It broke the internet overnight. A new artificial intelligence tool is going viral. It's called ChatGPT. It can answer follow-up questions; it can admit its own mistakes.

'write a poem about unicorns.' And it might spit out something that sounds just like it was written by a person. That really struck a chord with a lot of people.

It also struck a chord with investors. Tech companies and their investors have sunk billions of dollars into building

AI systems with a bet that these tools will keep getting more sophisticated and one day prove wildly profitable. There's this concept in AI that if you just keep feeding an AI model more and more data,

it will start to teach itself and become smarter and smarter. But progress seems to be getting a little trickier for some of these big companies. As expenses skyrocket, an industry known for moving fast and breaking things could be slowing down.

I think the progress is going to get harder. When I look at 2025, the low-hanging fruit is gone. They're trying to figure out how to make these models—that are getting increasingly expensive, eating up increasing amounts of computing power—worth that trade-off,

right? If you're not getting this incredible boost in performance, what do you do? AI really started in the 1950s, with Alan Turing being one of the early academics. You might be familiar with the Turing test?

Periods of AI innovation, and then what you might call an AI winter. But fast forward to today. In the past couple of years, this intense pickup in AI came from the breakthroughs we saw with OpenAI and ChatGPT.

Search, as we know it, is going to change, and the web as we know it is going to change. I was very skeptical; I didn't expect ChatGPT to get so good. When ChatGPT came out, it looked like, wow,

overnight we had this huge advancement—it sort of came out of nowhere for most people. I think it really kickstarted a lot of interest in innovating in AI, and also investing in AI, and companies trying out generative AI.

ChatGPT and AI systems like it work by using massive software engines called Large Language Models, or LLMs. A Large Language Model is an AI system that's trained on lots of data sourced from the internet, typically, and it can respond to written prompts

with amounts of text that sound really like they were written by a human. To do this, these models use an algorithm to process the parameters of any given prompt, and this is how that unicorn poem comes into being. And people keep expecting these companies to keep rolling out better and better

models that are more and more capable. What if I were to say that you are related to the announcement, or that you are the announcement? Me? The announcement is about me? Well, color me intrigued. For a while, these models have been getting better quickly.

But right now, at least three of the top companies—OpenAI, Anthropic, and Google—are having issues training their models to the level they would like. The easy gains are gone. We will keep scaling them, but it won't be at the

same rate of pure scale that we've seen in recent years. So we're working on new things, too. Good, curated data sets that are created by humans are increasingly scarce. I mean, the internet at this point has been largely scraped by these companies. So if

you want to make models better than what they are now, you need even more data. That's harder and harder to find. Some companies are going as far as paying people with advanced degrees to help train their models so that they can get this expertise to get better data.

So how do you continue to teach an AI? Where do you get that data from? Especially once this AI is getting so smart that it needs expert-level data—it needs the kind of data that a PhD student or a Nobel Prize winner would be

able to give you. How do you get all of that? How do you feed all of that when it's not as simple as just scraping the web anymore? Some projects are experimenting with what's called synthetic data, which includes training AI with content that is itself AI-generated.

Synthetic data basically means you take the output of an AI model and actually use that to start training more AI models. But, of course, that has its challenges, and that's still a technique that's being tested. And we don't know how much companies can really rely on

that or they will need to continue to also actually get human-created, higher-quality data. While we have seen meaningful new products and advances this year, tech companies are struggling to clear the high bar for improvements that would justify the tremendous amount of money they're spending. Because let's be real,

AI is not cheap. The CEO of Anthropic, which is a leading AI lab and probably a number two competitor to OpenAI in the startup world, has said it costs a hundred million dollars to train a new AI model, and in the coming years, that could increase to a hundred billion dollars.

