---
title: 'The Limits of AI: Generative AI, NLP, AGI, & What’s Next?'
source: 'https://youtube.com/watch?v=rBlCOLfMYfw'
video_id: 'rBlCOLfMYfw'
date: 2026-06-14
duration_sec: 1191
---

# The Limits of AI: Generative AI, NLP, AGI, & What’s Next?

> Source: [The Limits of AI: Generative AI, NLP, AGI, & What’s Next?](https://youtube.com/watch?v=rBlCOLfMYfw)

## Summary

This video explores the limits of AI by first defining the data-information-knowledge-wisdom pyramid, then examining past AI limitations that have been overcome (reasoning, NLP, creativity, real-time perception), current challenges (hallucinations, AGI, sustainability, self-awareness, judgment, common sense, goal setting, sensation, deep emotions), and finally the complementary roles of humans (defining what and why) and AI (executing how).

### Key Points

- **Data-Information-Knowledge-Wisdom Pyramid** [01:15] — Data is raw facts; information adds context; knowledge adds interpretation; wisdom is applied knowledge. AI currently handles knowledge, but wisdom remains a human domain.
- **Past Limits Overcome: Reasoning** [03:52] — Complex problem solving and reasoning were once considered impossible for AI, but IBM's Deep Blue beat chess grandmaster Garry Kasparov in 1997.
- **Past Limits Overcome: Natural Language Processing** [04:49] — Human language nuance, idioms, and humor were deemed too difficult. Yet Eliza (1965) and IBM Watson (2011 Jeopardy win) showed progress; modern chatbots understand context and intent.
- **Past Limits Overcome: Creativity** [07:10] — AI can now generate art and music, drawing on influences similarly to human creators. This challenges the notion that AI cannot be creative.
- **Past Limits Overcome: Real-Time Perception** [08:04] — Self-driving cars and robots perceive and react to their environment in real time, a capability once confined to science fiction.
- **Current Limit: Hallucinations** [09:57] — Generative AI confidently asserts false information. Techniques like retrieval-augmented generation (RAG) and mixture of experts reduce hallucinations but the problem is not fully solved.
- **Current Limit: Artificial General Intelligence (AGI)** [11:16] — Today's AI excels in narrow domains but lacks general intelligence across all areas. AGI, matching human-level performance across domains, remains unachieved.
- **Current Limit: Sustainability** [12:08] — Large AI models consume enormous energy and require extensive cooling. Scaling with more processors is unsustainable; using appropriately sized models is a key challenge.
- **Current Limit: Self-Awareness and Understanding** [13:10] — Whether AI systems are truly self-aware or understand meaning is a philosophical question. They can simulate thought but may lack genuine comprehension.
- **Current Limit: Judgment and Wisdom** [14:12] — AI struggles with ethical judgments, subjective quality assessments (e.g., music taste), and common sense. These require wisdom, which is the top of the DIKW pyramid.
- **Current Limit: Goal Setting and Sensation** [15:53] — AI can handle micro-goals within a larger task but not define the overarching purpose. Sensation (taste, feel) is partially implemented but not fully integrated.
- **Current Limit: Deep Emotions** [17:05] — AI can simulate emotions but likely does not experience joy, sadness, or loss. Genuine emotional experience remains a human trait.
- **Human vs. AI Roles** [17:47] — Humans excel at defining 'what' to do and 'why' (purpose, meaning). AI excels at figuring out 'how' to execute tasks efficiently. Collaboration leverages both strengths.

### Conclusion

AI has surpassed many historical limitations, but challenges like AGI, sustainability, and true understanding remain. Humans should focus on purpose and direction while AI handles execution; betting against AI's continued progress is unwise.

