AI is a horse (2024)

Bu the feeling I'm having with LLMs is that we've entered the age of fossil-fuel engines: something that moves on its own power and produces somewhat more than the user needs to put into it. Ok, in the current version it might not go very far and needs to be pushed now and then, but the total energy output is greater than what users need to put in. We could call it a horse, except that this is artificial: it's a tractor. And in the last months I've been feeling like someone who spent years pushing a plough in the fields, and has suddenly received a tractor. A primitive model, still imperfect, but already working.

[0] https://locusmag.com/feature/commentary-cory-doctorow-revers...

My favorite quote from the excellent show halt and catch fire. Maybe applicable to AI too?

We should use analogies to point out the obvious thing everyone is avoiding:

Guys 3 years ago, AI wasn’t even a horse. It was a rock. The key is that it transformed into horse…. what will it be in the next 10 years?

AI is a terminator. A couple years back someone turned off read only mode. That’s the better analogy.

Pick an analogy that follows the trendline of continual change into the unknown future rather then an obvious analogy that keeps your ego and programming skills safe.

I've found that the key is treating AI like a junior developer who's really fast but needs extremely clear instructions. The same way you'd never tell a junior dev "just build the feature" - you need to:

1. Break down the task into atomic steps 2. Provide explicit examples of expected output 3. Set up validation/testing for every response 4. Have fallback strategies when it inevitably goes off-road

The real productivity gains come when you build proper scaffolding around the "horse" - prompt templates, output validators, retry logic, human-in-the-loop for edge cases. Without that infrastructure, you're just hoping the horse stays on the path.

The "it eats a lot" point is also critical and often overlooked when people calculate ROI. API costs can spiral quickly if you're not careful about prompt engineering and caching strategies.

It could go and do the task perfectly as instructed, or it could do something completely different that you haven't asked for and destroy everything in its path in the process.

I personally found that if you don't give it write access to anything that you can't easily restore and you review and commit code often it saves me a lot of time. It also makes the whole process more enjoyable, since it takes care of a lot of boilerplate for me.

It's definitely NOT intelligent, it's more like a glorified autocomplete but it CAN save a huge amount of time if used correctly.

> (The horse) is way slower and less reliable than a train but can go more places

What does the 'train' represent here?

A guess: perhaps off-the-shelf software? - rigid, but much faster if it goes where (/ does what) you want it to.

Another one I like is "Hungry ghosts in jars."

https://bsky.app/profile/hikikomorphism.bsky.social/post/3lw...

Horse rumours denied.

I‘m not able to follow. So AI is a horse in this metaphor, what is a train then? Still a train?

Requires ammo (tokens) which can be expensive.

Requires good aim to hit the target.

Requires practice to get good aim.

Dangerous in the hands of the unskilled (like most instruments or tools).

https://en.wikipedia.org/wiki/Clever_Hans

^^ We are skeptical of AIs (and people) that claim they have consciousness ;-)

On AI with no name

It felt good to be out of the rAIn

In the desert, you can remember your name

'Cause there ain't no one for to give you no pain"

My term of “Automation Improved” is far more relevant and descriptive in current state of the art deployments. Same phone / text logic trees, next level macro-type agent work, none of it is free range. Horses can survive on their own. AI is a task helper, no more.

I have to start doing this for "top level"ish commentary. I've frequently wanted to nucleate discussions without being too orthogonal to thread topics.

But there is a price to be paid. Metaphors can become confused with the things they are meant to symbolize, so that we treat the metaphor as the reality. We forget that it is an analogy and take it literally." -- The Triple Helix: Gene, Organism, and Environment by Richard Lewontin.

Here are something I generated with Gemini:

1. Sentience and Agency

The Horse: A horse is a living, sentient being with a survival instinct, emotions (fear, trust), and a will of its own. When a horse refuses to cross a river, it is often due to self-preservation or fear. The AI: AI is a mathematical function minimizing error. It has no biological drive, no concept of death, and no feelings. If an AI "hallucinates" or fails, it isn't "spooked"; it is simply executing a probabilistic calculation that resulted in a low-quality output. It has no agency or intent.

2. Scalability and Replication

The Horse: A horse is a distinct physical unit. If you have one horse, you can only do one horse’s worth of work. You cannot click "copy" and suddenly have 10,000 horses. The AI: Software is infinitely reproducible at near-zero marginal cost. A single AI model can be deployed to millions of users simultaneously. It can "gallop" in a million directions at once, something a biological entity can never do.

3. The Velocity of Evolution

The Horse: A horse today is biologically almost identical to a horse from 2,000 years ago. Their capabilities are capped by biology. The AI: AI capabilities evolve at an exponential rate (Moore's Law and algorithmic efficiency). An AI model from three years ago is functionally obsolete compared to modern ones. A foal does not grow up to run 1,000 times faster than its parents, but a new AI model might be 1,000 times more efficient than its predecessor.

4. Contextual Understanding

The Horse: A horse understands its environment. It knows what a fence is, it knows what grass is, and it knows gravity exists. The AI: Large Language Models (LLMs) do not truly "know" anything; they predict the next plausible token in a sequence. An AI can describe a fence perfectly, but it has no phenomenological understanding of what a fence is. It mimics understanding without possessing it.

5. Responsibility

The Horse: If a horse kicks a stranger, there is a distinct understanding that the animal has a mind of its own, though the owner is liable. The AI: The question of liability with AI is far more complex. Is it the fault of the prompter (rider), the developer (breeder), or the training data (the lineage)? The "black box" nature of deep learning makes it difficult to know why the "horse" went off-road in a way that doesn't apply to animal psychology.

Either way it is an imagined end point that has no bearing in known reality.