Decoding AI Assistants, Agents, Copilots
Key Points
- AI’s hyper‑persuasive nature fuels hype about productivity, but it’s unclear whether generative tools actually make workers more efficient.
- Ethan Mollick clarifies the taxonomy: assistants are chat‑based bots, copilots are AI‑enhanced features embedded in software, agents are autonomous systems that set and pursue their own goals, and large‑action models can execute real‑world actions like scheduling appointments.
- The proliferation of buzzwords (copilot, agent, LAM, etc.) creates confusion, yet the functional differences matter more than the branding.
- Companies often misuse or overcomplicate naming conventions for AI products, which can distract from evaluating true capabilities.
- Organizations should direct attention and resources toward tools that demonstrably improve workflow rather than chasing the latest AI label.
Sections
- Untangling AI Productivity Terminology - In a conversation with Wharton professor Ethan Mollick, the segment clarifies the distinctions between AI assistants, agents, copilots, and large action models while questioning whether generative AI truly boosts workplace efficiency.
- GPT‑4 Boosts BCG Workforce Performance - A study involving 8% of BCG’s global staff revealed that access to GPT‑4 raised task quality by 40%, accelerated work by 26%, and increased output by 12.5%, with the greatest gains seen among lower‑performing consultants.
- AI Automation Undermines Human Meaning - The speaker praises AI’s power but warns that organizations are neglecting its deeper implications, exemplified by the robotic replacement of meaningful human tasks such as performance reviews and recommendation letters.
- Distinguishing LAMs from Autonomous AI Agents - The speaker contrasts LAMs—large language models that act on explicit user commands—from fully autonomous agents, using examples like market‑research drafting, and asks what practical generative‑AI experiments companies should begin now.
- AI's Role: Benefits and Limits - The speaker frames AI as a transformative general‑purpose technology, weighs its varied impacts, and uses a rapid‑fire Q&A to illustrate which tasks—like health second opinions versus vacation planning—are currently appropriate or premature for AI assistance.
- AI As Your Everyday Co‑Worker - The speaker urges listeners to treat AI as a ubiquitous work partner, using it for every task—from brainstorming questions to summarizing content and drafting emails—to learn its strengths and weaknesses and boost productivity.
Full Transcript
# Decoding AI Assistants, Agents, Copilots **Source:** [https://www.youtube.com/watch?v=VCA7wdZBuUE](https://www.youtube.com/watch?v=VCA7wdZBuUE) **Duration:** 00:18:01 ## Summary - AI’s hyper‑persuasive nature fuels hype about productivity, but it’s unclear whether generative tools actually make workers more efficient. - Ethan Mollick clarifies the taxonomy: assistants are chat‑based bots, copilots are AI‑enhanced features embedded in software, agents are autonomous systems that set and pursue their own goals, and large‑action models can execute real‑world actions like scheduling appointments. - The proliferation of buzzwords (copilot, agent, LAM, etc.) creates confusion, yet the functional differences matter more than the branding. - Companies often misuse or overcomplicate naming conventions for AI products, which can distract from evaluating true capabilities. - Organizations should direct attention and resources toward tools that demonstrably improve workflow rather than chasing the latest AI label. ## Sections - [00:00:00](https://www.youtube.com/watch?v=VCA7wdZBuUE&t=0s) **Untangling AI Productivity Terminology** - In a conversation with Wharton professor Ethan Mollick, the segment clarifies the distinctions between AI assistants, agents, copilots, and large action models while questioning whether generative AI truly boosts workplace efficiency. - [00:03:07](https://www.youtube.com/watch?v=VCA7wdZBuUE&t=187s) **GPT‑4 Boosts BCG Workforce Performance** - A study involving 8% of BCG’s global staff revealed that access to GPT‑4 raised task quality by 40%, accelerated work by 26%, and increased output by 12.5%, with the greatest gains seen among lower‑performing consultants. - [00:06:11](https://www.youtube.com/watch?v=VCA7wdZBuUE&t=371s) **AI Automation Undermines Human Meaning** - The speaker praises AI’s power but warns that organizations are neglecting its deeper implications, exemplified by the robotic replacement of meaningful human tasks such as performance reviews and recommendation letters. - [00:09:18](https://www.youtube.com/watch?v=VCA7wdZBuUE&t=558s) **Distinguishing LAMs from Autonomous AI Agents** - The speaker contrasts LAMs—large language models that act on explicit user commands—from fully autonomous agents, using examples like market‑research drafting, and asks what practical generative‑AI experiments companies should begin now. - [00:12:24](https://www.youtube.com/watch?v=VCA7wdZBuUE&t=744s) **AI's Role: Benefits and Limits** - The speaker frames AI as a transformative general‑purpose technology, weighs its varied impacts, and uses a rapid‑fire Q&A to illustrate which tasks—like health second opinions versus vacation planning—are currently appropriate or premature for AI assistance. - [00:15:27](https://www.youtube.com/watch?v=VCA7wdZBuUE&t=927s) **AI As Your Everyday Co‑Worker** - The speaker urges listeners to treat AI as a ubiquitous work partner, using it for every task—from brainstorming questions to summarizing content and drafting emails—to learn its strengths and weaknesses and boost productivity. ## Full Transcript
AI is hyper persuasive.
