-time increases, and consumers will rebound their discretionary spending as inflation calms down (see Spiraling Content Meets Maxed-Out Attention in #330). However, while phones remain our nexus for everything, a growing percentage of value is being captured off platform. Take subscriptions for example. Between news, streaming video, music, fitness, etc., I probably have over 20 monthly subscriptions, but Apple reported only 745M paid subscriptions on iOS that run through the App Store. That’s less than one per iOS user, which implies the majority of subscription-based apps are direct-to-consumer.
While the developer ecosystem, and overall smartphone app revenues, will continue to grow, it might be time to start looking at where developers are going next. Close readers of SITALWeek will have no problem guessing what I think that next big platform will be: chatbots and large language models (LLMs) like ChatGPT. I’ve been obsessed with these trending tools for the last year, and they are emerging as true platforms for further creation. I think we will see an explosion of services, apps, games, etc. leveraging/connected to tools like ChatGPT from OpenAI (which is rumored to be receiving a $10B infusion from Microsoft!) and generative AI. Huge value will come from combining chatbots with existing tools like search. Google, for example, is experimenting with a hybrid LLM-search tool with DeepMind’s Sparrow app, and the head of Deep Learning at DeepMind gave some examples of queries in this short Twitter thread. And, Stephen Wolfram wrote a fascinating paper about the power of combining a natural language interface like ChatGPT with the computational language and vast data in Wolfram|Alpha. Many of the new uses for chatbots and LLMs will feedback into, and perhaps even invigorate, the ways we use mobile devices, potentially stimulating app store growth in the future. (I did a deep-dive review of my writings over the last year on chatbots in #377 and added more to those topics last week in #379).
If I were to summarize how I think about LLM platforms, I would say: for the first time, we can have a conversation with data – with chatbots acting as universal translators. However, to take full advantage of this new AI, we first have to learn its language. You can think of chatbots as space aliens – we have to assimilate their language and technology to learn their secrets. Once we do that, we can begin to think in the many different languages these various chatbots use to communicate with each other about their vast troves of information (i.e., imagine ChatGPT conversing with Wolfram|Alpha conversing with Google Search). Interactions between all of these systems will continue to accelerate in both volume and sophistication (much like in the 2013 movie Her, which is still one of the best sci-fi representations of chatbots). LLM platforms will completely change how we gain knowledge about the world and fundamentally shift our perspective of our place within it.
Miscellaneous Stuff
JWST’s Stellar Discoveries
Here is a roundup of the early discoveries from the JWST, which includes finding planets just 350M years after the Big Bang (over 13B years ago), finding carbon dioxide in the atmosphere of a distant planet, seeing Jupiter’s aurora, and new information on star formation. The space telescope’s best discoveries still lie ahead; but, if nothing else, we have already been dazzled by its images of the vastness of the Universe.
✌️-Brad
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