## Transcript

Artificial intelligence is everywhere right 
now. In your phone, in your car, even writing  
emails for you. You may be wondering if there 
are actually any limits to what AI can do. I've  
heard many people over the last few decades 
confidently assert AI can do certain things,  
but it's never going to be able to do, and 
then you fill in the blank. Guess what most  
of those predictions have in common? They were 
wrong. The past few years have shown exponential  
growth in AI capabilities, bringing it from the 
research lab to everyday life. And it's doing  
most of those things that so many thought 
it never would or even could do. Of course,  
many limitations still exist, but my advice would 
be this. Don't bet against AI, unless of course  
you want to be wrong. In this video, we're going 
to start with a look at what knowledge really is,  
how it differs from data and information, and 
this will help set the context. Then we'll take  
a look at what have been considered to be the 
limits of AI and see which ones of those things  
have actually been accomplished and what's still 
left to do. Then we'll conclude with some ideas  
about the role of AI and humans and where each one 
excels with the hope of learning how to use this  
amazing technology to our best advantage. Let's 
start off with looking at the relationship that  
exists among data, information, knowledge, and 
wisdom. and we'll use this pyramid to spell it  
out. So, we'll start with data. Okay, this is just 
basically raw facts. If I give you data that looks  
like this, I say 10 six uh 42 and 8. Okay, that's 
raw facts. So, what you don't know really what to  
do with that, but that's data for you. Okay, now 
if I add some context to this data, now we have  
information. So this is where we sort of processed 
it a little more and now I'm going to tell you  
that this data actually represents the ages of 
people in a room. So now we have more context.  
This has more meaning to us. Now if I take that 
and say okay but let's apply some interpretation  
to the information that we just had. Now we end up 
with knowledge. Now knowledge tells us yet more.  
So for instance in this case we might say okay 
I've observed that most of the people in this  
room are under the age of 21. So now we've done 
yet more processing with this. And now finally the  
last piece of this is applied knowledge. Applied 
knowledge now gives us wisdom. and wisdom might  
look at this all of this information, all of this 
data, all of this knowledge and say, you know  
what, we've got these people in a room. Let's 
do something like uh do age appropriate games  
to keep them occupied. So, uh the 42-year-old 
probably won't mind too much playing a game that  
a 10-year-old and a and an 8-year-old would play, 
but you know, they they can go along with that  
for a little while. So this is an example very 
trivial example but you can see what I've done  
here data information knowledge and wisdom each 
one of these adds more context more interpretation  
and all of these then lead to the ultimate of 
wisdom. So another way to look at this pyramid  
is data. Well, that's a database. For instance, 
you know, we can store a lot of stuff in there,  
but that's all it is, just a collection of raw 
facts. Information, okay, we have an application  
running on a computer. That's now information 
technology. That's why we call it that. We've  
added context to all of that data. Knowledge, this 
is where AI really starts to come in. Now, we're  
adding more interpretation to the information 
that we've just processed. But here is where we're  
still trying to get. And that's wisdom. Back when 
I was an undergrad riding my dinosaur to class and  
studying AI in its earliest days, there were a lot 
of things that people said, "These are the limits  
of AI. Maybe one day we'll have a system that's 
able to do these, but they won't be anywhere,  
maybe even in our lifetimes." For instance, one of 
the things that was talked about was the ability  
to reason. We needed a system. If we really 
consider it intelligence, then then reasoning  
is a part of that. So the ability to figure out 
and do problem solving, complex problem solving,  
uh this was beyond our capability uh certainly in 
those days. But since then we've come out with a  
computer that can play chess. IBM in 1997 came 
out with a computer called Deep Blue that played  
Gary Kasparov, the best chess player in the world, 
a grandmaster. That's a lot of reasoning. That's  
a lot of problem solving. People thought you'd 
never have a computer that would be able to beat  
a grandmaster. Again, that's already happened. So, 
what seemed to be a limitation wasn't. Another one  
that was really difficult for a long time was 
natural language processing. Uh, human language  
has a lot of nuance, a lot of idioms, things 
where we say things that we don't mean literally.  
And sometimes you're supposed to interpret it 
literally, sometimes it is figurative speech.  
Uh for instance, as I've given examples before, if 
