Time for another AI post. For brevity, let’s agree that the following discussion is centered on the games industry (though a lot of the points are applicable to broader domains). This is a bit of a free-flowing post, but the central theme is be about the work relationship between human talent and AI.
These days we talk a lot about how AI will or will not replace jobs (and what kinds of jobs). One of the common optimistic opinions goes something like this:
- Games are entertainment products serving human consumers.
- The commercial viability of a game – whether human consumers prefer it (over alternatives) – depends on the game’s ability to identify and satisfy complex human motivations and needs. Put more simply, human taste is key to the creation of fun.
- Taste itself cannot easily be replaced by AI; thus, the future of game dev is not humans replaced by AI, but rather, humans directing AI and humans exercising taste.
This is a train of thought that I largely agree with. But it is possible to poke some logical holes. For one, from a “jobs to be done” theory perspective, there are lots of occasions where the job a game is “hired” to do does not require sophisticated taste in execution – for example, if the primary need is to “kill time”, then the consumer is often not seeking an intricately crafted experience. But, we can also argue that the key insight here – applying the “jobs to be done” framework to discern consumer needs – is itself an exercise of taste (or more precisely, judgment).
Before going further, let’s distinguish taste from judgment (and briefly discuss how they are formed). For me, taste is subjective. It is “beauty in the eye of the beholder.” I associate it with creative expression. There is no objectively good or bad (right or wrong) taste, but there are certainly societal opinions around “good” or “bad” taste. For commercial concerns, it is critical to understand how popular / niche a certain “taste” is (and this is often where we are surprised).
Regarding how taste is formed, I strongly believe that you can’t skip the experiential part. It’s not just about media literacy (playing lots of games if you want to make good games). You have to feel it for yourself, and your emotional journey is part of the taste alchemy in your head. Watching a gameplay video instead of doing a full play-through yourself (or say, watching a video essay about a film instead of the film) are not the same. Your emerging taste, formed from passive consumption, is then refined when you exercise creative expression; the community reaction to your work then completes the feedback and learning loop for your taste.
Judgment is more objective. It is exercised in decision-making and STEM-related problem-solving. We can often verify with measurable data whether the judgment was sound or not (however, beware of pitfalls – a reckless decision can get bailed out by random luck).
Since I’ve defined problem-solving as part of judgment, the typical learning model shows one path to forming good judgment in a specific domain – understand the fundamental concepts, study previous examples to see the concepts in practice (and learn derived principles / mental shorthands), and then work through new problems to reinforce the lesson.
Going back to the train of thought earlier: how defensible is “taste / judgment cannot easily be replaced by AI”?
Here, judgment seems like the weaker plank to discuss first. Chess is an example of a “solved” problem where humans are no match to machines, even prior to modern day AI. (For those of you out of the loop on chess these days – 30 years after Kasparov vs Deep Blue, the world’s best players simply have no chance against your average laptop running the open-source Stockfish chess engine.) And for domains that involve hidden information and randomness, as long as we can articulate a rational decision-making framework, it seems plausible that we can hand it off to software (AI or not) to ruthlessly exercise rationality.
But – this sounds like one of those economics theories (people are rational actors / efficient markets etc.) that fail in practice. As long as the domain concerns humans as stakeholders, and humans act with varying irrationality, good judgment without human intervention remains out of reach.
To give a trivial example: I recently tried to automate some meeting scheduling with an AI agent. After jumping through some technical hurdles, I ended up in a place where I realized I was a frivolous stakeholder – I was constantly nitpicking the agent’s proposed scheduling. Don’t schedule these two meetings back-to-back (but those other 2, that’s ok). Don’t book me 8 meetings a day. Mondays and Fridays are off-limits… Some of these criteria can be turned into explicit rules, but getting from 90% to 100% seems impossible (because it’s up to my whims).
How about taste then? Shouldn’t this be inherently harder for AI to replace since it’s subjective and personal? Maybe… But as a counter-argument: doesn’t the fact that so many people are forming digital relationships with AI show that AI, as-is, is already quite adept at emotional engagement? And so, while they may not be proven capable of predicting or defining a new popular taste, they seem to have some innate ability to understand existing taste.
Where am I going with this? I don’t know, exactly. I started out in one direction, but I seem to have argued myself into a slightly different space. But zooming out to the macro level, I think “humans directing AI and exercising taste & judgment” is still a solid-enough paradigm to work towards (for now).
There is one last bit to talk about regarding human talent and AI. Many have called out the risk of AI to the development of junior talent. The old ways of on-the-job learning is obsolete; and even worse, in the eyes of an experienced dev who has successfully navigated the transition to AI, adding a junior talent may be strictly worse than adding a few agents to the fleet. So there is a question of whether our human capacity in a domain will wither as new talent are not grown.
I think this risk is real, but it is overblown. The real impact is it skews the economic opportunity towards a more specific type of talent. The type of person who has deep natural curiosity and the drive to do stuff, and whose experience is self-taught. These kinds of people have always been in high demand, but in the AI age they are even more valuable. Yes – organizations still need to figure out how to turn fresh college grads into veterans, but these unicorns who’ve been tinkering with AI since their teens will also have a decade of experience by their early twenties. Curiosity, innate drive and self-learning mindset – are you born with these attributes, or can we nurture them?