Leading with Love (and Logic) in the Age of AI: Insights from Tim Sanders

Scott Kinka (00:01.271)

By 2030, AI is predicted to add up to 15 trillion, trillion with a T to the global economy. But how many companies will actually capture that value? My guest today says that the winners won’t be the ones who automate the fastest. They’ll be the ones who lead with love, strategy, and importantly, optionality. Welcome to the Bridgecast. Today, we’re joined by Tim Sanders, AI Evangelist, VP of Research Insights at G2.

and bestselling author of many books, but among them, one of my personal favorites, Love is the Killer app. We will make sure to get into that. We’re exploring why time is the new currency, why AI is perhaps just another leadership challenge, and how do we lead humanely in this changing hybrid world. Tim Litt Innovation at Yahoo, at Upwork, and now G2. He’s a bestselling author, as we mentioned, executive fellow at Harvard Business School. Lots of chops on this guy right here.

He is helping businesses navigate digital transformation and lead with love. His philosophy, Tim, you’re going to tell me if I get this right. Technology may change the rules of the game, but leadership rooted in love and strategy will win it. I love that. So Tim, before I get into this impressive background and your prolific writings, how do we do some, you know, summoning up your business philosophy there?

Tim Sanders (01:21.218)

That’s it. Perfect, Scott. Thanks for having me.

Tim Sanders (01:31.374)

It sounds great. ChatGPT could not have done it better. Even 4.5.

Scott Kinka (01:35.199)

And we did it that way on purpose. I love it. Well, Tim, we shared your impressive professional bio, but tell us about you just personally, something that I can’t get from chat, GPT, something beyond the resume.

Tim Sanders (01:50.07)

Well, I’m a musician. I obsess about analog synthesis. have to my direct right, a series of modules and patch cords and other analog gizmos, including a TAC reel to reel recorder, all of which is literally the exact opposite of artificial intelligence digital technology. So there’s an analog side to me that is kind of the yin to my yang.

Scott Kinka (02:20.129)

You know, when I was going back and refreshing myself on your history, you know, what you may not know is I had a very similar path into technology. I lived out of my own vans as well. And I’m a keyboard, I’m really a piano player, not a keyboard player. So a little bit less on the analog side. Well, as analog as it gets, there’s not even patch chords in my world. But yeah, right. Go figure all that out. But I found it super interesting that you started that way. Before we jump into the tech.

Tim Sanders (02:34.679)

Okay.

Tim Sanders (02:39.214)

You’re right. Yeah. Strings.

 (02:48.727)Scott Kinka

Pull something out of music that has a great corollary into your current job in technology.

Tim Sanders (02:55.532)

Rick Rubin. I mean, when you think about Rick Rubin, his career was about two things, taste and confidence in his taste. In a recent interview on 60 Minutes, he said, that’s proven fruitful for many of the artists I’ve worked for. So throughout my career and as I thought about music and now as I think about my relationship with artificial intelligence, it’s like we live in a world of Rick Rubin’s where you may not know necessarily the music

theory or even the engineering techniques. But if you have that sense of taste, that quality of judgment, and then the confidence to act on it, you can do anything. And so that’s how I think.

Scott Kinka (03:39.915)

You know what you like, I guess, at the end of the day. I’m reading one of Rick’s books and it’s great because that’s sort of compartmentalized into little stories and that’s one of them, which is like, you don’t have to do it right, you have to do it feels right in a lot of ways. Your taste is personal and people will resonate with that. At the end of the day, I’m

Tim Sanders (03:48.877)

Yeah.

Tim Sanders (03:54.861)

Right.

Tim Sanders (04:00.716)

Yeah. And the other thing I think about too is there’s a question of alignment. And I’m not trying to talk about alignment like, know, alignment in the AI sense. But when we think about judgment and taste, especially in a business environment, we think about like, what’s judgment in alignment to say the operating plan? What’s judgment in alignment to the corporate values that create that operating system that makes an organization behave in a predictable manner?

Scott Kinka (04:07.425)

Yeah.

Tim Sanders (04:28.94)

What does the alignment say between our judgment and taste and say the brand Bible at an organization which creates all those heuristics for customers and stakeholders? So alignment’s a big part of it too when you enter a social contract working with other people. Now for the solopreneur, pure taste, no breaks. But for me as a corporate career guy, alignment’s still a big deal.

Scott Kinka (04:57.271)

And we’ll probably explore a little bit of that alignment as we get little bit further into here. I’ve got a ton of questions. Let’s just round out your career at G2 now. Tell us a little bit more about the role and what you’re doing on a day-to-day basis.

