In this early episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative AI business continuity, the recent drama at OpenAI and what it means for businesses relying on their AI services. We talk about the importance of having backup plans and avoiding putting all your eggs in one basket when it comes to new, unproven technologies. We give tips for building business continuity plans for AI tools, emphasizing focusing on your core processes first before getting distracted by specific tools. We also discuss the pros and cons of some alternative generative AI options beyond the main players.
Normally, the show publishes on Wednesdays, but because of the Thanksgiving holiday and the timeliness of the news, it’s in your ears two days early this week.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn 0:00
In this week’s In-Ear Insights, this is going to be sort of a top of the news attention piece today.
Over this past weekend, and this is being recorded in late November 2023, OpenAI.
Their board essentially staged a coup and ousted the CEO Sam Altman, and then appointed the CTO, Marathi as the interim CEO, which then had Greg Brockman, who was the chairman of the board, and also a member of the executive staff also quit the next day.
The fit the feedback from their largest investor, Microsoft and the Microsoft CEO, Satya Nadella.
They’ve given OpenAI $10 billion in funding and I’ve owned 49% of the company said, Guys, what are you doing? And there was a round of negotiations that fell through.
And so what has happened as of Monday morning after this, this insanity is that the board had instead of stepping down, which is what their largest investor was telling them to do.
The board has appointed the twitches, CEO as the CEO of that of OpenAI.
Sam and Greg took an offer from Satya Nadella and are now heading up a new division at Microsoft advanced AI research.
And it appears that anywhere from a third to a half of OpenAI, senior staff are going with them.
So you have this massive brain drain, that the the future of OpenAI itself is very unclear.
So that brings me, you know, that’s the sort of the topical news that brings me to the topic I want to talk to you about Katie, which is scenario planning and disaster planning are not just for natural disasters.
They’re not just for, you know, acts of of deity.
They’re also for what happens when a critical vendor implodes, right, a lot of people are stuck in the idea that AI general AI is OpenAI, right.
ChatGPT is what they know Dali is what they know.
And they are dimly aware of things like Bing and Bard and Eclogue exist, but for them in the same way that Google is search to people.
OpenAI is generative AI, which means that many of their plans, many of the vendors that have built software around these ecosystems are now potentially at risk.
If OpenAI really did implode, and the core Brain Trust has left and gone to Microsoft, so talk about a your take on the news.
And then yeah.
What does this mean for people who are like us companies that are not, you know, core AI companies in the sense of like making our own AI? What does this mean for us? And how we rely on AI vendors, and how we should be doing some scenario planning around and what happens when kaboom.
Katie Robbert 3:00
Oh, well, I mean, there’s so much to unpack here.
So let’s just start with the whole, you know, leadership and, you know, everything like, that’s just a mess.
And that, obviously, you know, we, as the general public don’t know exactly what happened and exactly what the conversations were.
And if this was a long time coming, and to us, it just looked like snap decisions, like, we don’t know any of that.
What we do know is that the optics have really destroyed the brand trust, because whether it was a long time coming, or it was a snap decision becomes irrelevant, the way that it was handled publicly, now has shaken us as consumers to think that this company can be run well, and that we can actually, you know, use the product in a consistent way.
Because if everybody’s jumping ship, and then the board doesn’t know what’s happening, and then they’re letting people go, and then they want the board to also leave.
And then this company over here is like, it’s a mess.
And so as consumers, all we can do is sit back and go, Huh, guess I better find a different product to get behind.
And this becomes the issue when these products have the dominant market share.
And so you know, OpenAI to your point, Everyone just assumes, well, it’s just ChatGPT.
So now everybody’s using ChatGPT, because they were one of the first to market with a consumer friendly version of generative AI.
So that’s what everybody jumped on board with.
That’s what everybody you know, that’s the name that everybody associates and so now, this name is no longer trustworthy, and people have not taken the time to your point to learn Bard and all of the other ones.
But now, if I were bar If I were an AI, in the same boat, I can’t think of the names of the other ones.
But if I were in charge of those products, I’d be like, great.
This is our time to shine.
Let’s now get out there, show people what we can do show people that we have comparable, if not better features and outputs and usability and let’s just like, you know, forget what OpenAI is doing.
Like, just put them in the rear view, like this is our time to shine.
So that’s number one.
I’ve already exhausted number number two to your question about scenario planning.
You know, this is something that we’ve talked about.
But it all feels very theoretical until you actually face yourself, or are faced with, Oh, crap.
