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: A century of recorded music is now growing more than 30% a year; while we might not get autonomous cars for a while, the technology is being leveraged into other fields like agriculture; large AI models help make soup; AI systems are converging into platforms to drive efficiency and adoption; young galaxies; the power of comedy and transparency to change the narratives that dominate our world; and, much more below...

Stuff about Innovation and Technology
AI Botanist
Google X’s agriculture startup Mineral automates data collection in the field for phenotyping plants. The goal is to take some of the guesswork out of creating new breeds of plants, like strawberries. “The current version has all-terrain wheels and a half-dozen cameras that take pictures of plants from different angles, plus laser sensors and GPS to keep it from bumping into obstacles. It’s driven by a Mineral technician who uses a remote control; ultimately the company thinks its rovers will direct themselves. Mineral is also testing sensors that can be attached to conventional farm tractors, and ways to augment that data using images from drones, satellites and even smartphones.” Syngenta uses Mineral to identify weeds that can be individually target-sprayed with herbicide. Essentially, Mineral is a new use case of the technology from Google’s autonomous car unit Waymo combined with Google’s other AI capabilities.

AI Soup Chef
Campbell’s Soup has an AI model with 300 billion inputs, and they utilize an “agile design” strategy (typically deployed for software engineering) to help determine what their new flavors of soup should be and speed product development. Chief research, development, and innovation officer Craig Slavtcheff notes: “I would never go so far as to say we’re a tech company as we are all here driven by our passion for food, but [we’re using] all the goodness that came out of the world of tech, and applying it to food design.”

AI’s Carbon
Electricity usage by data centers has surprisingly remained steady since the advent of cloud computing as efficiencies in chips and system design have offset workload growth. As noted in #234: “Between 2010 and 2018, data centers grew compute capacity 6x and storage 25x while Internet traffic grew 10x. However, thanks to Moore’s law and the application of machine learning to improve data center efficiency, power consumption only grew 6% over that period.” That trend might continue, but it seems plausible that we will need breakthroughs in efficiencies for creating and deploying AI engines as they proliferate in usage. Training and utilizing a single large language model (LLM) creates anywhere from 50 to 500+ metric tons of carbon dioxide emissions, according to MIT Technology Review. With LLM efforts underway at many startups and most large tech platforms, it’s easy to see just how large a carbon footprint AI models could have. Add to that the generative AI engines and a host of new applications, and we quickly have the potential for an enormous emissions problem. One of the ways to improve efficiency will be to redesign hardware and software for high-throughput workloads.

Data Center Computing Unit
In related news, Microsoft and Nvidia have announced a full stack deployment of Nvidia’s chips, networking, and AI software on the Azure cloud (similar to a deal Nvidia announced with Oracle). Offering full stack is a break from prior cloud AI configurations, which were disparate collections of computing, communication, and software, and this move may indicate a new paradigm for many AI workloads. We could end up with a scenario where there are large, proprietary AI stacks, like Google search, running on their TPU ASICs, and then broad, general-purpose stacks powered by platforms like Nvidia. Regardless of how AI computing systems progress, it’s a validation of the integrated strategy Nvidia has been pursuing for years. In May of 2020, we quoted Nvidia’s co-founder and CEO as saying: “The exciting thing for the world is the server is not the computing unit anymore. The datacenter is the computing unit. You are going to program a datacenter, not a server.” The tighter the integration between software, hardware, and communication, the more likely AI can achieve power efficiencies over time.

Synthetic Creativity
I’ve been eagerly awaiting this new Wired article from Kevin Kelly on generative AI. Kelly covers many of the topics I’ve explored in SITALWeek over the last six months while bringing his special ability to see the bluesky potential of disruptive new technology shifts. “Instead of fearing AI, we are better served thinking about what it teaches us. And the most important thing AI image generators teach us is this: Creativity is not some supernatural force. It is something that can be synthesized, amplified, and manipulated. It turns out that we didn’t need to achieve intelligence in order to hatch creativity. Creativity is more elemental than we thought. It is independent of consciousness. We can generate creativity in something as dumb as a deep learning neural net. Massive data plus pattern recognition algorithms seems sufficient to engineer a process that will surprise and aid us without ceasing...For the first time in history, humans can conjure up everyday acts of creativity on demand, in real time, at scale, for cheap. Synthetic creativity is a commodity now. Ancient philosophers will turn in their graves, but it turns out that to make creativity—to generate something new—all you need is the right code.” I covered many of the ramifications of generative AI for a broader array of design and engineering disciplines in the Next Video Toaster.

Glut of Music
Music streaming services now have over 100M tracks available, and that number is growing by an astounding 100k each day. Further, AI-generated music is just getting started, and already one AI track – complete with synthesized vocals – has surpassed 100M listens on Tencent music. If the stats from Music Business Worldwide are reliable, that implies the number of tracks could grow by 30-40% per year on top of nearly a century of recorded music.

Miscellaneous Stuff
Early Galactic Coalescence
The dark age of the Universe may have been shorter than previously thought – perhaps only 100 million years – thanks to new images from the Webb Space Telescope. Data on two early galaxies, from just 350Myr and 450Myr after the Big Bang (around 13.8B years ago), indicate earlier and more uniform galactic formation than anticipated. According to one researcher: “These observations just make your head explode. This is a whole new chapter in astronomy. It's like an archaeological dig, and suddenly you find a lost city or something you didn’t know about. It’s just staggering.” The galaxies are also brighter than expected, which should help astronomers locate even more. Further spectroscopic analysis of Webb's data will reveal details of the types of stars and elements present in the galaxies.