Tim Sanders (05:05.869)

Yes.

Tim Sanders (05:09.71)

I study AI and then I object to everything else I have to do besides studying AI.

Scott Kinka (05:15.051)

Hahaha

Tim Sanders (05:16.972)

Okay, okay. That’s the whiny answer. The answer is I lead a team, Research Insights. produce a series of reports. We also do a lot of executive briefings with companies of all sizes about category trends, market trends, technology trends, et cetera. So my team covers a variety of disciplines. You could imagine marketing technology, sales technology,

Scott Kinka (05:18.209)

Perfect description. Yep.

Tim Sanders (05:45.524)

agents, which is a fascination of mine these days. So that’s the work I do at G2. And at the Digital Data Design Institute at Harvard, as a fellow, I advise such projects as their executive workshop, which I think is a fantastic program, as well as helping them with their charter to democratize AI for business people of all walks of life.

Scott Kinka (06:10.039)

Could you define that? Democratizing AI?

Tim Sanders (06:12.066)

Yeah, take the complex, make it simple, make it accessible through the use of things like storytelling or the right mental constructs or analogies, help a business person relate to artificial intelligence, just like they’ve related to every other development throughout their career. I think democratization for me has started to crystallize around a very singular mission. Solve trust gaps. Now, Scott, I think that

Scott Kinka (06:39.201)

Yeah.

Tim Sanders (06:41.452)

You know, your career, you’ve seen trust gaps. And what I mean by a trust gap is if you look at it like a chart, there’s the reliability curve of where technology becomes more and more and more commercially reliable. And there’s an adoption curve where leaders begin to integrate this reliable technology in their business in a strategic way. Here’s the reality. We trust humans more than we trust technology. Take cloud computing.

By my own research, which compiles a lot of data and uses inference, I would estimate that the trust gap measured in deadweight loss, dollars, was around 40 billion plus between the years of 2006 and 2020. And it’s simply because we just have distrust that keeps us from utilizing things other than people. So democratization isn’t just

saying, well, people understand it, people have access to it. That’s not good enough. We’ve got to help people understand it enough to try it enough, to trust it enough to win. And I think about two companies, and I think about cloud computing. People always say, me an example of a trust gap. I got it. Blockbuster versus Netflix round two.

Scott Kinka (08:04.001)

Hahaha

Tim Sanders (08:04.878)

Now everybody knows about round one. Round one was back at the turn of the century, right? Netflix CDs by mail, Blockbuster takes a few years to come, but Blockbuster by mail, that’s over. That wasn’t the end game. The end game was about six years later with video on demand. You see Reed Hastings at Netflix was a very early champion of cloud computing. Whereas Blockbuster was incredibly tepid about its adoption. They hardly stuck their toe in the water right up until the day of 2008.

Scott Kinka (08:07.327)

Yeah.

Tim Sanders (08:34.552)

when the Great Recession kind of ended it for them all in terms of raising capital. Netflix market cap today to me is as much of a signal of its ability to solve at the leadership level trust gaps around cloud as anything else. So I think that trust gaps are incredibly important in the era of AI, especially with AI agents where you have to trust them enough for them to be mostly autonomous enough.

to truly give you the scale and velocity advantage you can possibly get. That’s why I think we’re gonna see a generational disruption going on where Gen Z startups that have infinite trust in AI agents may be doing laps around the enterprise incumbents they compete with unless we democratize artificial intelligence for business people in all walks of

Scott Kinka (09:25.943)

I’m going to explore that topic around AI in a minute, but I want to just close out quickly on here because we just listened to you explain a very complex topic and make it simple. So I think I know the answer to the question, but I ask all of my guests. You’ve been at this, you’ve got a distinguished career, author, consultant, executive. What’s your superpower? Is it just in what you said now making the complex simple?

Tim Sanders (09:48.046)

Pattern recognition up to now, I say up to now, has definitely been something that’s served me very well. My ability to consume vast quantities of data than to make some powerful associations in a way that can produce an aha moment first for me and then for people as I share it has been up till now my superpower. Obviously that’s going to need to change. Pattern recognition is quickly getting commoditized.

Scott Kinka (09:52.727)

Okay, fair.

Tim Sanders (10:14.912)

I think the other thing that will come behind that, that I feel still has legs is what I like to call goaling. And that’s me making up a word goaling. What does that mean? My ability to create and define the right goal and then very clearly explain it to another person or a machine in a way that enables them to do the outcomes. Right? So goaling is like a superpower for human beings that will be valuable five, 10, probably 15 years from

Scott Kinka (10:37.623)

Gotcha.