As we're spending more and more money, the engineering complexity of getting it right is increasing, and that's why companies need to be larger, need to have more talent. It's hard to say if anyone's making money off of AI right now. We do know that

OpenAI has quite a number of paying business customers, but we don't know how that breaks down in terms of the exact number of companies that are paying for it, or what kind of benefits these companies are actually seeing. ChatGPT is

one of the most quickly growing consumer software products of all time, but when that will start to match the costs is in question. But it's not all about the money, is it? Let's keep in mind, OpenAI, for example,

was established as a non-profit research company focused on advancing AI for the benefit of humanity. Of course, it plans on shifting to a for-profit model, but the timeline on that is unclear. And still, despite the wintry forecasts, the money keeps pouring in.

They have plenty of money at the moment, but it's not totally clear if companies are going to be using it long-term, or what kind of return they're getting on their investment. These companies are having to do bigger and bigger fundraising rounds, billions and billions of

dollars, and at a certain point it's unclear where that money's actually going to come from if it's not coming from customers. Investors may be doubling down because of the promise of even more mind-boggling advancements just around the corner.

OpenAI came out with a new reasoning-based model recently. And the idea there is that if you give these AI models more time to sit and think about a problem, they can reason their way to a more accurate or

more intelligent answer. Another breakthrough that may be happening is also around things like agents, which are this idea of an AI that can just talk to you like a chatbot can, but actually complete tasks for you; like, let's say, book travel or actually integrate code into an

application. Perhaps the most exciting thing for AI enthusiasts and the most horrifying for its detractors is the prospect of artificial general intelligence, or AGI. AGI would be a system that can reason and think like a human, applying its

synthetic brain across disciplines. Not just reacting to prompts with poems or six-fingered hands, but accomplishing complex tasks independently of us humans and maybe even surpassing us. Could we one day be working for them? No one exactly agrees on when we're going to reach AGI or what exactly it will

look like. A lot of people are predicting that it can come very quickly. You see other people saying it could take decades, it could take a century. It could even never happen, and some of these setbacks that we're seeing give people reason

to maybe reassess or say, "Hey, this path to AGI may not be as simple as some people wanted."

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ChatGPT's Viral Success and Investor Interest

ChatGPT's ability to answer follow-up questions and admit mistakes made it viral.

ChatGPT's human-like poem writing impressed many.

Investors have poured billions into AI, betting on future profitability.

The belief that increased data and computing power would lead to self-learning AI is being challenged.

Challenges in AI Development and Progress

Progress is slowing as the low-hanging fruit is gone, and costs are rising.

AI's history includes periods of innovation and stagnation.

Recent advancements from OpenAI and ChatGPT have spurred interest and investment.

ChatGPT's rapid advancement surprised many.

ChatGPT boosted interest in and investment in AI and generative AI.

How Large Language Models (LLMs) Work and Their Limitations

LLMs are trained on massive internet data and respond with human-like text.

High expectations for continuous improvement of AI models are being challenged.

Top AI companies are struggling to train models to desired levels; easy gains are over.

High-quality data is becoming scarce, requiring more sophisticated methods to obtain it.

Companies are paying experts to improve data quality for training.

The need for expert-level data is increasing the difficulty of AI training.

Synthetic data, using AI-generated content for training, is being explored.

The reliance on synthetic data and the need for human-created data are uncertain.

Companies struggle to justify the high costs of AI development with sufficient improvements.

The cost of training new AI models is high and increasing.

The profitability of AI is uncertain, with limited information on business customer benefits.

ChatGPT's rapid growth needs to match the high costs of development.

OpenAI's non-profit origins and plans for a for-profit model highlight the complexities.

Despite challenges, significant funding continues to flow into AI development.

Large fundraising rounds raise questions about the long-term sustainability of funding.

Promising Advancements and the Future of AGI

New reasoning-based models allow for more thoughtful and accurate responses.

AI agents can interact like chatbots and complete tasks.

Artificial General Intelligence (AGI) is a potential future development, with varying predictions on its timeline.

Recent setbacks may cause reassessment of the path to AGI.