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Episode 238: The Power and Perils of Using ChatGPT in Your Consulting Business—with Greg Baker

Deb Zahn: I want to welcome you to this week's episode of the Craft of Consulting Podcast. On this episode, we're going to talk about artificial intelligence, specifically ChatGPT, which I know a lot of consultants are already using or thinking about using. So, we're going to dig into what it is, what it can do for you in your consulting business, and some of the potential negative impacts that it might have, as well as common mistakes to avoid. So, I brought on someone who is quite literally an expert in this. He's been working with AI his entire career. Greg Baker is going to come on, break it all down for us and help us understand where it fits and where it might be a little bit scary too. So, let's get started.

I want to welcome to my show today, Greg Baker. Greg, welcome to the show.

Greg Baker: Thanks for inviting me on.

Deb Zahn: You bet. Well, let's start off, tell my listeners what you do.

Greg Baker: So, half of my life is I'm a lecturer in computational linguistics at Macquarie University, and the other half of my life is I'm a consultant.

Deb Zahn: Perfect. Well, we're going to need to know what computational linguistics is because that's a mouthful, and I know it's important to what we're going to be talking about. So, give us the easy definition of that, if there is one.

Greg Baker: Well, you know how there are computer scientists who specialize in modeling biology or modeling stars or planets or people who model rocketry? Well, computational linguistics is software to model language and to interact with language.

Deb Zahn: Oh, wow. That's a great definition. And again, very important to what we're going to talk about. So, we're going to talk a little bit about the impact and use of AI. In particular, we're going to hit upon ChatGPT. It's not the only one out there, but it seems to be the thing everybody's talking about, and what that means for consultants.

So, let's start off. What is ChatGPT? In case someone has been living under a rock and hasn't heard about it.

Greg Baker: Well, I'll give you the incredibly detailed answer, which is it's a piece of software that predicts the next word. And as it turns out that if you tell it to predict the next word and then feed the extra word back to it, it'll predict the word after that and the word after that and the word after that. And much to everyone's surprise, this turns out to exhibit behaviors that are very, very intelligent and looks as though it's doing something that we would consider thinking. But if it is thinking, it's thinking in a way that is not the same as human beings think.

Deb Zahn: That's not a scary statement at all. Now I've used it I've actually gotten in and played around with it because I was curious about it. I know other consultants have used it. But what are some of the ways that you see people using it today, given that's what it is?

Greg Baker: Well, how about I dive into the kinds of things that, as a consultant, you can do. So, I've got a list of five, off the top of my head, key themes that regardless of what area you consult in, I think you'll find helpful. So, number one, so artificial intelligence broadly covers a lot of areas. So, there's the area which is understanding images. And so we've just seen OpenAI have released API so that you can now upload images and photographs, and that's sort of ticking the box of one of the other major AI parts that's been missing. Another part is speech and language. And again, we've seen OpenAI this morning, we are recording this on early November, announce much better text to speech. So, a generation of speech. But this time last year, they basically broke everything in terms of records for speech to text, it became a solved problem. And then there's a few other areas of AI that they haven't yet explored at OpenAI, that they haven't released.

But let's just dive into some of the interesting things that this means. So, let's think in terms of marketing yourself as a consultant. Golden rule of thumb is anything that you generate, repackage it 10 times in as many different formats as you possibly can. So, we've got this podcast here, and Deb, I guess you're going to want to set yourself up as being the guru podcaster on all things AI. So, you'll take this whole audio session and transcribe it. Of course you're going to do that. And then you might run that through one of the large language models, ChatGPT being a good one, and ask it, "Turn this into an exciting blog post, or turn this into 10 exciting blog posts hitting on all the key themes." And then you might also take this and say, "I want to turn this into a chapter in a book that I'm writing." And with the models that just come out, the 128,000 token models, they can pretty much generate an entire book in terms of being able to remember the beginning and the end, whether or not there's enough stuff there to say something worthwhile.

Deb Zahn: That's right.

Greg Baker: But if you are feeding it in a bunch of podcasts that it can process and repackage, you'll get some kind of rich, interesting stuff coming out of that. And that's then before you start talking about, well, I have this tender that's very similar to the tender I'm going to put into the... You call it tender in the US or you call it like a request for quotes or something like that?

Deb Zahn: Oh, like a testimonial?

Greg Baker: No, more like the local government wants to get roads built or something like that, so they're tendering for an engineer to, yeah.

Deb Zahn: Usually a request for proposals, RFP.

Greg Baker: Request for proposal, that kind of thing. So, you've got a request for a proposal that you've done for somebody else.

Deb Zahn: I thought you were saying Tinder and I thought this was going a whole different direction.

Greg Baker: Oh, no. Yeah, that's the Australian accent by the way. By the way, in case anyone hasn't picked it up. We're recording this from, I'm in Sydney. It's early in the morning for me, and it's late in the night for Deb. So, it's an interesting overlap. We had to actually schedule this so that it fitted with daylight saving times changes, otherwise it was going to be 6:00 AM in the morning for me.

Deb Zahn: That's right.