What does that mean? It's innovative. What does that mean?
That, you know, in an ideal world, that helps us as humans thrive.
What I worry about is organizations that aren't thinking about these issues.
You don't have to look far to find news about business leaders and employees
exploring how generative AI can help us work better, work
faster, work more efficiently.
But real talk.
Are we actually getting more productive?
Or is this just the story we're telling ourselves?
And what's up with all of the names?
Copilots, assistants, agents, LAMs?
It would be great to finally get an answer, simply speaking, to
what's the difference between AI assistants and AI agents?
And where should organizations and employees be directing their attention
and resources to really accomplish efficiency?
Well, today I'm joined by Ethan Mollick.
Ethan is an associate professor at the famed Wharton School of Business,
where he studies and teaches entrepreneurship, innovation and AI.
And he's the co-director of the Generative AI Lab at Wharton.
He's also a popular author and blogger, and his latest book
is Co-Intelligence: Living and Working with AI.
Ethan, thank you so much for being here.
It's a pleasure. Thanks for having me.
When it comes to AI based productivity tools,
kind of feels like there are too many terms out there, right?
So let's start with how do AI agents differ from, say,
AI assistants, copilots and large action models?
Ethan, I need you to help us to get all of these names straight.
Well, it doesn't help that even the term AI is, like, in dispute, right?
It meant something very different prior to 2023 than it does today.
So you're not alone, right?
The easiest way to think about this now is probably to think about
what you've been using when you use ChatGPT
is you're using a chat bot or an assistant it’s sometimes called.
Then when you have a AI tool integrated into your software,
like an AI help bot or, it's a tool that helps you, you know,
finish your graphic design or something that's usually called a copilot.
And then agents are autonomous AI systems that will go off and do work on their own
and set their own goals and just perform autonomous work.
They're not there yet. That's what everyone's aiming for next.
And large action models are a new, made up term for an AI
that can actually do things like if you say, set up an appointment,
it can actually act on your phone and set up the appointment for you.
Okay, so there are some real distinctions
that you just drew in between each of those.
So does the name really matter after all?
If you're the one who's building it?
The one thing that I think we've learned about AI over
the last couple of years is AI companies are terrible at naming things.
I mean, who would have ever called ChatGPT 4.0 and Claude 3.5 sonnet and you know,
like Llama 3.1 405 B, like, these are things I have to say
with a straight face now.
So like, I don't think we should worry too much
about the names because they're a complete mess and it is confusing.
I think that creates barriers that aren't necessarily there.
Like these systems are not that hard to use
or to work with, and it just sounds harder than it is when we use all these names.
You've been doing a lot of research
on the practical implications of generative AI, often hands on.
In a recent talk, you cited an experiment using gen AI
that resulted in a 40% increase in quality of work.
Can you tell us a bit about that experiment?
And then I also want to know
what type of gen AI was used for this and what does quality mean?
It's a really good question.
So the particular
study you're talking about is one I do with my colleagues at MIT, Harvard
and the University of Warwick, where we went to Boston Consulting Group,
and they gave us 8% of their global workforce.
And we did an experiment.
We developed 18 business tasks ranging from like consulting, standard consulting
and now analytics and marketing and persuasion ideation.
Some of the consultants got access to GPT 4, some did not.
The people who got access to GPT 4 had a 40% improvement
in quality, 26% faster, and got 12.5% more work done.
So when you talk about the amount of work that's being done,
can you still break down how does that quality figure into it?
Sure.
So we found this 40% quality increase
that matches a bunch of other work that shows that when an AI does
work, it produces pretty high quality work out of the bat.
I would say solid A-minus from a lot of the most advanced models out there.
Not always a plus, but but solid work.
And so we found that it increased the average quality,
especially for low performers.
So low performers got the biggest boost in their quality.
High performers got less of a boost.
So this is an experiment.
So now how can real life companies achieve something similar?
What key actions did they need to take in order to get here?
So that's where it gets really interesting.
