After a brief hiatus due to me (Pascal) traveling in gorgeous Patagonia, your weekly radical Briefing is back. Two weeks on a long-planned adventure trip with my 80-year-old dad in the completely reception-less backcountry of Patagonia was a welcome reset and something I highly recommend. Life is indeed short, my friend, and as exciting as our technology-driven world is, nothing beats watching a calving glacier up close for hours at a time.
Read on to explore the more significant and longer-term implications of large language models.
A couple of months ago, we suggested here that the major LLM players were rushing us into a “move fast & break things” era of generative AI. Not the boldest of prognostications, but one that seems to be holding up pretty well. You can find your scorching hot takes on the near future of AI elsewhere this week; instead, I’ll try to offer a few more commonsensical positions that feel likely to prove durably correct.
Working out from that starting point, it’s quite clear that the field is moving fast (and accelerating) and likely to break some important things, but it remains remarkably – and somewhat fascinatingly – unclear exactly what it is that will be broken. Web search? The larger information ecosystem? Democracy? The global economy?
How about the established cost structure of creating new software? As analysts at SK Ventures argued last month, there’s a distinct possibility that systems increasingly capable of generating viable, explainable code in response to natural language prompts will radically reduce the cost of (initially, low-level) software engineering work. That work is highly structured, grammatical by definition, and predictable when dealing with well-understood problems – precisely the kind of work that LLMs are poised to do most effectively and, eventually, reliably.
Two interesting upshots here: The first is that we might then see the cost of software engineering collapse in a way reminiscent of what we’ve already seen with computing, storage, and networking. The second, as Pascal pointed out in one of our community exchanges recently, is that systems that might (relatively) soon be able to generate the code for most any app would seem for present and future startups to be a slippery foundation upon which to build new digital products and services.
Taken together, all of that would also further argue for the absolutely central importance of holding unique, high-value data as a/the core asset of the business when the other costs of digital innovation are continuing to collapse – and with them, technical barriers to entry. It will be interesting to watch the Bloomberg example for lessons.
One more while we’re here. I heard my friend Alexandre Nascimento (who, unlike me, actually IS an AI expert) tell a roomful of executives in Brazil last week that while AI systems won’t take their jobs, executives who effectively use AI systems just might. That struck me as a nice formulation and another prognostication likely to hold up pretty well. (via Jeffrey)
☝️ Don’t Learn the Wrong Lessons from Failure There are several common traps that leaders fall into when attempting to learn from failures. The authors outline these learning pitfalls and suggest how to better leverage them effectively for future success. Jane ⇢ Read
🖼️ From Balenciaga Pope to the Great Cascadia Earthquake, AI images are creating a new reality As AI image generators rapidly become more sophisticated, their creations might outpace our ability to adjust to a flood of believable but completely false images like Balenciaga Pope, and the ‘Great Concordia Earthquake’ which never happened. Mafe ⇢ Read
🦣 Would you eat a lab-grown woolly mammoth meatball? Emerging tech might finally allow you to taste a woolly mammoth meatball. What a time to be alive, friends! Jeffrey ⇢ Read
🙊 Amazon’s big dreams for Alexa fall short Tech analyst Benedict Evans noted that for many users, Alexa is just viewed as a “glorified clock radio,” but Amazon’s voice assistant has been an extraordinary success, leading the US market with a staggering 66 percent share and controlling over 300 million smart devices; however, it remains to be seen how this market is going to change. Pedro ⇢ Read
The AI hype bubble is the new crypto hype bubble When Cory Doctorow speaks, I tend to listen. After his brilliant insight into the “enshitification” of TikTok, he turns his attention to AI. Pascal ⇢ Read
👋 So long NFTs… We barely got to know you…
🐣 Cyber criminals are coming for your chicken.
👓 Fairly simple idea, not hard to build with today’s tech stack – and hugely impactful… AND, finally, a truly good use for AR glasses.
🤖 And when Jaron speaks, we also listen: The danger isn’t that AI destroys us. It’s that it drives us insane.
🚽 A smart urinal (which apparently is a thing now) stuck on boot sequence.
👮 What happens when linear government meets exponential tech: Italy uses GDPR laws to ban ChatGPT.
💥 Here is an anime dating sim that also does your taxes.
This is why movies are so dark these days. 🎬
|🏴☠️ The Heretic: Der Fisch Stinkt Vom Kopf||🎧 Listen to the audio version|
🧨 Disrupt Disruption - The Podcast: We brought Christina Nesheva, CEO at Officinae Bio, back to talk about leadership. Listen now.
📕 Disrupt Disruption – The Book: Get your copy of our bestselling book and learn how to decode the future, disrupt your industry, and transform your business here.
Radically yours, take good care, friend!
— Pascal, Mafe, Pedro, Vivian, and the three Js (Jane, Jeffrey, and Julian)