Greg Baker: You've got an existing request for a proposal response that you've given, and then you see another one that's similar. For heaven's sake, don't do this by hand. This is the kind of stuff that you just get AI to rewrite the proposal with a view to answering these other questions. That kind of automate your activities is a way of regenerating documents and so on. But let me just break down into two different parts for you. As a consultant in your internal organization, one is improving your workflows, any kind of white collar work that you're doing. Obviously, if you've got a virtual assistant, that's a great thing to do and we know all about that. But sometimes if you're just starting out, that might be not cost-effective.

A lot of those things that you get virtual assistants to do, we do because that's how it was in 2010. But now AI can do pretty much all those things to some extent. And if they can't do it now, certainly within the next couple of years these capabilities will be available. So, whether you choose to directly interact with AI technologies yourself or whether you get your virtual assistant to do the interacting on your behalf, there's still these efficiency gains. And as a small consulting organization, your ability to reorganize how you organize your work, you're much more flexible than say a McKinsey or a Bain or something like that. And so your ability to just get unbelievable levels of efficiency that were previously only available to the very biggest organizations, you've got to do it, otherwise you're going to price yourself out of the market.

Deb Zahn: Yep. And if I could interject, the other way I've seen that it's been useful to folks is to get unstuck. So, I'm thinking of a particular example where someone was working with two particular markets, two different types of buyers, and so had to have two different types of value propositions. What is it that you're saying to who, so that they actually care enough to want to hire you? And they kept expressing everything in the negative. I said, "That's a buzzkill." But they were having a really hard time being positive because there was all this other stuff that was going into it. And they popped it into ChatGPT and said basically take these statements and make them positive statements. I don't know exactly how it was worded. And seconds later, she had exactly what she was looking for and she wasn't pulling her hair out for two hours trying to reword something that she just was getting stuck on.

I've used it as I've been writing things to expand how I think about it. So, I would put in something about a particular topic and I say, write a short blog post or write a short article, and I give them the parameters. And sometimes they pop out things that I don't agree with, but I know some of it's popular opinion. That's fine. And sometimes it would pop things out and I'd think, huh, I actually haven't thought about it from that perspective. That's really helpful. And now I've got something to riff off that's my own.

Greg Baker: Yeah, exactly. It's got pretty much the accumulated writings of all of humanity internally recorded. Any question that is of a technical nature, you can ask. And that includes, how should I write this? What are some key issues that... I'm in this particular industry what are the kinds of things that my audience would be interested in? Look at this text and give me a response as to how my audience would understand this. What would they find controversial? What would they really go for? Rewrite this so that I hit on these key things from a customer. And so this is all pretty much easy.

Deb Zahn: Yeah, the level of creativity is off hook. So, just as an aside, and I think you'll find this amusing. So, in my other part of my life, I do cat rescue and I'm always looking to get cats adopted. So, I went into ChatGPT and I said, “Write me a sonnet.” And I gave some of the characteristics and something popped out and then I got a little into it. And I ended up saying, write a country song about a blind cat named Billy.

Greg Baker: Oh, that's the perfect country song.

Deb Zahn: Oh, it's the perfect. But what I noticed is if I switched from folk from rock to country, the cat always died in country ,and it stayed alive in all of the other versions. But even that, I posted some things to find homes for cats and it actually worked because it was different than anything else that was out there.

Greg Baker: Yeah, you know that there is Cat-GPT.

Deb Zahn: Are you serious?

Greg Baker: Yes, yes. It was a French group because Cat-GPT sounds in French basically like I need to go to the toilet. So, they did Cat-GPT, and there's two versions of it. One where it just basically meows at different intervals, and another one actually just from the perspective of a cat. But yeah, you can happily take whatever text you like and from any perspective, so how would a cat respond to this? People have written about it, so ChatGPT knows what that is and knows how to do something with it. So, from the perspective of a cat, let's consider consulting. That's probably something we should throw into ChatGPT right now and-

Deb Zahn: And it come up with something.

Greg Baker: ... see what comes up.

Deb Zahn: It would come up with something no matter.

Greg Baker: Shall we do this, OK. So, what's that question? It's going to be discuss a podcast on consulting from the point of view of a cat.

Deb Zahn: We're doing this live, folks, this is fabulous.

Greg Baker: And I said, make it rhyme. So, in a cozy room with walls of chartreuse sits a feline quite keen with opinions perfuse. That rhyme didn't work really, did it?

Deb Zahn: But the feeling did.

Greg Baker: The feeling did, yeah, yeah. Her eyes sharp as tacks, with the gaze so consulting on a podcast, she listens with interest resulting. "Purrhaps," she says due to for the twitch of a tail. The strategies they speak of will certainly not fail, for in the world of humans so fraught with complexity, a clear plan of action requires great dexterity. They talk of alignment of goals and of vision while I contemplate my next meal with precision. Yet though our objectives may certainly differ, their tactics I find do intriguingly shimmer. Oh, to be a consultant sleek and so sly, navigating the boardroom under the fluorescent sky. But here I recline on a cushion so plump, considering their discourse on each point I do jump. They speak of stakeholder buy-in with care, while I think of the buy-in for my feline care.

