You're Overspending in AI. Let's fix That

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You Might Be Considerably Overspending on AI. Let’s Fix That

You are overpaying for AI. Period.

And the reason is none other than your ChatGPT subscription. Not only do they force you to a single provider, which is already severely suboptimal because there’s no such thing as saying, “OpenAI is the best option for everything,” but you could be getting better outcomes at five times less of a price.

Or more.

You might say, well, it’s just $20 bucks, but what if I tell you that you could be spending $3 or $5 at most per month if you optimize your stack while actually getting better results? That’s hundreds of dollars in yearly savings per user, or even thousands if you’re currently paying for several subscriptions (ChatGPT, Gemini, Grok), all while also giving models the capacity to take action.

And let’s not open the enterprise subscription can of worms. In fact, if you don’t follow this approach, you will soon see yourself spending hundreds of dollars a month when you could be spending ten times less.

Thus, today, I present to you what I believe is the best combination of models you can leverage today, while ensuring that, combined, they cost less than your ChatGPT Plus subscription ($20/month). Furthermore, I will list the precise features that warrant paying the subscriptions for some of these products, providing you with a clear-cut algorithm of what to buy and when.

Let’s dive in.

The Goal? A Menagerie of Agents

The writing is on the wall. There will be no such thing as a “winner takes it all” event in AI, nor a model that is consistently better than all the others in every category.

It will be a menagerie.

All for one, and one for all.

Even when discussing the idea of Artificial General Intelligence, or AGI, the end goal of most AI Labs, they are starting to hint that this AGI thing, this ‘God AI’ could be a conglomerate of different specialized agents, not just one model, as posited by Greg Brockman, OpenAI’s President.

In other words, AGI could be a synergetic relationship between an AI super coder, a ‘know-it-all’ generalistic agent, a maths prodigy, and such.

The reason is that, as we’ve covered countless times, AI still suffers from a depth problem, where specialized models outcompete far smarter, generalized ones in their domain.

Disclaimer: By specialized models, I still mean foundation models, which are trained on a large amount of data and various topics, but crucially, then post-trained for a specific domain.

It has been widely proven that training a model from scratch on single-domain data is easily beaten by foundation models. So I’m not saying we shouldn’t train foundation models like the GPT or Gemini families, but these, despite being generally smarter, can be outcompeted by smaller foundation models with the right training despite being, in overall, ‘dumber.’

Using a human analogy, a woman trained in all the sciences who then gets a PhD in Physics…

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