We are still seeing these kind of large scale improvements,
but they're at the individual level.
So individual coders who use these systems are much more productive.
And by the way, everyone's already using them like penetration rates,
from what we can tell from early studies, like there's
a study in Denmark of all places from January of last year that shows that,
already 65% of marketers, 64% of coders,
you know, 35% of lawyers were already using AI at work.
The productivity benefits go to the individuals, though,
not to the organizations.
To have the benefits flow to organizations, organizations
need to think hard about how they're incentivizing people
and how they're building organizational structures to succeed.
Ethan, how can businesses empower
these productivity gains then, rather than having people hide them?
Is reward systems the answer? So it's a few things.
Thinking about rewards is a big deal.
Why are people incentivized to show you what they're doing?
I've seen organizations give out
$10,000 prizes once a week to whoever has automated their job the best,
but even just doing things like having the company
executives show you they're using AI can help make this different.
And then there's the organizational structure piece.
What do you do when you're more productive?
What am I supposed to do with my time?
That feels like a big question a lot of companies aren't asking.
So are there any other recent studies then that you want to name drop?
There's a lot of interesting work.
We've got a repeated set of studies that show that AI is very creative.
So my colleagues at Wharton run a famous innovation entrepreneurship class.
They had the students in class generate 200 ideas.
They had the AI generate 200 ideas.
And they had I'd say judges judge the ideas by their willingness
to pay, how much they'd pay for the the product that was created.
Out of the top 40 ideas by willingness to pay, 35 came from the AI,
only five from the humans.
So we're already seeing higher creativity.
Similarly, if you get into a debate
with an AI, you’re 81.7% more likely to change your view to the AI’s
view then if you get to a debate with an average human.
So these systems are already extremely powerful.
I'm trying to figure out if I should feel encouraged
and empowered by that research, or if I should feel intimidated.
I think a little of both.
I mean, I think this is empowering and powerful,
but I also think we haven't started to really deal
with the implications of all of this.
Like AI is hyper persuasive. What does that mean?
It's innovative. What does that mean?
That, you know, in an ideal world that helps us as humans thrive.
What I worry about is organizations that aren’t thinking about these issues.
Okay.
So as long as the organizations are at least having this knowledge,
that's a strong foundation for them to begin to think about it.
And thinking about actually changing things like, what are they going to do?
Like, I mean, look, the number one thing that people tell me they use
AI for secretly inside their company is writing all their performance reviews.
Now, performance reviews are really annoying to write.
They're meaningful when they're done by human beings.
They're not meaningful when done by the AI,
but it's the first thing people automate, right?
Like, similarly, like, I actually I had a great experience.
I had to write letters of recommendation all the time for people.
And the whole idea of a letter recommendation is I purposely set
my time on fire as a signal fire, that I care about someone, right?
Like I'm like, I’m going to spend 45 minutes writing a letter for you.
I have to read all of your stuff
and your resume, and then I write a letter for you.
And the letter doesn't matter.
It's the 45 minutes I set on fire that’s an indicator that I care, right?
Now, here's the problem.
If I just put the resume in, the job they're applying for, and the letter,
then I get a much better written letter
in 30 seconds than it would take me to do in 45 minutes.
Do I turn in the better letter that didn't take the time to write.
Or do I turn in the worst written letter that took me 45 minutes to write?
If you ask your students, they'll say like, turn in the meaningless letter
because it's better written and they'll get the you know, get the job
more likely.
That starts to challenge how we view things.
In fact, I had a student send me,
for the first time, as when they asked for a letter recommendation,
they sent me a prompt that they just said, paste, this is into GPT four.
It use this to write the letter.
No joke. Did that student end up getting the job?
They did, yes.
Well, that's a good letter. Well done.
So based on where we are right now, do you think AI agents
are the future of productivity?
So my book is about Co intelligence, the idea that people working with machines
do better.
I think that that is still very relevant.
I think if you can figure out a way to work with an AI, you do better, right?
Agents are a different breed, right?
Agents are the idea that I give an AI an assignment.
Write me this code to do this, do the market research, generate the report.
Come back to me. It's almost like we're with the contract worker.
So all the AI companies
think that agents of the future, we don't know yet whether they are or not.
Where do you sit on that?
Based on what I've seen for other agents,
I think they're going to be a very big deal.
I think assigning a tool to go out and do something in the world.
I've already been using AI coding agents. I can't code in Python.
I do a few hundred Python programs a week now because the AI does it for me.
So I actually think agents are going to be a very big deal.
I mean, I'm going to put you on the spot here.
Can you give me a specific year or maybe like a range of time
that you think it will take for the future to be here?