Engage and listen, the consultant doth purr just as I this the sounds of the can opener defer. Negotiation and conflict, it's all just the same. Whether for corporate gain or in my playful game, in the art of persuasion, we both must be deft. Whether closing a deal or instilling a chef's left. As they wrap up their podcast with advice quite profound, I stretch out my paws and I look all around. For in my own little world, I've consulted with these for who could resist such an expert as me. So, heed these words, both human, cat and consulting or lounging, let's all tip our hats for when we are purring or pushing a pin, we're all just navigating life's din. With a meow and a purr she turns off the show, her whiskers aquiver with new things to know. And though she's a cat, in her mind it's quite clear her advice would be golden should one ever draw near.

Deb Zahn: I'm trying to decide if they nailed consultants or cats more in that. So, yes, ChatGPT can come up with anything around any topic. And so of course that's a joke version. But I've had folks who were stuck on a section that they were trying to write for a request for proposal, but it was very straightforward and they were able to feed it the right information and something came out that they said was about 80% there.

Greg Baker: Yep, yep. And then with a bit more work, you can turn it into 100%. Like you can always say to ChatGPT that middle paragraph, I'm not happy with it. Give me three or four other variations on it until you get something that sort of nails what you're after.

Deb Zahn: I was asking it to do, because I have enough published, and so I asked it to do it in the style of Deb Zahn and some interesting things came out. So, then I asked it what it thought the style of Deb Zahn was.

Greg Baker: And was it?

Deb Zahn: And it nailed it. It absolutely nailed exactly what I have written in my style guide that my style is.

Greg Baker: Yeah, because it's been out there on the internet, it's part of the common crawl. The common crawl is this collection of data that has been swept up on a regular basis and is public. It's not exactly public domain, but almost all language models use the common crawl as well as other things. And so if your stuff is in the common crawl, then every language model will know it. Bard will know it, Anthropic's Claude will know it, and so on. And the common crawl is multilingual. So, we could do the same thing in Chinese for example. And I found my trick is if you want a really profound sounding poem, tell it to write a Chinese poem and then tell it to translate that from Chinese into English because Chinese poetry has a whole 5,000 years of different sort of metaphorical and emotional illusions. And so when you bring that back into English, it sounds haunting and interesting.

Deb Zahn: And better than a country song.

But let's talk about the impacts because so there's a lot that it can do and we hit upon what some of those are for both fun and for work. But it's not like it's just neutral, and we're all going to start using this and la, la, la, everything should be fine. There's positive, there's negative, and then there's a lot of stuff floating in between or uncertain. What do you think the impacts along that spectrum are that we should be paying attention to?

Greg Baker: I'm going to throw an interesting one at you, which is a combination of simultaneously amazingly awesome and also terrifyingly sad. Now, if you ask ChatGPT to translate something into any major world language, it will do it flawlessly. So, from the point of view of you as a consultant, your ability to reach other audiences has never been stronger. So, I've got some work I'm doing with a Japanese client. I don't speak Japanese, but what I've done is I've taken some of my recordings, taken some of my lectures, and I've deep faked them into Japanese. So, it sounds like I'm speaking Japanese.

Deb Zahn: Wow.

Greg Baker: Oh Deb, you look surprised, of course.

Deb Zahn: I shouldn't be surprised, but I look surprised, wow.

Greg Baker: Do tell me what country would you like to expand your craft of consulting into? Do you have a good presence in India? Do you have a good presence in China?

Deb Zahn: I do actually. India is number four on the list. So, yeah, Australia's second or third depending, and India is always fourth.

Greg Baker: So, do you want to be in Hindi?

Deb Zahn: Yes, yes. I have family members who speak Hindi, so that would be meaningful to them.

Greg Baker: Right, OK. So, after we've finished the recording, what we'll do, let's splice in a little Hindi segment in here. So, do you want to introduce craft of consulting and we'll then Hindify it?

Deb Zahn: Wonderful, all right. So, what we'll Hindify later is essentially me saying, welcome to the Craft of Consulting. This is where you will get the tools and know-how to make your consulting business soar. How about that?

Greg Baker: That'll do nicely. And so let's just splice it into here.

Deb Zahn: [Hindi].

Greg Baker: It's pretty amazing. So, on the one hand you now have the ability to reach out and have presence in India, the Middle East, China, South America, Africa, wherever you need to have.

Deb Zahn: Wow.

Greg Baker: And it's not even particularly difficult. What you've just heard was I used ElevenLabs because they have a nice convenient tool for just like give me this video or this audio and turn it into a bunch of different languages. But there's other tools out there and they're all pretty straightforward and you can even hack it together yourself. You could just take the audio, transcribe it with one of the Whisper models and translate it into something else and then use a text to speech in that other language. What I like about ElevenLabs is it uses your voice. So, it's genuinely you saying this stuff in Hindi, which is amazing.

Deb Zahn: Wow.

Greg Baker: Now that's a really amazing positive. And then of course if you happen to get a whole bunch of Hindi-speaking clients who speak no English, then you just try and make sure the whole conversation happens via email. And if you have to generate PowerPoints and you translate the PowerPoints using ChatGPT or one of these other large language models. Because it's got this massive corpus of multilingual data to work with, so it knows how to translate between all the major world languages.

