I know this is a contentious subject and tbh I'm not a big fan of Gary myself, but I'd like to make a simple explanation as to why AGI requires symbolic manipulation. Note that I'm careful here and not saying it needs to completely neurosymbolic nor am I saying the advancements we've made won't contribute to AGI (they will). Unlike Gary I am not willing to ignore the advancements that LLMs have made and the utility that they provide. Their limitations do not invalidate the improvements. But this is the identical error those make that dismiss symbolics as an avenue forward.

At the end of the day, the current framework of ML operates by ingesting data, modeling that data, then iterating on that model to better fit data[0]. This is something humans and every living creature does. But many animals, humans included, do so much more. The problem comes when we get to understanding abstraction, and an issue is that we operate at such extreme levels of abstraction that it is easy to miss. After all, we were designed to be better at recognizing differences because it allows energy savings for business as usual settings. The problem is that pattern recognition leads to no such viable path for our levels of abstraction. It may not be obvious, but we are symbolic manipulators. Our language is composed of symbolic manipulation, our code, and our math. The last of which may be the most clear example of the distinction. But this might be a bit uncommon to see unless you're from a strong science background.

We all know that there are great differences in numeric/empirical solutions from analytic solutions. The latter of which is considered both harder and much more rigorous. The latter is naturally causal and interpretable. The reason we do numerics and empirics is because limitations. But analytics is why a physicist can sit in a room with a pen and paper and (eventually) discover fundamental laws of nature. Many of these achievements are not solvable by observation alone[1]. But these equations are symbolic. The symbols are the abstraction. A major advantage of the symbolism here is that once we are able to formulate solutions and the rules of the symbolic system, we can manipulate as we please. This has so much more flexibility than a numeric solution. This is the underlying reason the theorist exists! It allows for us to quickly and accurately ask new questions and find errors or limitations. This kind of manipulation allows us to ask why gravity is an inverse square law and to understand why it is exactly 2[2] and not close to 2. It allows us to set concepts aside that we might call a constant (when the resultant is unitless), solve currently tractable factors, and even then later determine what this constant is (often the job of an experimentalist). We may even later ask ourselves how a constant may be decomposed into other factors. The symbolic nature allows us to pattern match in ways we wouldn't be able to with numerics. There is just a high level of abstraction that we are unable to do with the numerics. Abstraction that we rely on to create and understand the world as it is.

So the great question in AI/ML is not if these systems need to do symbolic manipulation. It is if a machine can learn to do symbolic manipulation through numerics. This is still unknown and there are arguments on both sides (right now the case is stronger against this happening through data processing). The only naive thing would be to not pursue both paths (well there are many paths). We're venturing into the unknown. We've gotten a long way through the methods we've been using and this is reason to continue down that path. But at the same time, this is not reason to pursue others. We've never seen that happen in the past. All technologies undergo radical shifts that are no apparent to those on the outside. Imagine if we didn't pursue LiPo because lead-acid was working. If we didn't pursue transistors because vacuum tubes were working. LEDs because incandescent. And all the new technologies began as worse (often much worse) than those they later replace[3].

Lastly, I want to speak to investors directly. If your goal is to invest in a new company that will make AGI, you are likely to lose if that company is pursuing via LLMs[4]. There are already major players in this space that are far ahead and have more momentum and funding. There are things that they are missing that others might see, but they have the capacity to find those limits and fix them[5]. Instead, you have a better chance on what appears riskier: less developed avenues that also have explainable avenues towards the goal. It is high risk, but it always was. Here's the thing, I said the question of numerics leading to symbolics is still open, but another way of looking at this is that we know symbolics is necessary (or at least we know symbolics is sufficient for intelligence).

[0] Note the dependence on the previous estimate/model.

[1] I want to note that there is a feedback mechanism which is what I reference in my first paragraph. The theoretical physicist stands on the back of experimental physicists just as the experimental physicist stands on the back of the theorist. An untest{ed,able} hypothesis is no theory and a fitting data is not a physical model without theory. See Fermi/Dyson's conversation about fitting an elephant.

[2] This is a calculation you will do in an upper division classical mechanics course (physics).

[3] Often also with people questioning why we should pursue these other paths, not recognizing -- or unwilling to -- the limitations of the current technology. And no technology is without limits. That alone should be reason to pursue other avenues.

[4] If you're investing in products, then pursue LLMs. They are much more mature and you have the infrastructure of research behind you. You can also likely adapt to a changing underlying technology.

[5] Unless your real goal is acquisition by said players