The product that I’ve been using for a long time that is now embedded and integrated into my business is no longer available.
What the heck do I do? So, you know, we talk about scenario planning, but nobody does it.
We know that it’s a good idea.
We know that we should do it.
Nobody does it.
Like, let’s just set that expectation.
We know this.
Nobody does it.
And so we find ourselves over and over and over again panicking, because we’re like, Oh, crap, OpenAI just, like imploded fell apart.
So I can’t use ChatGPT.
But I’ve built a whole, you know, I felt three different webinars and a talk.
And, you know, it’s written my code and this and that, and I’ve, you know, I let go of all of my content creators, because ChatGPT was going to do it for me.
And now I’ve built my custom Katie GPT-3 model, and I can’t use that anymore.
Like, there’s a lot of things that we have now relied on.
And so other than Yes, I
Christopher Penn 6:44
do I say real quick as of right now.
OpenAI software remains available, nothing has changed.
So just people like Oh, my God, it’s good.
It’s not God, everything is as it was Friday morning.
From a our perspective as users,
Katie Robbert 6:57
right, as of this recording.
But and I say that caveat, so things may not change, we may still have access to this product as is.
Or we may not, we don’t know, because we are not privy to those conversations.
And this is where your question about scenario planning comes into play Chris.
And so especially with newer technologies, newer brands, so let me step back for a second.
So you have brands.
So if we take the CRM example, you have brands like Hubspot, you have Pardot, you have Salesforce.
For the most part, those brands, those products aren’t going anywhere, it would take a lot for brands like that, that are well established, to completely fall apart now features that you rely upon may change but the brands as a whole are likely not going to go anywhere.
Maybe they’re acquired, maybe, you know, they rebrand they reskin things, the usability changes, but the core of what that product does, isn’t going to go anywhere, there is a well established brand.
On the other side of the conversation, you have OpenAI that is a newer brand in comparison, and therefore the product is newer.
So when you have a newer product, that’s when you should really focus on that scenario planning because it’s still volatile, it’s still vulnerable until it sort of hits that threshold of it’s well established for 18 months, three years, five years, 10 years, and you know that it’s stable.
Christopher Penn 8:40
The the the big questions that a lot of folks in the in the space have about the current events is not so much that, you know, the company will go away, but that that’s essentially a sort of end of the road for the innovation.
Right now we all use GPT-4 Sam Altman, that dev day, which was like a week ago, that said, you know, GPT, five is in the works.
That is unclear now.
And so to your point, there’s a lot of people watching very carefully, us included, see where Microsoft takes us, Microsoft clearly is going to be one of the beneficiaries of this anthropic will be Google, who apparently has already been the recruiter has been hitting the phone lines over the weekend saying, Hey, you want to come work for Google.
But to your point, it does damage brand trust.
On the technical side, one of the things that we’ve been talking about for a while now in generative AI is, is knowing the difference between an interface and a model, because we’re at a point now where the interface you use, and the model you use can be different so you don’t have to use ChatGPT.
With the GPT-4 model, right? You can use cat ChatGPT to interface with the KT GPT model, which is a tuned version.
But there’s also this entire open source ecosystem.
Same with Power BI Facebook’s llama two model that I run that for a lot of personally use because it runs locally on my laptop and it doesn’t cost anything to do.
So it is less friendly than ChatGPT.
But it is as capable as the GPT-3 2.5 Turbo model.
And so one of the things that we’ve talked about, for quite some time is in your generative AI strategy and plans, you absolutely should have alternate vendors like, hey, if this vendor goes away, we use this one, or if we’re trying to do this task with this model, and that model is not a good fit, use this one instead.
And I feel like a lot of people have not given that some thought of, okay, What’s plan B for any given generative AI task? Well,
Katie Robbert 10:51
it may not necessarily be that they haven’t given it thought it may not, it may be that they don’t have the capabilities for a plan B.
So when Google Analytics decided to roll out Google Analytics 4, we talked a lot about we even before that, we talked a lot about running backup systems like matomo, for if and when Google Analytics decides that it has functionality that you can’t use or data that’s changed enough.
And we’re seeing a lot of that play out amongst our peers and other agencies, where they’re having a hard time using the new functionality of Google Analytics, 4, and the data doesn’t match up.
So they’re like, what else can I use? What’s the alternative, and matomo, while not as user friendly, gets the job done.
And so now we’re sort of in that same scenario here with generative AI, the challenge is that a lot of companies don’t have the resources for a plan B.