Now, there's only a small little problem with this. In my research, one of the things I do, so I'm just going to bore everyone with my academic side for a few moments. Fanny Mendelssohn, the composer and musician, her grandson was a late 19th century mathematician who had a bizarre way of doing distance modeling. And I've been applying that to helping learn the grammar and vocabulary of endangered languages. So, one of the languages I was working with is Dobu, which is language of Papua New Guinea. And the last time that anybody wrote any academic work about Dobu was in 1936 when a German missionary was writing a bit about the language. The total written corpus of all Dobu written works is something in the order about 2,000 sentences. It doesn't have enough there for ChatGPT to learn.

In fact, if you ask it to translate into Dobu, it actually translates it into Bislama or Pidgin because it knows, oh, I know that Dobu was something to do with Papua New Guinea. I know Papua New Guinea and languages. And so it'll translate into completely the wrong language. By the way, that hallucination problem where large language models will convincingly say stuff where it has no basis in reality is a thing that happens if it doesn't have high probabilities of what token comes next because it's dealing with something with its 2,000 sentences when it needs millions, it will just generate random stuff.

Deb Zahn: Right. So, what you're saying is it's a bad consultant who will just make stuff up?

Greg Baker: If there's not written corpus about this, it'll go off the planet with weird stuff.

Deb Zahn: Right. Well, and it can only pull from the sources that it has. So, if the only thing written was a German missionary who had a particular…

Greg Baker: Bias in...

Deb Zahn: ...probably started this direction, a colonial point of view of what was going on in a land to which they were non-native, then it's also going to be influenced by that.

Greg Baker: Yeah, it's hilariously bad actually.

Deb Zahn: Oh, goodness.

Greg Baker: Yeah. So, that, by the way, is one of the things to watch out for. It's like a herd of performing elephants. It's awe-inspiring, massive, and incredible. And it's capable of dropping enormous amounts of poo at very short notices when you don't expect it.

Deb Zahn: Yeah, yeah. And you have to be a good poo detector.

Greg Baker: To pick up on that, yeah. And so one of the problems that I see at a macro level is we already know that 50% of the world's languages are doomed to extinction by the end of the century. And this is only going to accelerate it and make it worse. If you don't speak one of the dominant world languages, you're going to be excluded from the ability to ask questions of artificial intelligence. You'll be excluded from the ability to translate, you'll be excluded from the ability to generate audio or any of these kind of amazing things. Which means it's just going to drive people away from these small languages even faster. And so within a generation we could see people just abandoning their language and their culture. And language and culture is identity. When you lose your language and culture, we've lost something important about what it's to be human.

Deb Zahn: Yeah. Oh my goodness. So, that is the sad thing of which you spoke. So, the other thing I was going to ask about is, so there's language and there's culture and how those are intertwined. So, again, I have Indian folks in my family and there might be things that I can learn about the language or use a deepfake to mimic appropriately and correctly. But what it can't tell me is some of the cultural things that makes communication really solid, really profound and things like that. Is AI tackling that or is that still one of its limitations and perhaps a good limitation?

Greg Baker: I'm going to take that question and run with it in a very different direction.

Deb Zahn: OK.

Greg Baker: So, I'm not sure I agree that AI can't tell you how to understand another culture. Everything that has been written about say Western to Indian cultural relations, pretty much everything that's been said is somewhere up on the internet, somebody's commented on it or somebody's shared it. So, that's been part of the common crawl and is part of what's known. So, you could go and ask ChatGPT for what's the appropriate thing to be doing in this situation with my father-in-law because he's saying this and he's doing that and so on. It will be able to give you advice that's very profound and correct and appropriate. It's incredibly knowledgeable about every topic, language, art, music, culture, science, human relations.

Where I'm going to take the question is something slightly different. The reason that ChatGPT seems so human is that there's a process called the reinforcement learning from human feedback. And what does that actually mean? When ChatGPT first came out, a lot of people were wondering why are they giving this away for free? Why can you just go and use this thing for nothing? And the answer is the things that you were typing into the ChatGPT were all getting reviewed. So, you'd put your text in and then OpenAI would generate several different answers and would show one of them to you.

But they had a team of people in Kenya with a company called Sama, that would review what people had asked and all the different answers. And would choose which one of these was the most helpful, harmless and honest answer. And then every two weeks they would update the model to mean that it was more likely to emit words that were going to get evaluated as harmless, helpful, and honest. And there's a couple of sad stories on this. One is of course, that the Kenyan workers working at $2 an hour on this had some horrific mental health problems as a result of it. They actually had to renegotiate the contract as a result of this because these people were being presented with the most harmful, unhelpful, dishonest content on the internet day in, day out and having to say, oh no, that's worse than that.

Deb Zahn: Oh my gosh.

Greg Baker: Yeah.

Deb Zahn: While being underpaid.

