AI predictions for 2024
Is 2024 going to be a crazy one?
I hope it's not too far into the new year to make my own prediction about AI. I want to believe I have something to contribute to this topic as I am thinking about this a lot and have some experience in products and AI research.
I predict while there will be some progress in 2024, we have mostly flattened out.
I also predict the real opportunity for further progress is elsewhere than where people are looking today.
All this might be wrong, and 2024 can well turn out to be a wild one. That's ok. The future of the internet in 2000 was hard to predict. Still, it's good to give it a try:
We have mostly flattened out
By this, I mean "What is here today and working is to stay" + "There is not much new coming beyond that very soon". This means no new radical AI products tomorrow—no wide disruption of tech or society. No singularity is happening.
I am not excited about that, but there are two reasons why I think this:
First, in the past year, entrepreneurs, big and small, tried their best to use LLMs to build new or disrupt old products. Some of this stuck, but much of it didn't. Why something possible in 2023 would suddenly take off in 2024? One year might not sound like a lot, but given the amount of attention and volume of things tried, it makes some precedent.
Second, I am unaware of any significant technological breakthroughs that would unlock new product categories. I like to follow the research community quite closely. It usually gives foresight at least 2-3 years ahead of tech product adoption. My read is that we have stuck with the current state of AI tech for some time.
Things will still get incrementally better, though, mainly through continuing bundling the existing stuff. Techniques you could see before in a 2023 demo or research paper will become more mainstream. This will result in higher benchmark numbers, more inputs/outputs of different kinds, and more integrations where LLM is both at the top and bottom of the application stack.
Given the slowdown, the field will likely continue to catch up. The performance of models will become more competitive between dominant players. Google’s will be comparable with OpenAI. Apple will launch LLM of its own. You will also continue to do more of that with open source. Nothing of this, though, will change the fundamental question of product use cases.
How does this translate to AI having a disruptive effect on the world?
For a broad transformation, a well-defined thesis should come true. Looking back at the internet era of 2000. The thesis was "everything is much better online." And this thesis indeed came true for many things. One by one, you would better shop, make bookings and do business online in a digital way and over email than how you did it before.
"Is everything much better with AI?"
Similarly, for AI to create a fundamental transformation, the thesis is "everything is much better with AI". This thesis is certainly actual in the long run. But it's not the correct thesis to think about today. AI will be limited to the current LLM technology without further fundamental breakthroughs. The thesis should, therefore, be" everything is much better with the current version of LLMs". Suddenly, things are not looking as rosy as only some things seem to be much better with them.
Let’s look more closely at some of those:
Things that work today
A handful of use cases exist where current LLMs make a better product. Those are already popular today. Things like writing SEO blog posts are significantly faster with LLMs. Similarly, copilots for coding or customer service are picking up.
The common pattern is that these tasks are centred around text/voice communication and that you usually hire groups of other people to do it for you. And the work is often reviewed word-by-word. It makes sense if LLMs make the review and suggestions for you!
Things that don't work and why
Most other use cases don't have their disruptive LLM-enhanced product yet.
1. It's hard to beat a dashboard.
One key reason is that the LLM experience needs to be significantly better than its main competitor: An existing well-designed dashboard for the task. Think about that. It's tough to beat a great dashboard! Any serious conversation leading to decisions eventually turns into figuring out and analysing data. It's not a chat interface anymore. The most suitable format to present such information is a dashboard. LLMs may be able to generate these dashboards for you as well. It will be a while until they are much better than existing product dashboards refined over time.
2. Search is still a king.
The above is true for search as well. I am not bullish that LLM chats will replace search very soon. When people search, they like to see different options, dig deeper to understand them and then make a choice. The existing search interface provides this experience. LLM chat can undoubtedly enhance it and make it better, but not replace it.
3. Automation is more difficult than you think.
It's also hard to fully automate things and, this way, create entirely new product categories. This is because building generally robust AI applications is very hard. I wrote about why this is the case here.
Is there an opportunity?
So, where do we go from here? I see four avenues where progress can be successfully made in the near term. Maybe not yet in 2024, but soon.
1. LLM-features in existing products
Instead of looking for new radical LLM-first products, narrowing the focus is better. Get back to basics and think from first principles about individual features in existing products that can now exist that could not exist at all before. Experiences even the best dashboard or GUI could not previously deliver or technology to support. Why using them would be compelling?
2. Narrow and deep vertical AI
Going extremely deep on an important vertical and building dedicated AI can unlock new results impossible to achieve before. Think about AlphaFold. This likely does not lead to a consumer product you can directly experience every day. Its impact, though, can be significant. It requires a deep understanding of the problem and can also be capital-intensive. But hey, if it were easy, everyone would do it.
3. New kinds of datasets
Imagine a future where you give LLM a complex task you usually do in your day job that takes several hours to complete. Doing it might involve interfacing multiple apps, replying to emails, figuring out necessary information from your colleague through Slack, etc. The work of many office workers looks like this today. After some computing, the LLM presents your plan for review, which you approve, and goes to work. In the meantime, you get coffee and watch sports while LLM does the work that previously took you all day.
“For broad automation to progress, a new dataset is necessary to capture how people use their computers daily to accomplish tasks.”
A new kind of dataset is necessary to make this possible in all its variety and using only the current technology: the one that captures how people use their computers daily to accomplish all these tasks. What they see and what they click/type as a result. An LLM trained on it should be able to construct predictions and plans. It would need to be a big one to work. Collected over millions of people, thousands of hours each.
Nothing like that kind of dataset exists today. Not sure if it ever will. It will be immensely useful, but it will have tons of data privacy issues. Humans, however, can accomplish all these tasks without training on all these data. Do we need to wait for the next core technology breakthrough for this to be possible for AI as well?
4. New physical interfaces
You might use technology differently if new physical interfaces are available to you when old ones are not. Do you remember the first iPhone in 2007? That's what I am talking about. Multi-touch became the default way you use it. This is despite the phone with multi-touch is less efficient than using a computer with a mouse and keyboard. The computer was just not with you all the time. Phone was.
A similar thing can happen if augmented reality glasses become more prevalent and with you constantly. The most convenient way of interfacing with them might be through LLM conversation. This is regardless of whether this is the best solution for any task. The success of such interfaces is very difficult to predict until you compellingly experience them. I am hopeful we will see more progress on this front soon.
That’s it! Just remember - don’t blame me if none of this turns out to be the case. What are your predictions?