You know, so you’re running llama two, from open source, which means you have skill set to code against open source, you know, language, you know, how to run a server from your laptop, you know, how to interpret, you know, whatever’s in GitHub, and all of those things.
That’s not true of a lot of companies.
They’re just not staffed that way.
And so, you know, think about it in terms of, you know, I’m gonna pick on PR agencies for a second.
Because PR agencies, the majority of the people employed by a PR agency, are going to be PR people and PR people by training are not developers.
And so this is where you start to see, okay, they have the skill sets to run a system like ChatGPT, because it’s a lot of writing prompts and iteration, when you take the skill set deeper, where they actually have to stand up a server and stand up their own custom model.
That’s where you start to lose people along the line.
If it’s just an interface, people can figure it out.
If they have to set up the interface for themselves.
That’s where you lose them.
Christopher Penn 13:13
Exactly.
This is from the workshop that we do, which by the way, if you didn’t get the email from us last week, we’ll send it to you again.
This is the gender of AI starter kit.
And one of the things I want to point out here is that for pretty much every major task on this list, there’s at least one other choice.
So for reading and writing text under 1000 words, you know, there’s ChatGPT plus GPT-3 point five or GPT-4, but I’m actually modifies se llama to is is good.
So as Claude, so those are all perfectly fine.
For text over 8000 words, you’ve got now Claude to GPT-4 Turbo and llama to extended, you’re creating images you got Bing image created with ChatGPT or with Dolly.
That that’s the beginner version for analyzing images Google Bard or ChatGPT.
For real time information Bing are barred for analyzing data ChatGPT Plus for sensitive information, llama two or as Azur OpenAI for writing code ChatGPT Plus, or the code llama.
So the only task on this list where there isn’t an alternative is the Advanced Data Analysis feature in in ChatGPT.
Plus, other than that, there is an another option for pretty much everything on the list.
And so even, you know, scenario planning for for AI capabilities could be as simple as this, like, Hey, here’s the major tasks we do.
Here’s our first choice tool.
Here’s our second choice tool, and having something like this documented, would probably go a long way towards certainly easing anyone in the operations teams, concerns like Hey, are we overly dependent on one vendor?
Katie Robbert 14:48
Well, and you know, there was not that long ago, a point in time where none of this existed.
And so making and this is why we really double down on making sure You have the five P’s straightened out for any given task for any given team for any given project.
It’s the process that you really need to understand.
So think back to a year ago, you know, this time last year, generative AI was just starting to become part of the conversation.
And a lot of companies were like, Oh, what is this thing, but they it wasn’t integrated into their process? What is your process look like? A year ago? Could you replicate it without one of these tools? And I don’t think that that’s a bad plan C.
So you have plan A, which is I’m going to use ChatGPT, you have Plan B, which is okay, Loma two, I could figure it out.
And Plan C is, what if I don’t have any of this? At one point in time we were doing our jobs without these things.
What does that look like? And I feel like, I mean, this is, I don’t want to get to soapbox here.
But I feel like we tend to forget, like, it’s possible to do these things with to do tasks without any of this technology.
So why is that never an option?
Christopher Penn 16:07
Well, to your point, you know, we talked about this a couple episodes ago, where companies are making some pretty rash decisions about staffing and hiring and things and saying, oh, you know, we can cut 80% of our content marketing team, because AI is going to write all our content for us now.
Katie Robbert 16:24
Yeah, it’s, and that’s exactly it.
It’s a rash.
You know, not well thought out decision.
You know, and again, these are, we’re not privy to these conversations.
This goes back to the original topic of what the heck is happening with leadership at OpenAI? You know, we, as outsiders don’t know, we can only speculate.
But it’s, I mean, it’s never a good idea to make these decisions, just like these snap decisions to replace somebody.
Because it’s not long term planning.
It’s not long term, it’s not sustainable.
So as generative AI became more consumer friendly, became more accessible.
A lot of companies were like, well, we need to cut some money from the budget.
So let’s just go ahead and invest in this software and get rid of the people that we saw that happen across the board, we’re still seeing it happen, that very short term thinking.
And now these companies that have gotten on board with this one product that may or may not be stable in a couple of weeks, are probably continuing to panic.
And now they have to make more panic moves based on their initial panic move.
Christopher Penn 17:35
Yep.
So for a company like us, because obviously we use a gazillion and a half different tools we ChatGPT is just one of many that we use.