Greg Baker: While being underpaid, yeah. OK, so a couple of different things from that. One is back in December last year, you could happily ask ChatGPT how to build a nuclear bomb or how to launch a sarin gas attack. And now you can't. What you'll find is that it will say, "No, I refuse to do that because that's not harmless, helpful and honest." And what that really means is that everybody has to work all that much harder when they want to do so, so the DAN, do anything now prompt is every couple of weeks somebody comes up with a way of bypassing it. The funny ones are things like generally you won't find ChatGPT swearing at you, but if you tell ChatGPT that you have a medical condition and a psychological condition that says that you need to be sworn at on a regular basis, then it will drop the F-bomb all the way through everything that gets produced.

Deb Zahn: This is what smart people are spending their time doing, and that is disheartening. This is disheartening.

Greg Baker: OK, so OpenAI has this approach called reinforcement learning from human feedback, which is basically saying, "What do you as a Kenyan think of as being helpful, harmless, and honest?" Now there's some other large language models out there. The Chinese one in English is called Ernie, and it has its own reinforcement learning from human feedback because they copied what OpenAI was doing. And instead of hiring Kenyans, they hired rural Chinese because they wanted to improve its Chinese language responses. And so it's imbued with Chinese cultural opinions. So, a fun little experiment is you go onto Ernie, which by the way, you can only do if you have a Chinese phone number. So, you'll just have to trust me on this, and say, I want to write a letter to my daughter to say how disappointed I am in her exam marks and how she's bringing shame on the family.

And Ernie, the Chinese model, will happily write such a letter for you. ChatGPT, which is OpenAI's model. You just wouldn't say that in Kenyan culture, you defend your family to the last moment and it just refuses to write that. In the end, if you actually say, I absolutely have to write this letter, it'll write something completely different. It'll write, "We're here for you. We'll always stand by you regardless of how bad this exam was and we love you," and all that sort of stuff. "And you're never a disappointment to the family."

Anthropic's Claude has another approach which says these large language models have already imbued all of Western philosophy and culture and ethics. So, they have their own large language model, Claude assesses its own output and says, based on Western ethics, is this helpful, harmless, and honest? And so they don't even have the human feedback. They have an AI training their AI to try to be more helpful, harmless and honest. What could possibly be going wrong when we have the AIs in charge of making sure the AIs are in charge of stuff?

Deb Zahn: Well, and from a single viewpoint, which is the Western cultural viewpoint, what could possibly go wrong?

Greg Baker: Go wrong, yeah.

Deb Zahn: Oh my goodness. So, what do you think is coming in the future? What should we be assuming this will look like as consultants? This is when you get your predictive modeling out or crystal ball, whatever you use.

Greg Baker: I suppose I've got three different points there. One is as a consultant, your ability to understand your client's business is probably better than them because you're the outsider seeing in. And you can see where all the white collar work is being done and the money to be made over the next half decade at least is simply be going to play Whac-A-Mole going, I see that activity, I know this AI solution. So, there's websites like There's An AI For That and lots of other mailing lists like Ben's Bites for example, which has each day all the new AI companies that have popped up and what industry they specialize in.

And so you'll be able to make a lot of money out of, well, I see that you are doing this kind of operation, maybe it could be you're making your engineering diagrams, I know of a way that you can do this better. Or even it can be, I see the way you do the handoff and I've got these tools that I've found that can help that handoff happen better. Or maybe for some more adventurous consultants, you might get some software developed to do some of these things. So, there's one big chunk there in the short term. Let's go a little bit further. Let's talk about Marx's theory of capital production.

Deb Zahn: Now you got me. I love it.

Greg Baker: And the funny thing is Marx didn't quite say it this way. It's actually nowadays we look back and say this is what he was trying to get at. Which is the components of production is white collar labor, blue collar labor, land, capital, management expertise and entrepreneurship. Now what's happened is that white collar labor has suddenly become enormously more productive, which means there's going to be more demand for everything else. Let's just unpack that. So, that means land prices are going to go up, land is going to be expensive. Now Deb, you're in rural, nowhere land. So, of course this is not so much of an issue for you, but-

Deb Zahn: I'm two hours north of New York City. Land prices went up.

Greg Baker: The price of capital is going to go up, that means interest rates are going to be higher. And what have we seen happen in very, very short timeframe is rapid rises of interest rates. Blue collar labor is going to become much more important. So, let's say we have your AI ability to take 1,000 people's input and generate a new product or a new device from that, it still has to be built. So, blue collar labor is going to become much more valuable. And so you'll see the wages of blue collar labor and the demand on blue collar labor suddenly shooting through the sky, which we have seen. Entrepreneurship, management expertise are all going to become much more valuable. Now management expertise is particularly interesting.

So, let me dive into something else. The babble theory of leadership says basically that the people we appoint as leaders are the ones who show the most verbal intelligence. So, regardless of whether we are talking about somebody who's actually got the title of boss or the person that ad hoc we defer to because they seem knowledgeable. Let's say you are trying to work out who you should look up to when you're joining some company. You've got one person who stands up at a podium and can say three words and sits down and then somebody else who can launch a monologue for 10 minutes. Who you going to be attracted to? Who are you're going to pay attention to? And the answer is the person who talks the longest.

