Regarding AI
As a lurker in the tech-adjacent part of the Internet, I can’t seem to catch a break from reading takes on AI. I’ve heard it all, ranging from the perspectives of artists and creatives to the hot takes of L5 MANGA SWEs. Now feels like the right time to offer yet another take—if only so I can look back in 5 years and laugh at how much I got wrong.
Regarding Bubbles
When a new piece of technology comes out, perhaps the most important question to ask is: “is this a bubble?”. While it may seem obvious in hindsight, it’s incredibly difficult to tell in the moment, despite the efforts of many (financially) interested parties. Perhaps a few years ago, crypto was seen as the hot new thing—yet most would agree that it was a bubble and is largely irrelevant nowadays, with the exception of some niche applications far from the widespread use cases championed by crypto evangelists. The fate of virtual reality, a similarly-hyped technology, has yet to be determined. Generative AI seems somehow distinct from the former two examples; it seems to fit more in the category of “steam engine” and “World Wide Web” rather than the likes of “Non-Fungible Token.”
I think a distinction is necessary between the developers of foundation models and the secondary/tertiary repackagers of generative AI. Though they are far from profitable, I do not see OpenAI, Google, or Anthropic going away any time soon. However, my prognosis of “B2B SaaS” companies like Cursor, Windsurf, or the viral Cluely (to use a humorous example) is not very positive. “ChatGPT wrappers” are a dime-a-dozen and offer little to no added value.
Let’s delve into my example of Cursor. While definitely a novel technology, Cursor offers few features that any other company couldn’t immediately clone. In fact, as someone who never made the jump to Cursor from Microsoft’s Visual Studio Code, I’ve personally watched VSCode catch up to Cursor in only a few months. Given that Cursor is a fork of VSCode, this seems like a classic example of Microsoft’s not-so-secret strategy: embrace, extend, extinguish.
ChatGPT wrappers are simply too easy to create that I seriously doubt the long-term viability of such companies. On the other hand, OpenAI, Google, and Anthropic’s value proposition is not one that any company can create overnight. The high costs of training a sufficiently large model creates massive barriers to entry that small companies simply can’t compete with, which is precisely the reason why I think they’ll be sticking around. But these same barriers to entry represent some of my largest fears about AI.
Regarding Barriers to Entry
It’s easy to understate the power of the open-source software community. To my knowledge, no other industry has such an amazing implicit agreement and expectation of collaboration above profit. For example, Meta decided to open-source React, giving us the most powerful web framework ever made; Google decided to open-source Chromium, giving us Electron apps like Slack and VSCode; and hobbyist devs contribute to libraries like ffmpeg and tz, which hold up modern computing as we know it. I see foundation models as a threat to this open-source ecosystem.
Perhaps open-source software flourished because code is inherently democratizing. As long as you have a good enough machine, you can run any piece of software. This suddenly becomes untrue as soon as we introduce compute-heavy tasks like training, or even running, a foundation model. Although open-source, local models exist, none will ever come close to the power of OpenAI, Google, or Anthropic and their GPU farms. If AI is truly the next big step in our tech tree, gone are the days of cloning a repo and running it on your own machine. If you want to do anything with powerful AI, get ready to enter your credit card information—and make sure to stay on OpenAI’s good side.
Perhaps I’ll end this section with an appeal to emotion. Think of any other industry with high barriers to entry in your life. What’s your general sentiment towards cell service providers? How about airlines? That’s what AI will be like in a few years.
Regarding AI Taking Our Jobs
With the risk of sounding like a detached economist, blissfully unaware of the very real lives of actual laborers, I generally am unconcerned with “AI taking our jobs.” As a probable future software engineer, I’m definitely in the crosshairs. But to create policy based on the fear of AI replacing human labor is misguided.
Although I generally reject the premises of Laissez-Faire ideology, I do think there’s some truth in the saying a rising tide lifts all boats, albeit with a slightly different interpretation than originally intended. Technology is the rising tide. The Industrial Revolution eliminated dangerous manual labor positions, yet introduced many more jobs that were before unseen. (Although perhaps not less dangerous.) The invention of the computer eliminated plenty of administrative desk jobs, yet created entire industries of IT and software. It may be the case that AI will take jobs, but such is a “necessary evil” in the progression of society. People will become more educated and more will be well-equipped for the jobs that AI creates. Holding back AI because of the loss of jobs will slow down the creation of new ones—jobs that contribute more to society.
Although we’re still a ways away from this utopia, the ideal end goal of this progression of technology is the replacement of all jobs that only exist out of necessity. Just like computers [sense 1] were replaced with computers [sense 2], doctors and lawyers will be made unnecessary, freeing up time for humans to do what they were always meant to do: create art.
Regarding Computers Making Art
Speaking of making art, a new cultural divide is emerging between creatives and technologists. Can a computer create art? Should a computer create art? My position is still forming, so I will instead offer a series of questions.
Is the invention of image-generating AI different than the invention of the camera? What did people think when the camera was invented, and how does that mirror/differ from the sentiment towards image-generating AI now?
What is the human brain other than a computer that takes inputs, combines them with memory of previously-seen works (commonly known as “inspiration”), and produces outputs?
Is an art piece’s value based solely off of how good it looks, or is there some other mushy factor that gives it worth?
Can a computer feel?
Perhaps this discussion is gratuitous. Perhaps “computers making art” will be a short-lived fad, overshadowed by something much more impactful, yet much less talked about…
Regarding Non-Generative AI
I think recent advances in non-generative AI has largely flown under the radar. Amazing breakthroughs are being made in medical and scientific industries. Have you heard the latest about protein folding or cancer classification? These advances are what is going to drive forward technology and society, not “Ghibli filter” bots.
Regarding The End of AI
Many people point to the exponential curve of AI ability and assert that progress will never slow down. While I’m not certain, I believe AI development will reach an asymptote due to model collapse.
Garbage in, garbage out. AI output is being put on the Internet en masse, with no real way to tell whether a piece of content is human– or AI–generated. As we all know, large-data models necessitate huge amounts of training data, scraped from Reddit and the likes. But what if Reddit is all AI content in the first place? Studies have shown that training AI on AI output does not improve it. It may be the case that we are approaching the maximum capacity of AI, unless we can solve the Dead Internet problem.
Conclusion
I’d love to hear your thoughts, especially if you are deeply immersed in the field of AI. Reach out: eric@yoonicode.com