I would guess 2024 if I had to guess.
2025 on the outside.
Okay, great.
Along those lines too, I'm very curious about what will AI agents
end up doing for us or with us that they aren't doing yet?
Like, what are they lacking?
Right now, when you use an AI system, you have to direct it, right?
It's designed to be a co-intelligence to work with you.
I think that that is very different than an agent that just does the work for you.
If I want to write a piece of code or write a document,
I'm going to give the document to the AI.
It will give me feedback. I'll give it back information.
I might have to paste in some research.
I'm directing the AI.
With an agent based system, I would say something
like, do all the market research you need to, go out into the world
and collect the data you need, then write our initial draft,
you know, simulate running, a bunch of different people reading
the draft, make changes and updated, give it to me when it's done.
And, you know, also make sure it's on the website
and well formatted and figure out how that works.
And, you know, read up the latest research to make sure everything's up to date.
But is this something that an LAM
can start to solve rather than an agent to go back to our name conversation?
So LAM is a little bit vaguely defined still.
But the way I understand LAMs are large language models that could take action.
So the most common version is like on my phone,
I can ask the the large language model to do something.
That is different from the agent because it doesn't require full autonomy, right?
An agent is has autonomous goals that it seeks to pursue
as it wants to pursue them.
While an LAM would be more like the example of like,
you know, telling your coffee machine make me the perfect cup of coffee
kind of set up, and it would push all the buttons for you to set that up.
But it feels like gen AI is something that people just need to dive in
and start getting their hands dirty.
What should companies be experimenting with right now to get productive?
The R&D process inside most companies, not for their product,
but for their own process,
has largely been outsourced to enterprise software companies like,
you don't do the work yourself.
You have, IBM has been thinking hard about this problem and brings you a solution.
And I think that the issue is, is that
that's left a lot of companies without a lot of R&D bones, right?
They're not used to think about how to actually own, process or approach.
They build good products or, you know, services or solutions,
but they're not thinking about how do I fundamentally change my organization.
And I think the real key is experimentation.
What is AI good for it?
You're gonna have to figure that out.
And the people that figure it out, or the subject matter
experts inside your own company, the people who actually do
the research today, who are using the job this all the time.
So you need to empower everybody in your organization to be learning
and testing in an ethical, legal way, but not so constrained
that they don't get anything done.
What are Ethan Mollick’s top five ways that Ethan Mollick stays productive?
So I differentiate in
both the book and our study between what we call a cyborg and a centaur model.
So a centaur is like an AI model, like an AI approach
where you like half person, half force, you divide the work up to like,
I like to do analysis, you do the emails, I divide it that way.
Cyborg work is more blended.
You do the work with the AI back and forth, throwing off individual tasks.
So for example, things I've done in the last day
we were developing a logo for a new internal project.
So what we did was we sat down with Claude and said, here's the concepts
we want to get across the logo, draw an image for us on the logo,
try it against a blue background. What if this was more rounded?
Can you think of a few other ways to do it?
Another case was like, hey, I've got this document.
I need it to be
50% shorter, can shorten it down without removing any of the important context.
There was another case of like, you know, I read this academic paper, I'm like,
I think I get it.
Can you just make sure this understanding is correct?
Like so lots of little use cases all the time.
Well then is there a world where you OD on AI?
I mean, I think it's the same danger that we have of OD'ing on our phone.
Like there is this kind of like, what is human?
When is it inappropriate to do? We haven’t divided those lines yet.
AI is a very profound technology, technological change.
We call these kind of changes general purpose technologies.
They affect everything in our world.
So the internet and the computers were one right?
It took a long time to play out, electricity
before that, before that steam.
So they have lots of varied effects.
Some are good and some are bad.
And we're going to watch these things play out.
So there's so many areas where using too much AI is going to be harmful.
There’s going to be areas where AI is going to be transformative in good ways.
And we need to play this all out. I want to do a lightning round.
Are you down with that? I'm always ready. Yep.
All right. So yes or no.
What would you let an AI agent do for you today?
Plan your next vacation? Yes or no?
Not yet. Not yet. Okay, okay.
Be your manager.
Not yet.
Give you a health diagnosis.
As a second opinion? Definitely. But not as a first?
I mean, I've got access to humans.
My standard is always best available human.
What's the human you have access to?
And are they better or worse than the AI?
AI is the cheapest second opinion in the world,
and most of the studies show it's pretty great at medicine.
I still wouldn't, you know, I'm not going to tell you
out of on a podcast that like, trust the AI rather than the doctor.