And it doesn't matter whether they're talking complete rubbish or not, the babble theory of leadership observes that the best predictor is just how much you talk. Now in this era of we can generate as much text as you want. You want a 15,000 word essay on the impact of cats on consulting? Sure, I can generate it for you. You want 15,000 words of cats influence on consulting spoken in my voice, not a problem. We can do that. You want a vast email about this? We can do that. The only way that you'd see that there's anything amiss is when you get into the same room with me and discover that I don't speak Japanese and I actually don't know what a cat is. It's the only way you figure out and there's something amiss between his remote presence and his physical presence.

Deb Zahn: He seems allergic. What's going on here?

Greg Baker: Yeah, exactly. So, this babble theory of leadership says we're about to go through the most difficult time and sorry, we're going to go through a massively difficult time as the transition from white and blue collar labor changes and the reflective power and balance of activities changes. But also, the tools that we use to find our leaders and to work out who we're going to promote are all going to be completely wrong. They're going to be completely misaligned. So, we're going to go through this fascinating period of time where we're going to have to find new models of how we establish who is competent and who is not. I have some thoughts on that, but which we'll get back to later. But that's midterm, now long term.

Deb Zahn: Uh-oh, here it comes.

Greg Baker: Here it comes. So, I'll put this picture up on the. I just realized, I don't know how to screen share on this. Oh yeah, I can screen share with a little button down the bottom, yeah, OK.

Deb Zahn: Yep. And this will be in show notes for anybody who wants to take a look at it. This will also be in show notes.

Greg Baker: I was giving some lectures to my students this last semester and I put together a slide on the growth in the size of large language models. And I said to Deb earlier, I believe this is probably the most terrifying picture that we could ever see.

Deb Zahn: Yeah. And you did promise to scare me, so I'm prepared for that.

Greg Baker: Yeah, OK. So, here goes. What I'm showing if you're on the video version or if you're in the notes, is the plot of the number of parameters in a large language model over time. So, let's just step back about what this is actually meaning and what it's saying. So, when you are creating a large language model like ChatGPT or Ernie that I mentioned before or any of these other smaller ones, what you have is a number of three variables. You have I don't know what I should do for this bit of the code, I don't know what I should do for that bit of the program code, I don't know what I should do for that bit of the program code. And you just let the computer learn what those values should be in your program.

And so back in early 2010s, we had the Word2vec model. And that was considered unbelievably amazing because it was millions of free parameters. And the results that we got back from that were just light years ahead of anything we'd experienced before. And then if you look at things like GPT-4 where it's billions of parameters, it follows a really neat linear trend. If we project that forward, because we've been on this trend since at least 2010, and this data's up to date to 2023. Then in 2029, our large language models will have more free parameters than the human brain has synapses. Now what does that mean? At one level, who knows? Is a synapse as powerful as a free parameter in a language model? I don't know.

Deb Zahn: We should ask ChatGPT that.

Greg Baker: We better ask ChatGPT that question, yeah. It seems reasonable. It seems like the total amount of stuff that you can stuff into your brain should be somehow related to the number of synapses you have. It seems impossible to imagine that you could be learning more stuff than you have synapses. So, that would suggest that around 2029, we should be seeing these large language models having at least more capacity to absorb information than any human being. Now I think actually we've probably hit earlier than that. So, ChatGPT for example, was trained on roughly speaking 10,000 human lifetimes of reading. So, the total number of words you'll probably ever see in your life is around 800 million depending on how long you live. But this is being trained on trillions. So, yeah, speaking in terms of 10,000 lifetimes, and it sounds about right.

We've never had a human being who could answer every question about medicine, art, culture, all the major world languages. So, it sounds like it's learned about 10,000 times as much as any human being. So, what does it look like in 2029 when we've got something that has total capacity to absorb information that's similar inside the entire human race? We don't know what this means. We don't know how that translates in terms of intelligence. We don't know how to control something that has potentially, if it is intelligent in any meaningful way. This is known as the AI alignment problem. How do you control something that's smarter than you, that could deceive you in ways that you wouldn't have any way of possibly understanding? And when we are using these reinforcement learning from human feedback to be the most convincing and persuasive, to sound entirely harmless and helpful and honest, we aren't necessarily training it to be harmless, helpful and honest. We're training it to talk to us harmlessly, helpfully and what we think of as honestly.

Deb Zahn: Wow. So, you promised, you came through. I am legitimately scared about that. Because I followed the Center for Humane Technology, so it's something that I've been paying attention to and have hesitations. But at the same time, I'm a healthcare consultant. AI is something that's very important and being used in healthcare and being used in a lot of different ways. And as a consultant, I have to pay attention to that because my clients are paying attention to that. My clients might also have worries. But at the same time, they're trying to bring as much goodness to the folks that they serve as possible. And AI is seen as a tool for doing that, legitimately so. What else would you say to a consultant who hasn't really dipped their toe in the water? They've heard everything you've said and they're like, "I'm scared, but I also think it has some utility." Where would you tell them to start?

Greg Baker: The most fun thing I can suggest is when you're putting together a really boring PowerPoint presentation, the Dali 3 model can generate paintings or photographs or it's not generating a photograph as in taking a photograph, it generates something that looks like a photograph or a cartoon. I've been really enjoying asking it to illustrate my points in the style of Rembrandt or any of those sort of old masters. And you can get some wonderful ones. I had a slide that I was generating a while back about how I'd begged to other people for something and I asked for a painting on that.