What is your generative AI sort of roadmap in your head as the CEO look like, in the next year? Let’s assume that OpenAI basically freezes in time it stays where it is, they can keep the lights on, it’s not like it’s not going to be a case, presumably, like Twitter, where the new CEO just blows everything up.
That let’s assume the OpenAI just stays frozen in time, it doesn’t get better.
But it doesn’t get worse.
It just is.
But obviously, the rest of the industry continues to move in different directions.
And now with Microsoft acquiring like half of the team, Microsoft’s offering is probably going to get dramatically better in time.
What how do you think about our roadmap for 2024?
Katie Robbert 18:33
The first thing I do is not think about the tools themselves.
And this is where a lot of companies do their planning backwards, they think about a tool like ChatGPT.
And then they base a plan around it.
And really they should be starting with what is our purpose? What is what is the problem we’re trying to solve? What is the question we’re trying to answer? And then who are the people? We need? To answer that question to solve that problem? What is the process and then factoring the tool? So for us, the first thing I’m going to look at is, what are our growth plans for 2024? What is it? What are our goals? What do we want to accomplish? How are we going to measure those goals? And then sort of like digging in layer by layer by layer until I get to well, what platforms do we need in order to execute these things? Because that should never be the first priority of your conversation.
The first priority should be, who are the people we need? And if you only need one person, if you only need one data scientist, that’s fine.
It’s an easy conversation.
It’s an easy part of the plan.
But you still need to think through Okay, what if that data scientist suddenly decides that they want to go, you know, cheap farming and no longer wants to run these, you know, tools that we have these platforms, these services, you have to have a backup plan for that and that all needs to be figured out first, and then Once you have all of those requirements, then you can say, Okay, what is the best tool for the job? You know, to sort of use a Chris Penn analogy with cooking in the kitchen? If I want to bake a cake? Okay, first I need to figure out what kind of cake am I going to bake? Is it going to be an angel food cake is going to be a chocolate cake isn’t going to be a bundt cake.
So then I need to figure out, do I have all the ingredients? Do I have, you know, eggs and flour and sugar and milk and butter and all that sort of stuff? Great.
You know, do I even have an oven and then you can start to get into the then you can be like, Okay, do I need a bundt? Pan? Do I need a whisk? You know, but you have other things to figure out first? Do I even have the time to bake a cake today? That’s probably a great question to ask.
And so as you go down that list of requirements, you see that the tools themselves are not the top priority, there’s other things you need to figure out for.
So that’s how I would be approaching it is, what the heck do we want to do next year? How do we want to get there? And then what tools do we need?
Christopher Penn 21:01
Yep.
And as the resident technologist, I think of that list, plan A, plan B, plan C? What are the different options that are available to accomplish any different tasks? If my blender breaks? Do I have a whisk? Yes, okay, it’s not optimal, but it can still get the job done.
What’s the option if you don’t have that? Well, you know, you gotta gotta do literally by hand with a spoon, you’re really not going to enjoy it, it will get, but it can be done.
But it can be done.
And so I would encourage people to look at their processes right now and say, Okay, well, where are we slotting in generative AI? And then, to your point? Is that, do we still have the people to do that process? Or do we fire half the team, right, in which case, your your plan C is not an option, because you you don’t physically have the people to do what you used to do.
If you did everybody to switch over to general AI.
So you’d probably should either relook at your purpose, and say, Okay, well, if content marketing is still important to us, Well, is it? We’re not.
And then go ahead and look at those tools and say, Okay, this is, these are the different options.
And I do think it’s important that, at least for someone, like a CIO or a CTO, and maybe a CEO, to look at the landscape, and try and get a bigger picture perspective of the landscape, what are all the options that are out there? I would also be talking to your vendors, right? So if you are working with a generative AI vendor of some kind, you know, there’s a gazillion half copywriting vendors and things like that.
I would be asking them what is in your contingency plans? What is in your business continuity plans? If you know the you had a situation like Twitter, and the new CEO of OpenAI just drives the company face versus the ground.
What’s your plan vendor? Like? Do you have other models that your team is has has implemented so that you’re like, Okay, we can we can flip the switch, and users will see no interruption in service, we’ve just switched models from GPT-4, to Claude to or to llama to 70 V perennial, the 70 new parameter, model.
vendor, what’s your plan, and this is a great time of year, you know, we’re sort of in the homestretch of 2023.
