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AI Is Eating Software – Which Already Ate the World
Read on for the inevitable collapse of the software engineering profession
I’m Pascal Finette, co-founder of be radical – and this is our weekly Briefing. We share our latest insights, analysis, and articles we read; all focussed on the future of technology and business. Just like a good banana, it’s easy to digest. nutritious and yummy.
Decode. Disrupt. Transform.
Like many of you, we at be radical have been fascinated lately with thinking through the rippling implications of today’s (and tomorrow’s) LLMs – the large language model AIs that have captured both the collective imagination and a huge amount of investment recently.
One implication that I keep coming back to is what all of this could mean for software production in the next few years and then what all of that could mean for any number of businesses.
The key argument here (which I first heard sketched by Paul Kodrosky & Eric Norlin of SK Ventures) goes like this. In the past several decades, we’ve seen the cost of key technological inputs involved in digital innovation and software production collapse. The costs of computation, data storage, and networking have all declined dramatically – transformationally, even – making new things, including the creation of wildly valuable new digital products, services, and platforms possible.
But all the while, one other key factor has remained very scarce and very expensive – software engineers.
This picture may be about to shift significantly. Early studies suggest that programmers using new tools like GitHub Copilot (with the AI acting as a pair-programming partner) are reporting significant productivity increases.
Quite a bit of coding is the kind of thing (grammatical and predictable) that LLMs should be able to do very well in time. That should make aspects of software development cheaper and faster and easier to do. As Rodney Brooks, who is generally on the more skeptical end of the continuum regarding the potential of LLMs, has written: “It is going to be easier to build from scratch software stacks that look a lot like existing software stacks.”
That’s not nothing, friends. Existing, known tech stacks will become easier to reproduce. Development cycles will accelerate. Some barriers to entry will disappear.
There are many, many potential upshots here, but I’ll highlight just two for now. In this scenario, a couple of things will become more important than ever before for businesses of all types:
1. Holding unique, high-quality, high-value data that doesn’t exist in any other set of training data
2. Continuing to know your customer better than anyone else in the world can
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The Thin Wisps of Tomorrow
Globalization changes shape but doesn’t reverse. In a recent paper by Thun et al., the research team introduced the concept of “massive modularity” – a form of economic coordination that accommodates complexity at scale and is a signature feature of multilayered component ecosystems underlying the ICT hardware industry (and arguably many others). Modularity, or in be radical’s verbiage, “stacks,” is a core enabler for companies to operate efficiently and effectively in increasingly bifurcated markets – small, nimble, niche players at the top, and large, vertically-integrated companies at the bottom and middle of the market. Massive modularity also means that processes are increasingly hard to decouple and reshore – a counter-indicator to the growing near-shoring narrative.
Direct-to-Consumer brands are going direct(er) – by manufacturing themselves. DTC has been a massive trend over the last couple of years and one which accelerated the great bifurcation of consumer markets (our Hourglass Economics model). Now the companies are starting to vertically integrate in an effort to better control quality and increase profits – which should prove further trouble for the companies stuck in the fat middle of “value for money” (or maybe more accurately, “a blend of bland, chill, premium mediocre).
Surprise, Surprise: Startups, Not Incumbents, Drive Commercialization of High-Impact Innovations. In a recent paper, Kolev et al. show the growing importance startups play in commercializing high-impact (and oftentimes disruptive) innovations. The reason is simple: Startups have more incentive than incumbent firms to engage in potentially disruptive R&D because large, established firms have more to lose from the discovery of new technologies that replace traditional ways of doing things. With no existing operations, startups have nothing to lose and much to gain from disruptive innovation. (via NBER) Lessons for incumbents: Become good at partnering with startups.
What We Are Reading
🇪🇺 EU moves closer to passing one of world’s first laws governing AI The European parliament moves to approve rules aimed at setting a global standard for the technology, which encompasses everything from automated medical diagnoses to some types of drones, deep fakes, and bots such as ChatGPT. If approved, it would also include a blanket ban on police use of live facial recognition technology in public places. Jane ⇢ Read
🔋 The Real-World Costs of the Digital Race for Bitcoin Bitcoin mining companies use a disproportionate amount of energy, yet ironically they are also profiting from catastrophes by promising to quickly shut off power to prevent blackouts across the nation. These companies have made hundreds of millions by reselling electricity to unknowing civilians. Mafe ⇢ Read
🚗 Car-Rental Companies Are Ruining EVs The rapid adoption of EVs by rental car agencies may be producing an unintended consequence: negative first experiences that could significantly deter prospective future buyers. Jeffrey ⇢ Read
🏪 Brands Wanted to Cut Out Stores. Not Anymore. We’re quick to make assumptions — as the surprising strength of brick and mortar shows. Individual metrics correctly suggested a move away, but jumping to general conclusions was proven premature. Julian ⇢ Read
🎶 Paul McCartney using AI to create “Last Beatles Record” Paul McCartney has revealed that he used AI to create what he calls the “last Beatles record,” featuring John Lennon’s voice that was taken from old demo tapes. I hope this new song “Doesn’t Let Me Down” 😉 Pedro ⇢ Read
🧠 Brain Waves Synchronize when People Interact Synchrony – the linking up people’s brains during social interaction, has been linked with empathy and cooperation. This phenomenon has been observed in both face-to-face and remote interactions. Pascal ⇢ Read
Around The Horn
Facebook released an LLM specifically trained to generate music – it’s pretty good.
Text-to-Image AI now runs on smartphones, generating images in less than 2 seconds.
LLMs are magical – at tricking you.
LLMs are also biased – maybe more so than humans.
Here’s the surprising use of LLMs in the ER.
And an argument that the “unoriginality” of LLMs is precisely its defining characteristic. (via our Learning Partner Richard Hammond)
Electric cars are better for the environment – full stop.
Some Fun Stuff
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