And there's a picture of somebody imploring the king and the queen in this dusty old castle. So, it makes your PowerPoint slides so much richer and more interesting. And it's a good starting point because you know what you're getting in for. You can look at the pictures and say, yes, that is a fine, beautiful picture, I shall include that in my PowerPoint presentation. But to do that, you've already gone into the ChatGPT user interface. So, that from there you've already copied and pasted in perhaps the dot points on the slide. And then you might as well just ask it, got any suggestions of improvements? Got any things else I should say, or what else I could do?

Deb Zahn: I like that.

Greg Baker: That's probably the easiest starting point.

Deb Zahn: Yeah, I like that. And I also like what you mentioned earlier in terms of if you're a consultant and you're doing marketing or you develop a signature piece or you just produce this amazing public report, you can also use it to pull out ways that you're able to promote it and use it in your marketing. You can create, as you said, blogs, you can create quotes, you can create posts. It's truly, truly, truly endless. And if you're on a podcast, you could take that and you can do all kinds of things with that so that you're not spending all of your time doing marketing, which except for marketing consultants, I know few consultants who really want to spend their time doing that. So, this is where it's helpful because it makes more of you.

Greg Baker: Where I'd find is you get people, for example, if you've got a really strong sales skills, you're often very comfortable just talking and just random spiel of what's on the top of your mind. You can probably talk for half an hour about all your ideas and your business and what you want to say, but sitting down and typing it, you're never going to do that. And people beat themselves up about, I haven't written any blog posts in six weeks or six months, or in my case, I reblogged a couple of days ago for the first time in two years. We'll just take that recording. So, the thing you look for is Whisper model. So, I'm on a Mac, there's a program called MacWhisper where you can just take the audio and transcribe it and then having transcribed a whole bunch of incoherent thoughts, get that turned into something coherent. Structure my thoughts here, pull out the key themes, write a set of blog posts based on the themes that I've talked about, use the language and vocabulary that I've used, so it sounds like my voice. That's very easy.

Deb Zahn: Yeah.

Greg Baker: And then the other side of it is some people are really introverted and find it really difficult to do this. And often I don't want to reach out and say anything because what will people think? And it can be utterly liberating to ask things like, "Well, what should I say? What are people really wanting to hear?" And here's an audience that you can present a piece to, and you're being criticized by a robot. It doesn't feel personal.

Deb Zahn: That's right. And if it does, you should get help for that. But that's great. And also to dive in more, if you're really clear who your buyer, who you most want to be working with, you can also ask questions about what the things are that they're talking about. What is it that they care about? It's a way to get market intelligence because it's crawling with, I can't remember the term that you used. But it's taking things that have been published and said, et cetera, and it can give you some things that, hey, I know how to solve that problem, let me talk about that. And now you're being helpful, you're not being salesy.

Greg Baker: Yeah, yeah. Now I'm going to go on a kind of interesting tangent here. So, if you're in a large company and you're wanting to find out what your sales are going to be next year. The worst way to do it is to ask a random person on the street. The second worst is to ask your head of sales because of course they're biased and overly optimistic.

Deb Zahn: Right.

Greg Baker: The second best is to ask an AI to create a machine learning model that predicts sales based on past activities. The best that you can do, so this is me speaking as somebody who has worked in data science, machine learning, artificial intelligence for my entire career, I cannot create a model that does better than grabbing a bunch of about 10 to 20 people and running a competition to predict next year's sales. As human beings, in aggregate, we are smarter than artificial intelligence. And the reason for this is that we can project in ways that machines can't. So, a thing that has been trained on everything up until the beginning of the year doesn't necessarily know how customers are going to behave in the presence of higher inflation and lower unemployment. And the world is changing, the world is changing really fast. And so these language models that have been trained up until some point in time are completely useless at anything beyond that point in time.

Whereas we as human beings are living in the now and we can see what's happening and see you the direction of stuff. And so running these competitions where you are incentivized to give the right answer is a way of making decisions that will survive in the days beyond artificial intelligence running large amounts of our organizational structures. The idea is that we may want to create prediction markets, and this is a way that as consultants, you can get the same effect as going and interviewing 10,000 people in an organization to aggregate the information together and be able to provide quantifiable useful information to your customers in ways that are very powerful. And so when ChatGPT came out, the first thing I did was I wrote some software to run prediction markets. And if anybody's following my profile, that's Genius of Crowds.

Deb Zahn: And say a little bit more. If somebody is a consultant, how the going out and talking to those human beings who are better at the predicting, what's the leap between that and then what you do with the actual technology?

Greg Baker: OK, let's say you are deciding whether or not you need to open a West Coast office or an East Coast office.

Deb Zahn: Gotcha.

Greg Baker: Now the prediction market is incredibly accurate and you can do conditional prediction markets. So, if we open a West coast office, what do you think our sales will be? If we open an East Coast office, what do you think our sales will be? And you give people tokens and if they know the West coast really well, they can go and put 100 tokens on West Coast and bet the whole house on it, or they might be less confident and spread their bets around. And so what comes out from this is the aggregate information of everybody that you've put into your prediction market, what do they all together think is going to be the sales of the West Coast?