we say it's raining cats and dogs, we know that it  
doesn't mean that there are small animals falling 
out of the sky. That's an idiom. So we have if a  
system is going to really be intelligent in 
the way that we are. It needs to be able to  
understand those things. It needs to be able 
to understand things like humor and understand  
when you're cracking a joke and when you're not. 
Well, sometimes people can't tell that either,  
and sometimes it's because it's a bad dad joke. 
But be that as it may, in general, we're able to  
tell the difference between what is humor and what 
is not. And we've actually made some advancements  
here. In 1965, there came about a first the first 
of what really is the modern chat bots, but this  
was not using modern technology called Eliza. And 
it was able to have conversations with you. Now,  
it wasn't very great conversations, but it would 
ask you questions and and answer questions.  
how are you feeling today? How does that make you 
feel? Uh this kind of thing almost like you feel  
like you're talking to one of these very passive 
psychologists. Uh but IBM advanced this a lot in  
2011 when we came out with Watson which played 
Jeopardy the uh TV game show and was able to win  
and beat champions at that because Jeopardy is 
full of natural language and play on words, puns  
and things like that. You can't program all of 
those into the system and have it know those. It  
really has to understand the meanings behind 
those things in order to do it. And in fact,  
as I say, we've already accomplished that. And 
look at today's modern chat bots. They're able  
to understand a lot of this nuance and they're 
able to take the instructions you give it in  
natural language and understand what you mean in 
a surprising way. In fact, I think that's maybe  
one of the most remarkable aspects of generative 
AI technology is that it's able to do that for  
the first time. We feel like a computer really 
understands us. It's able to infer what we're  
asking for. In some cases, even anticipate the 
next thing that we need, just like a person  
would. We consider that to be intelligent. 
How about creativity? The ability to create.  
I remember hearing a lot of people say, you know, 
computers can't really create information. Well,  
they actually do. Uh, we've got where with 
generative AI, we can create art. We can create  
new works of music. And you can say, well, but 
those are really just mashups of existing. Well,  
guess what? When people compose a new song or draw 
a new picture, we're influenced by the things that  
we've heard as well. Listen to all the top musical 
artists that you know, and they'll tell you, "Oh,  
yeah. Here are my musical influences." So, those 
things all went into the back of their heads and  
influenced the way that they create. So, we are 
creating new things and they are variations on  
the old. But that doesn't mean just because a 
computer did it, it wasn't creative because in  
fact it is. They're coming up with new ideas and 
will continue to do that. We base our learning and  
our creativity on certain things that have 
been done in the past and so does AI. Now,  
here's another one. Real time perception. 
Things like robots. Well, that was the stuff  
of science fiction at one point, but we have them 
today. And you might not think of it as a robot,  
but a self-driving car is one of those where it's 
having to in real time perceive its environment,  
see what's going on, anticipate where the next 
car is going to move, and where it's going to  
be at a specific point in time and do all of 
those calculations in real time, and make real  
uh decisions about that. Robots are having to do 
the same thing in order to navigate around a room.  
So all of these things that basically we used to 
consider to be limits of AI, I'm going to say,  
you know what, we've done all of those. Now, let's 
take a look at some other areas where we've made  
progress, but I don't know if we would say, you 
know, it's sort of mission accomplished yet. And  
one of those would be uh the area of you've 
heard of an IQ, how about an EQ, an emotional  
intelligence uh and an index for that? Well, these 
systems are able to simulate that. And honestly,  
I feel like some people are just able to 
simulate emotional intelligence as well,  
but that's a whole other subject. But an EQ in 
a system, you can see in the modern chat bots  
the ability for them to understand your moods 
and the way that you're expressing yourself.  
So there is some level of awareness in terms of 
the way that you're describing things. I mean,  
we have the stories about people who felt an 
emotional relationship to a chatbot. Well,  
some people feel emotional relationship to their 
shoe, but that's a whole other thing. The fact  
that these systems can talk to us and understand 
at least give the appearance of understanding  
moods and things like that is certainly in the 
area of okay, I it looks like we're doing this  
at least in some cases. Now, another area that's a 
