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@pluralistic@mamot.fr · activity timestamp 29 min. ago

Any frank assessment of your achievements starts with an equally frank assessment of the world-historic forces attending those achievements. For example, I often tell young people who want to get into tech, "If you don't have the foresight and work ethic to be born in 1971, I can't really help you."

If you'd like an essay-formatted version of this thread to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2026/08/21/world-historic-forces/#multicore

An old-timey baseball player sliding into base in a great dust-cloud. The background is a high-magnification multicore CPU.

https://pluralistic.net

Pluralistic: Born on technology's third base (21 Aug 2026)

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oscarfalcon

@oscarfalcon@mastodon.social · activity timestamp 9 min. ago

@pluralistic

I am growing gray waiting for this effing hey-aye bubble to pop.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 28 min. ago

When it comes to tech, being born in 1971 - to a computer scientist father, no less - conferred a tremendous advantage for my career chances. My dad - a refugee - came to Canada at a time when post-war public services meant that he could become the first person in his family to go to university, all the way to a doctorate.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 28 min. ago

That set me up for life in a house where tech and education were all around me. Both my parents are teachers, both from working class families where no one had ever gone beyond high school, who found themselves in a time and place where it was easier than at any time in history for people from backgrounds like theirs to attend university.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 28 min. ago

I got to go to university, too, at a time when education was cheap enough that I could drop out of four schools before figuring out that it wasn't for me, and still be debt-free, largely thanks to income from a series of part-time jobs.

When I dropped out of my final degree program, it was to take a job in tech at a time when anyone with a little creativity, work ethic, aptitude and curiosity could walk into a career.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 28 min. ago

Millions of us did it, and I ended up working as a freelancer, then founding a startup, and then going to EFF. I know I work hard, I know I apply myself to understanding the world around me, but also...when it comes to this kind of career, I was born on third base.

There's plenty of this to go around. Think of boomers who bought their "starter home" with the income from their first job and traded it in for a succession of larger, nicer homes, each of which skyrocketed in value.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 27 min. ago

Some of those people fancy themselves to be veritable Warren Buffets for having had the shrewd financial insight that buying a house and living in it was a good idea. The *truly* smart ones know that they just got lucky.

There are world-historic forces all around us, creating moments and circumstances that contribute to the life-thriving of those of us who are lucky enough to be suited to the moment we find ourselves in.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 27 min. ago

Take computing: for decades, computing was ruled by Moore's Law, an unbroken run in which computers got faster and cheaper every year. If you were interested in the kinds of computing applications that were well-suited to serial computation - programs that worked best when run on a single computer - you were in luck.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 27 min. ago

Even if your application or field of study was expensive and difficult to realize on today's computer, you could just stand still for a year or two and a much faster computer would park itself on your doorstep, ready to solve your problems.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 27 min. ago

When Moore's Law tapped out - when the pace at which transistors got smaller and computers got faster slowed and plateaued, and the expense of even modest performance gains climbed infinitywards - computing changed with it. Parallel computing - putting more cores on a chip, more chips on a board, more boards in a system - took off, as chipmakers and system builders switched from a focus on building their computers tall to building them *wide*.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 27 min. ago

As parallel computing took off, so did parallel applications. This is the beginning of the graphics revolution, as GPUs - components made up of many, many low-powered computers - became more central to academic research and commercial product roadmaps. But it wasn't just graphics that saw a huge lift here: any task that could be parallelized got easier and cheaper to perform every year, in a steady trend that has run to this day.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 26 min. ago

This is the era of performance gaming, VR and AR, cryptocurrency, and, of course, AI.

In *What Technology Wants*, Kevin Kelly introduces the idea of the "adjacent possible" through the example of the helicopter. Da Vinci sketched a "helicopter" - blades in the shape of maple keys attached to a kind of wine-press screw - in the 15th century.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 26 min. ago

In the centuries that followed, many other people had the insight that twirling blades on a screw of some type could provide lift for some kind of heavier-than-air craft. But it wasn't until strong alloys, internal combustion engines and light, energy-dense refined hydrocarbon fuels came on the scene that the helicopter became possible, whereupon it was all but inevitable, with several people independently inventing the helicopter all at once:

https://memex.craphound.com/2010/10/13/kevin-kellys-what-technology-wants-how-technology-changes-us-and-vice-versa/

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https://memex.craphound.com

Kevin Kelly’s WHAT TECHNOLOGY WANTS: how technology changes us and vice-versa

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 26 min. ago

In the same way, the computing industry's focus on parallel computing made life easier for people who burned to do something parallelizable. Then the achievements of the parallel computing partisans drove more investment in improvements to parallel computing hardware *and* theoretical work on how to parallelize other problems.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 25 min. ago

This feedback loop raised the profile of parallel computing applications, attracting more bright and ambitious people to those applications, whose even more impressive accomplishments brought more people into the field, more capital into hardware development, and more resources to parallelization research.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 25 min. ago

The point being that world-historic forces, combined with accidents of history, shape the outcomes of individuals, companies and disciplines. It's much easier to be an accomplished graphics wizard in an era in which GPUs are doubling in power every year than it is in an era when linear computing is getting the lion's share of investment and improvement.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 25 min. ago

These forces and accidents have acted on AI in ways that profoundly shaped its development. The latest AI boom started when a group of machine learning researchers tried a minor variation on existing techniques and saw a *major* improvement in the outcomes. This is one of the most exciting kinds of breakthrough: if tweaking a single variable in a small way produces a large improvement, then it may be that further tweaking will produce even more improvements.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 25 min. ago

The minor variation that produced the major improvement in AI performance was *scale*. Prior to the "deep learning" era, AI research relied on a mix of hand-built models of reality and training data that computers fitted into those models.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 25 min. ago

Deep learning swapped the painstaking work of describing reality in software for a brute-force approach: throw *lots* more training data at the system and then throw *lots* more (parallel) computing power at that data and let the computer figure it out without your having to explain how the world worked.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 24 min. ago

The early gains from this were very exciting: they dangled the promise of software that could essentially "teach itself" how to do complicated, valuable things in a series of accelerating returns. The fact that the early improvements in AI systems that used this technique were *so* much greater than anyone would have expected based on AI research up to that point dangled an even more exciting promise: that the improvements would continue to scale faster than the inputs.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 24 min. ago

Researchers and investors came to expect an AI that was "untouched by human hands," that taught itself how the world worked. This was the self-licking ice-cream cone of machine learning, the world of "theory-free inference" that had fueled the Big Data industry.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 24 min. ago

With theory-free inference, you don't have to figure out how the world works in order to act upon it: you can just gather up all the data about how things happen in the world, use statistical methods to find the correlations, and then intervene to change the outcomes. You don't have to know why a molecule improves a medical condition - it's enough to discover that fact, produce that molecule, and administer it to people with that condition.

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Cory Doctorow

@pluralistic@mamot.fr · activity timestamp 24 min. ago

Lots of stuff in the world works this way. Our understanding of the causal relationships that make up reality has massive holes in it that we fill with mere correlation. Correlations are easier to discover than causes, and while correlation is (famously) not causation, causes and effects *are* correlated, and if you can evince the effect you're seeking without understanding precisely what happened to make that effect appear, well, at least you got the effect you were seeking.

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