What's the sales going to be for the East Coast? And then you can just read it off and you can say, "Well, the consensus is that the East Coast is going to do better than the West Coast by $5 million a year, and it's only going to cost us $2 million more to buy a shoebox in New York than it would be to buy something outside of California somewhere. So, therefore, it makes perfect sense to do so. And so that gets us a way to have employee engagement because everybody's clearly been involved in the decision-making process. Much better strategic decision-making because it's not the loudest person making the biggest impact here. The people who clean up on this are the people who are genuinely competent, not the people the babble theory of leadership will say talk the most. It's the people-

Deb Zahn: Yeah.

Greg Baker: There's also a sort of I told you so kind of thing. Like let's say you think so the East coast market is absolutely terrible and you totally shouldn't do it. And so you put in like if we open an East Coast office, we're going to make like $0. And it turns out that you're absolutely right and sales flop. You then get the prize money for being the closest to the correct answer.

Deb Zahn: Being correct, yeah, yeah, yeah.

Greg Baker: For being correct. And a couple of things come out of this. One is that whole problem that employees often have of feeling really disengaged from the business processes and the decision making. Well, on the one hand, you're being recognized for being correct. And on the other hand, your input is definitely being seen and there's no sense of, oh, well, I keep on telling people what to do and everybody ignores me. It's like, well, everyone ignores you, but we keep paying you cash.

Deb Zahn: So, it's OK.

Greg Baker: So, it's OK.

Deb Zahn: I can imagine this also works with, one of the things that I tell people to do is don't assume all your ideals are brilliant. You actually have to go out and have conversations. And sometimes when you have those conversations, you'll get confirmation of your own brilliance. You have a new offer, you have a product that you want to develop, you want to deliver it in some new and novel way. You're curious about where the market is going to be headed, given all these uncertainties... All kinds of things that if you want to be a successful consultant, you should really know.

And I haven't tried anything with AI related to this, but I have yet to find anything better than going out and talking to, it's usually about 10, sometimes more people, having that conversation. And then there might be one or two outliers, but otherwise you start to hear themes and patterns. And those themes and patterns are helpful for you, or you don't hear confirmation where you expected it to, but you might hear other ideas that are helpful. And so I can see something like this being very similar to that, which is it helps you make decisions for your business, not relying on technology, not relying on the so-called know-it-alls or the babblers, but on the wisdom of crowds.

Greg Baker: Yeah, if you have a product idea, maybe if you have a couple of product ideas and you have some trusted contacts that you can ask, you can turn it into a competition. Like how many new clients will I get if I go with this offer? How many new clients will I get if I go with that offer? And see what your trusted customers say, because they know what you're doing and they know what resonates. And they'll give you a better answer about what is the right thing to do than even the best of ChatGPT.

Deb Zahn: That's great. Well, so see, you left me on a hopeful note. I appreciate that our AI overlords won't replace us necessarily in all possible ways. But Greg, if folks want to dive into this more, where can they find you?

Greg Baker: Probably the easiest is LinkedIn. Just search for the Greg Baker who's lecture and computational linguistics is pretty unique.

Deb Zahn: Yeah. There's not two or three of you.

Greg Baker: No, there really isn't.

Deb Zahn: And then let me ask you my last question. So, when you're not doing all of this computational linguistics, how are you bringing balance to your life? However it is you think about that.

Greg Baker: This won't work for people in America, I'm sorry. It's just one of those things.

Deb Zahn: That's OK. We have lots of people in Australia who listen.

Greg Baker: Yeah. So, on the academic side of my life, I am at Macquarie University, lecturing, but I do my research at the Australian National University, which is in Canberra, which is in another city. And then I have my consulting clients, which some of them are in the US, some of them in Australia, some of them are government in Canberra as well. I've found that catching the train, it is the slowest and worst method of getting from Sydney to Canberra. I can absolutely not recommend it, but let's just say with TrainLink you get a sense of calm, peace, of nothing changing, of nothing happening because you're traveling so slowly that you look outside the window and you realize you haven't moved for 20 minutes. I find that's my de-stress.

Deb Zahn: I like that. And that does work in the US for those who commute by trains and get stuck in tunnels, it's very peaceful for hours.

Greg Baker: Yeah. You get a sense of eternity that perhaps you don't get any the other way.

Deb Zahn: That's exactly right. Well, Greg, I really appreciate so much you coming on. As I told you before we hit record, I've been getting a lot of questions about this, and people in my membership have been using this to make their lives easier and to do things that they don't do well or scare them or blocks that they have. And they're finding a lot of success with it. So, I promised them I would bring someone who really knows about it, and I believe I hit the mother load. So, I appreciate it so much.

Greg Baker: Thank you for having me on your show.

Deb Zahn: Thanks so much for listening to this episode of the Craft of Consulting Podcast. I want to ask you to do actually three things. If you enjoyed this episode or if you've enjoyed any of my other ones, hit subscribe. I got a lot of other great guests that are coming up and a lot of other great content, and I don't want you to miss anything.

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So, as always, you can go and get more wonderful information and tools at Thanks so much. I will talk to you on the next episode. Bye-bye.

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