Although I spoke to one of the most famous therapists alive today,
a guy named Marty Seligman, who invented positive psychology,
and he says that the AI is better than him at therapy.
So, you know, you draw your own conclusions.
What about evaluate your performance?
I absolutely use it for that today. Yeah.
I was going to say if you said no for that one, I was going to call you out.
It's like you use it yourself. Yeah. All the time. Yeah.
Of course.
How is this what would you what would an outside person say about this?
What would a high school student say about this?
What when someone is really critical, say, all the time.
Can you complete this sentence for me: in five years, AI agents will...
Be ubiquitous in that you will see them anywhere you're online.
You're more likely to run into an AI agent than a person.
What do you think is going to be
the most unexpected place that we run into an AI agent?
That's a tough one.
I mean, I like, by the very nature, be unexpected.
I will tell you the unexpected place where I think there'll be the biggest delay.
I think teaching is not going to change as much as people think.
Classrooms and how we teach will change, but we're not going to replace teachers
with AI agents, but we will supplement them with AI tutors and things like that.
I think you're going to see AI agents in more high end jobs than you think.
I think a lot of first round legal work might be done by AI agents for example.
How did you use gen AI recently on the job,
and did it make you more productive?
As academics, you also review very high end academic papers.
I always review the paper myself, but afterwards
I give it to the AI and say, what would you add to this that I missed?
There was a flaw or issue to improve the paper.
It does a great job with that.
That's very high end PhD level work.
I like that. What would you add to... okay.
Thank you. I'm going to use that prompt myself.
Appreciate you.
Also,
does it matter if productivity increases if we're focused on the wrong things?
Because how's gen AI gonna help us with that?
So first of all it is a pretty good advisor and manager.
So it might help us with that.
But I agree, like part of the moment requires us to really think about work
in a deeper way than we're used to thinking about work.
We need to be thinking about like, what is it?
Why are we doing the things that we're doing?
What parts of the process can be improved or place changed?
Really big set of questions.
All of this builds up to an overwhelming idea that AI is going to be your coworker.
What are the things that our listeners should go do right now
to be better at work?
So I have four rules in the book, and I stand by them because like,
even though I wrote them like a year and a half ago,
people have been telling me they work.
So the most important is just use AI for everything.
Like, bring it to everything you do.
Everything legally and ethically that you can,
you know, I don't know if you used it
before this podcast, but I would, if I was in your shoes,
I would be like, give me some questions to ask.
Then I'd be like, all right, let's rollplay back and forth some interactions.
Then I'd take this transcript and say,
what were the most interesting nuggets where I might make cuts?
How would I summarize this down?
How can I turn this into a digestible format for different audiences?
And then I might say afterwards, like, well,
how do I email all the people in my team
to let them know what was good about the podcast or not?
You know, what questions should I redo or do differently next time?
I would use it for absolutely everything.
Absolutely everything.
And that's how you learn what's good or bad,
because it's going to be good at everything.
It's going to suck at some stuff.
So you need to figure that out by using it.
Second thing is just to realize that it's going to do part of your job for you.
That's not the end of the world. Jobs are bundles of tasks.
We lose tasks all the time.
Like, I'm a professor,
by the way, out of the 1016 most affected jobs
by AI, according to most of the studies on this, business
school professor is number 22. So I think about this a lot.
And like my job is going to change, right?
Like will grading be done with AI? Will other stuff...
but like I'm still hopefully to be talking to you here.
So what I do day to day might change, my core job doesn't change.
So you want to think about doubling down on what your best at.
Because whenever your’re best at, you're almost certainly better than AI.
Third thing, don't make prompting hard.
Just talk to the AI like a person.
Tell it what kind of person it is, and you're 90% of the way there.
And then my last rule is this is the worst AI you're ever going to use.
Ethan, you mentioned that one of the things that you do
before you publish a paper is you ask, is there anything that I missed in here?
So now I'm going to ask you, Ethan, is there anything that I missed, anything
that you wanted to cover that we did not mention today?
I would say that I think these were really good questions.
I think that the real issue is this gap between individual and organizational.
And my biggest concern is that organizational leaders
aren't getting it, right?
So there's huge transformation happening under their feet.
Organizations are getting filled with secret cyborgs
who are doing all their work with AI and not telling anyone.
And if they don't realize that, that is both a huge risk and a huge opportunity,
and if they only treat as a risk, they're going to lose.
This has been such a tremendous conversation.
And again, I really appreciate you for joining us.
For those of you who are watching and listening along,
please let us know your thoughts in the comments below.
And absolutely stay tuned to this feed, because we've got new episodes
biweekly on Tuesdays. We'll see you soon.