limitation though that we still have is this area  
of hallucinations. Hallucinations are a difficult 
problem. and they're a a byproduct of generative  
AI where the system basically confidently asserts 
something that just isn't true. So it's trying to  
predict what the right answer would be and many 
many times it's right. It's shockingly right.  
But when it's wrong, it is shockingly wrong in 
these cases. Now we've got technologies that  
are making hallucinations less and less likely. 
Uh things like retrieval, augmented generation  
uh helps with this where we feed additional 
information to give more context so that the  
model doesn't just use its own imagination to come 
up with answers. Uh things like mixture of experts  
helps as well where we have different models 
used for different areas. Chaining of models.  
Uh so there are things that we can do in order 
to reduce the hallucination problem and we're  
doing that. So this is one of those I wouldn't say 
uh is a solved problem but we can certainly see  
that we're moving into it. So this one's somewhat 
solved. Okay. So, those are the things that we've  
kind of already done or are still working on 
and maybe be able to see an end in sight. Let's  
move those out of the way. And now, let's take a 
look at the future. In other words, what are the  
current limits? What are the problems that we're 
still having to to work on these days? Well, one  
of the limits of AI is a thing called artificial 
general intelligence. Right now, we see AIs that  
are super smart in a specific area, in a specific 
knowledge area. Now again with some of these chat  
bots that we have today, they seem to know a lot 
about pretty much everything, but they also have  
limitations. For instance, they don't do real-time 
perception. Uh they can't tie their own shoes,  
for instance. So artificial general intelligence 
would be something that was as smart as a person  
doing all the things that we consider to be 
intelligent and at least on par with what a  
person would do across all the different domains. 
That's something that we haven't really fully  
achieved in a single system yet. The next level 
beyond that would be artificial super intelligence  
where we have something that is better than 
humans in every domain and that's the right  
now again the stuff of science fiction. Not saying 
that we won't do it but we haven't really done it  
yet. Another problem that's still to be solved is 
with sustainability. So right now we have systems  
that can do amazing stuff but boy do they suck 
up the gas. They take up all the electricity.  
They need lots of cooling. They're very expensive 
to run. This is not something that's going to be  
able to scale if we just keep throwing more and 
more processors at this situation. Uh that's not  
going to work. We're going to end up using all the 
electricity that's on the planet just in order to  
uh to to run some of these queries. So, we're 
going to have to be able to make better, smarter  
decisions with sustainability. Use models that are 
the right size, not just the biggest model, but  
the right size model. In some cases, a small model 
might be more efficient and do a better job and  
might even hallucinate less if we've got the right 
use case. So, this is still work that we're that  
we're doing that is not yet, I would say, a solved 
problem, but there's a lot of things we can do  
about it. Another one that is really the area that 
is is science fiction today is self-awareness. So,  
is a system self-aware? Does it know it exists? 
Does it have consciousness? Well, I don't really  
know the answer to that. This is really not a 
computer science question. This is a philosophy  
question. So, I'm not going to try to deal with 
that one here because I'm not even sure how the  
answer would be. But another thing that gets us 
back into this area though is understanding. So,  
a system can spit out a lot of things, but 
it actually understand what it's saying. Um,  
does it really know what the meaning of the 
things are? Seems like it's done a lot of that,  
but there's always the question of is this really 
just simulating? Is it simulating thought? Well,  
I don't know. I'll tell you there's a lot 
of people I've talked to and I think they  
may be only simulating thought and simulating 
intelligence. So, again, it's a little hard to to  
draw the line clearly, but uh this seems to be an 
limitation where the AI maybe doesn't understand  
the biggest broadest context that we'd like it 
to understand. Uh judgment. So remember when I  
was talking about data, information, knowledge 
and wisdom. Well, this is that last one. This is  
the business of wisdom and judgment. And in this 
case, is the system able to make good judgments?  
Maybe ethical judgments. Can it determine what 
is right and what's wrong? Again, can people do  
that? Some people have a real hard problem with 
those kind of judgments. So, it's hard for us  
to program a system that will if we can't figure 
out what those rules would be. But we we certainly  
