No AGI With LLMs In Their Current Form.
Generative models are just one type of models in ML. Neither self-driving cars, nor inference about financial data primarily rely on generative models. Perhaps you can have LLM program that logic on the go, but that would require the LLM to effectively dominate those tasks (i.e. effectively become good at driving a car and being able to build sound financial models via a programming language), and given the probabilistic nature of the inference I donāt think we can have LLMs and agents fully taking over these critical areas. Regarding the probabilistic nature, Iām specifically referring the unsecured/unparameterized probability - itās hard to modulate the potential error.
Right now, agent setups are very good at boilerplate, repeatable code - and most of the code is like that. Most of software development is already abstracted by various frameworks, and a lot of the softwares do very similar tasks and are implemented in similar ways, following specific patterns. Anecdotally, think of a generic SaaS - there are several competing products that do basically the same thing, because they solve a common problem. Agents/LLMs are already great at predicting code of which they have a lot of examples in their training set. This alone, is a massive productivity boost, especially if you are prototyping/iterating.
To give a more concrete answer, the current tech would probably top out at multi-agent, multi-model and some form of DSL(s). So you would have multiple agents, some of them using distinct models. For example, one model for planning, another model for writing code, another model for debugging, etc. The DSLs would be used for both: customizing agents and models (input) and the model would output the code in a DSL (output). So when you ask the model to generate code, it would output it in a higher-level programming language/DSL, and then that one would either be compiled down/translated into a different target, wether that is machine code or a programming language like Python. The DSL design would be optimized for this āgenerative developmentā - in practice it will make things like math-induced security issues more difficult for LLMs to make (e.g. the DSL forces the LLM to use pre-defined cryptographic primitives, and at compilation time type metadata is associated to enforce its correct usage across schemes). So at this techās peak, the LLMs probably wonāt be outputting their code in directly Python. I donāt mean that this is the peak for generative models or ML field as a whole, but for the current state of the tech and the approaches it uses.
Before the tech tops out, however, I think there will be a security issue at scale, followed by an increased focus on security of agents and the the inferred code. Since many products will be using the same models to generate code, the same flawed security patterns will arise in many places, and it will be āeasyā to exploit them at scale. Even with detailed instructions, SOTA models still generate vulnerabilities. Even though most of the code is fine, it takes one small issue in a sensitive place to compromise the system - and if you write any non-trivial code with LLMs, you will run into them. Iāve encountered a lot of this issues myself, and in the code that Iāve reviewed from other developers who wrote their code with LLMs. Proper context helps a lot, but it doesnāt resolve the probabilistic nature of the output.
Regarding getting closer to human-like functionality, it will almost certainly require other ML schemes/approaches in addition to what we have now and further refining and combination of existing approaches. I think we will soon go into meta-models, where a model internally uses other models/sub models to produce the output (in its simplest form, think of a random forest that āchosesā the most appropriate sub-model to run or the output which one of the sub-modelās outputs to use for the next step) - this can help mitigate some of the risks arising from the probabilistic nature of the inference. That would require coordinated, specialized agents, powered by meta-models.
Where I think we have a lot to still to gain with the current tech, even if its progress freezes, is education. LLMs provide a novel way to interact with digital information, you can do it in the same way that you would interact with another human - via spoken language. Being able to formulate your very specific questions, in your very specific way and usually getting a correct answer to it still amazes me to this day. If you combine generative models with VR, you get a whole new dimension of experiencing information, allowing to learn more effectively.