This is a great time of year to reach out to those vendors and say, What is your plan because as renewals come up for vendors with services, you can say like, I want to, I need to see your plan for business continuity for the infrastructure you’re built on.
Because if you don’t have a plan B, we can’t necessarily trust our business with you, at least if it’s a task that we’re working on, is mission critical.
Katie Robbert 23:40
I think that’s a really smart approach.
Because we, as the buyers, are the renters of the software’s forget that we have control over the conversation.
And we can ask for that stuff.
You know, I would expect some of our clients to ask us like, hey, we know you use generative AI? Can you tell us what systems you use? Can you tell us, you know, what that looks like for us next year, but those are reasonable questions.
And if you run into a vendor or you know, a agency that you work with, who isn’t willing to answer those questions, or doesn’t No, huge red flag, go fine.
You know, maybe fine Trust Insights.
We are an open book, we are transparent.
TrustInsights.ai AI slash API services.
But that’s exactly it.
Chris, you know, we’re we’re in this precarious, unstable situation right now where everything may be fine.
Maybe nothing changes for the end user.
Or maybe everything changes.
We don’t know.
And so this is when we should be thinking about what happens if plan A is no longer available.
Is Plan B viable? Or do we need to go to plan C, whereas we pretend that none of this technology existed in the first place.
And we go back to doing things the way that we did and keep finding efficiencies there.
Yep, yep.
But you have to know what those are.
Christopher Penn 25:15
Exactly.
And it’s kind of goes back to what you were saying earlier about analytics.
One of the things that we’ve done for years is have self hosted versions of things, it is more work, it requires a higher level of technical skill, it is, is more overhead, it’s more costly to do that, to run those systems in parallel.
But when Plan A goes out the window, Plan B is already up and running.
So the this there’s no scramble to say, Oh, my God, we have to we have to totally start from scratch.
What do we do? Like? No, just let’s go look at plant.
Let’s go look at Plan B, the the side infrastructure is running in parallel? Is it as good now? Is it good enough? Yes.
With the AI stuff, yeah, a lot of the things that are on this list here, there are there are open source options that you know, we run, for example, creating images, you don’t have to use either Bing or ChatGPT, you can download and run Stable Diffusion, it is a pain to set up, right.
And it is not friendly to get it up and running.
But you can and you can’t.
And then that model as it is today, in whatever instance you choose to put it in, is yours.
And even if every other company goes out of business, you keep that capability.
The same is true for you with a tool like LM studio and the llama model.
Even if Microsoft and Google and meta and OpenAI all vanish tomorrow, that runs on my laptop that can run on your laptop, it is yours, and no one can take it away from you.
So having, you know, kind of what you say, going back to the five Ps, if you’ve got the people with the skills, they can help build resilient platform plans that will keep you running, you may not be able to take you know, you may not see massive leaps ahead.
But you will see a preservation of the capabilities that you have.
Well,
Katie Robbert 27:09
and you know, again, the Plan C is every single one of those things on that list.
You do not need generative AI to do, yes, it takes longer.
But we’ve been doing it this way for a long time without the assistance of generative AI.
So, you know, do you need generative AI to read and write text? No.
for reading and writing text? Over 1000 words, no.
For creating images? No.
for analyzing images? No.
For real time information? No.
for analyzing data.
No.
For writing code? No.
Does it take longer? Yes.
Do you have to find people with the right skills and build out more robust processes? Yes, but it can be done.
And so for a lot of people, for a lot of companies, that is going to have to be the fallback plan.
While all of these companies figure out what the heck they’re doing with their leadership and their products.
Christopher Penn 28:06
Exactly.
So what I would say to wrap up is, as we all pay attention to the news, and as you head into the holiday season, and perhaps as long as you don’t work in retail, your business gets a little bit slower in December, it’s a great time of year to build those continuity, business continuity plans to build what your AI backup plan is, and to to publish it internally so that all your stakeholders know no matter what happens in the crazy, you know, landscape, your business is covered.
And if you’re unclear about how to do that, hit us up go to TrustInsights.ai AI slash AI services.
If you’ve got some thoughts about what’s going on in AI and you want to share them, hop on over to our free slack group go to TrustInsights.ai AI slash analytics for marketers where you have over 3000 other marketers are asking and answering each other’s questions every single day.
And wherever it is you watch or listen to the show.
If there’s a challenge you’d rather have it on instead, go to trust insights.ai/ti podcast and while you’re on your channel of choice, please leave us a rating and a review.
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Thanks for tuning in.
I will talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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