know that right now these are limitations that 
the systems have. How about in terms of judging  
something that's just very subjective like the 
quality of something, maybe music? You know,  
what I think is really great music, you may 
not think. So, you know, you say, "Well, Jeff,  
you have no judgment at all." Uh, but I have a 
different view of that. But these systems are they  
able they're able to generate music and they're 
able to throw away stuff that is just absolute  
gibberish but can they tell what is going to be a 
hit and what's not going to be for instance in the  
music area. So there's a lot of of work here in 
this space so that it's able to do some of those  
qualitative judgments as well. How about this one 
common sense? And I'm going to really put that one  
in uh in quotes because air quotes because um I 
mean is it really all that common? It seems like  
again we have limitations with people. So we can't 
really expect a system to be able to perfectly do  
what we consider to be common sense because we all 
might have a different idea about that. Certainly  
there are some things that we know and the systems 
ought to be able to understand that but today  
there are some certainly some limitations to 
that. How about in terms of goal setting? Well,  
some people would say that with today's agentic 
AI that a system can in fact set its own goals and  
go off and accomplish those things. And what I'm 
going to make a distinction here is that we have  
micro goals. These are sort of the small things 
that we need to do if I give you a a larger task  
and the macro goals. So the larger task, this 
is what needs to be done. This is how I go about  
doing it. And right now today's agents are able 
to do these kind of micro goals, the goals within  
the larger objective, but the big goal, why would 
we do this in the first place? That's maybe still  
uh without uh beyond its reach at the moment. And 
then sensation, how about this? Does this does a  
AI system really sense things? Does it understand 
what's happening? What how things feel? How things  
taste? Um that sort of stuff. The things that 
are of the senses. Well, we're building robots  
that are able to certainly see and hear. Can 
they taste? In some cases, maybe to an extent,  
but there's a lot of other things that go into uh 
these kinds of sensations that we haven't put all  
together in one system. And then here's the really 
big one, I think, and that's deep emotions. Is a  
system really able to feel the same way that we 
do? Is it able to experience joy? Is it able to  
experience sadness, loss, uh, accomplishment? Does 
it really get what all that's about? And again,  
I know some people who don't really do all 
that particularly well. So, so this is one  
of the things that is difficult to put into a 
system and we can simulate it today, but is it  
really feeling these kinds of things? So, I would 
suggest to you these are some of the things that  
to one degree or another are limitations with 
today's AI. Now, what is the role for humans  
and for AI? How do we work together? How do we 
make sure this is a tool that works for us? Well,  
people really should be over here doing this kind 
of stuff. Answering the what question. What is it  
we want to do? That's the overall macrolevel goal, 
the objective and answering the question why.  
What's the purpose of this? Is there meaning in 
what we're doing? What's the ultimate thing that  
we're trying to accomplish? And without purpose, 
all of this is just meaningless work. So people  
are still far better at that kind of thing. And we 
should be the ones controlling this tool that way.  
Over here on this side, once we've told the system 
what needs to be done, AI can many cases with an  
agent figure out the how and go off and perform 
it, actually do it. Agents are able to automate a  
lot of things much faster than a person could. and 
they can do it in an optimized way, but they need  
to know what to do in the first place. We need 
to know why. So, if you look at a history of AI,  
it felt like for the longest time we were making 
very little progress and then all of a sudden it  
just took off. And we're at this this inflection 
point where the developments and where all of this  
is going to go, no one really knows. But what I 
can say this for sure, we can look at a history  
of milestones that we've accomplished already. 
And we can look at lots of future research, things  
that still need to be done, which is actually 
very exciting. If you're someone that enjoys  
it and the possibilities of problem solving, then 
we're going to be able to do a lot more work and  
ultimately we're going to get end up with systems 
that do things we didn't even imagine yet.  
So my advice to you if you start looking at 
the limitations of AI today I would say don't  
become preoccupied with those because the people 
who have and have asserted that AI will never do  
this that or the other thing have generally been 
wrong. My advice to you, don't bet against AI.