Stuff about Geopolitics, Economics, and the Finance Industry
Voter Tipping Point?
Reflecting on last week’s US midterm elections, I recalled a Big Think article (previously shared in #364) suggesting that the last decade has seen a much closer 50/50 split in right/left politics because of a generational and demographic tipping point that now will start leaning younger (which, for now, also implies more progressive on average) for the foreseeable future. This theory might be correct. I always tend to think values/ideologies vary most widely at the individual level, but surely there are some nuanced differences between generations. Time Magazine believes the current election data support an outsized impact by Gen Z, but it might simply have been a more general reflection of progressive ideas, notably concerning green energy and individual autonomy, beginning to steer the American zeitgeist. According to the Big Think article, this shift might at the very least lead to a little less polarization.

Comic Relief
Depending on your point of view, trolling large companies that do bad things can be very funny. It’s funny to me, which is why I enjoyed the “verified” blue check mark debacle on Elon’s Twitter that resulted in people mocking large, Industrial Age companies. The most prominent example was a parody tweet from someone pretending to be Eli Lilly proclaiming they were heretofore going to give insulin away for free. Eli Lilly was unamused. I, however, enjoyed a moment of levity watching all the big global consumer brands, advertising agencies, drug makers, etc. ride away on their high horses and declare their feelings had been hurt so badly that they would stop their Twitter advertising/participation because, apparently, this sort of parody is dangerous. The Information Age has created a paradox that few seem to understand: increased transparency exposes agents who are causing harm (or extracting too much value for themselves, i.e., negative-sum behavior), while the extreme velocity and volume of data transfer makes it exceedingly difficult to determine what is objectively knowable with any degree of certainty. In short, we are flooded with transparency and falsities at the same time.

Humans are storytelling machines – that’s how we came to rule the planet: language, imagination, and opposable thumbs. Everything is a story. We are always looking for stories, telling stories, and trying to convince people that certain stories are true or false. Largely starting in the 1970s, as the media and advertising industries came to increasingly dominate the social narrative (in the form of magazines, radio, movies, broadcast, and cable TV), for the first time since the Scientific Revolution our ability to distinguish truth from fiction started to deteriorate. With the advent of the Internet, social networking, and TikTok’s ultimate short-form storytelling, we are now so immersed in stories – eight billion people glued to little screens bombarding them with stories to be interpreted, adapted, and told to others – that an outside observer might assume that any sort of objective truth is irrelevant to societal function. However, while nearly all of the stories are indeed untrue in their entirety, they also have little relevance to anything existentially important. The sooner people are trained to understand this inconvenient truth about the media we consume, the sooner we can progress to the next societal phase. Whatever that phase may be, it's sure to be better.

If you follow enough stories, tell enough stories, and try to make connections between enough stories, eventually you might get a little better at identifying which stories have some chance of being true, or at least teasing out the bits that might be more firmly embedded in reality. Among other activities, that’s how I see the profession of investing. We tell stories when we buy stocks and assemble a portfolio, trying very hard to find objective threads of evidence we can feed into our narratives. Then we look really closely to see if the story is true or not for each investment, as well as whether or not the story that defines the portfolio in totality has a chance at being true. We try to examine where our stories are vulnerable, or overly precise, in their embedded predictions. Stories are the heart of our pre-mortem process. I’ve been known to inform prospective clients that I am telling them a story and that it’s their job to decide if it has a chance of being true. CEOs tell stories about their companies and cultures. Salespeople tell stories about their products and services. Customers tell stories about why they consume those products and services. Politicians tell stories about society today and in the future. Your view of your “self” and your place in the world is merely a long running narrative your brain tells you about your time on Earth so far, which itself is largely influenced by the stories other people tell about you.

Few people intentionally concoct elaborate lies (at least for very long – it takes a lot of mental effort). Rather, the vast majority of people simply don’t realize that they are telling stories that may or may not be grounded in truth. This society-wide storytelling is the essence of our culture, and it can change slowly or quickly depending on the stories people decide to agree on. I prefer stories with comedy, heart, and commentary on the world around us. While they don’t always turn out to be true, they tend to be more true over time than the stories filled with cynicism, pessimism, and despair. Of course, there are still some things held to be objectively true, especially following the Enlightenment and the Scientific Revolution. We should cherish those things, but also remain open to saying “I don’t know”, because sometimes even seemingly objective truths migrate over time with new evidence. Some special people have the power to will their stories into existence, but most of us aren’t even actors in the stories we tell, let alone writers or directors. The more quickly that humans can come to understand how the Information Age has informed storytelling – and its tenuous relationship with objective truths – the more we can appreciate the humor of it all and turn our attention to making progress on the real, challenging issues facing the world. So, that’s my, ahem...story, of why I enjoyed the recent parody trolling on Twitter: comedy is the best way to shine a light on anything uncomfortable or complex in the hopes of changing the future narrative for the better. In a little glimpse that perhaps, after all, comedy does have the power to change the narrative, the CEO of Eli Lilly, speaking at an event last week, declared that the Twitter fury over the parody tweet "probably highlights that we have more work to do to bring down the cost of insulin for more people."

✌️-Brad

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