Scott Kinka (10:44.919)

So I guess that brings you to the next point on the goal. Like you mentioned the Gen Z companies that will lead probably the next round of growth in corporate America right now. You’ve also mentioned that AI multiple times and you and I spoke a couple of weeks ago, AI is also becoming quickly commoditized. It’s really a matter of what we do with it. what does that really mean for companies that maybe aren’t Gen Z led? I how do they differentiate in the next 12 to 18 months?

Tim Sanders (10:53.944)

Yeah. Yeah.

Tim Sanders (11:12.61)

You know, I think that every single leader, especially at incumbents or especially enterprise organizations, needs to become very paranoid that they aren’t leaning in hard enough. What do I mean by that? They aren’t embarking on two things, deliberate practice and deliberate study. I’ve been through these eras before, nothing like AI. There’s never been anything like AI, but I’ve been through some stuff before,

Scott Kinka (11:26.154)

Mm-hmm.

Tim Sanders (11:39.15)

Early on in my career, back in the day when I was one of many personal assistants to Dr. Edward Deming during the quality revolution, for those of you on the call, many of you might not have heard of the quality revolution. This was a big deal back in the 1980s. That’s when statistics came to manufacturing and they designed variants out of production instead of just using inspection at the end of the process. was revolutionary, first for the Japanese and then for everyone.

Scott Kinka (11:58.678)

Yeah.

Tim Sanders (12:08.46)

That required incredible transformation. I was kind of there on the ground floor to see it and I really understood it. And I realized that the more people studied things like Six Sigma and deliberately practice using things like fishbone root cause analysis or flow charts, the more they trusted it, the more they adopted it, and the more they became black belt Six Sigma. I saw it all over again with WWW one back when I was working.

Scott Kinka (12:35.679)

Mm-hmm.

Tim Sanders (12:36.172)

Mark Cuban and then later Yahoo. Do we trust the internet enough to go to e-commerce or do we just like Barnes and Noble tack it onto our business but never really lean in, right? So I’ve seen this again with artificial intelligence. It doesn’t matter which generation you’re with with respect to what you can do. I’m just saying Gen Z doesn’t know any better. We know better and that can get in our way. So what’s the solution? I tell people that have

Scott Kinka (12:45.43)

Yeah.

Tim Sanders (13:06.094)

10, 20, 30 years of business experience, dedicate four hours a week to becoming a student. What does that mean? Well, I want you to think about reading and studying two hours a week purposefully, whether it’s reading one of the many books that I could recommend to you to really ground yourself on the disruptive economics of artificial intelligence, how trends matter to your business and your markets. could be podcasts like the Bridgecast.

It could be reading select papers and articles from what I call your mastermind group. But if you put a couple of hours a week into study and a couple of hours a week into deliberate practice against that 10,000 hour rule, that familiarity will cause you to trust it enough to lean in, try it, and make it your company’s superpower. But you can’t just have meetings about it. You can’t just, you mentioned before you play piano, okay?

Scott Kinka (13:55.852)

Yeah.

Tim Sanders (13:59.604)

You didn’t learn to play piano by putting on vinyl records of piano masters and listening. You learned by taking lessons and putting your hands on the damn keys. So I’m in my 60s. I’ll be transparent. know, AI should be a thing that I should be afraid of if you think about it. Well, maybe not because I’ve been in tech. But my point is generationally, I challenged myself three or four years ago to go back to school, take MIT’s executive education program at Sloan.

CSAIL on machine learning and NLP. You know what, Scott, somebody asked me, what’s the last book that made you cry? It definitely was Introduction to Natural Language Processing. So you can go back to school and you can become more deliberate instead of just getting briefs and updates from your team on what’s going on with AI. And I think it’ll change the game for you and it levels the playing field. I’m seeing people even older than me leaning in harder than me.

Scott Kinka (14:41.943)

You

Tim Sanders (14:59.266)

because they did exactly what I just said. They invested the time and much like going to the gym, they built the muscle.

Scott Kinka (15:06.295)

You’ve been, it’s interesting. Sometimes I feel a little bit when I’m speaking with IT leaders that AI is sort of the solution looking for a problem. You I mean, you mentioned, you mentioned some of those earlier waves that we went through. I mean, you know, at BridgePoint who sponsors the Bridgecast here, you know, we’re spending time, we’re still repatriating cloud workloads back to where they belong because the boards that go get cloud 10 years ago. I think actually when we spoke a couple of weeks ago,

Tim Sanders (15:13.218)

Yeah, yeah, me too.

Tim Sanders (15:27.917)

Right?

Yeah.

Scott Kinka (15:33.375)

I may have mentioned to you, I’m constantly telling folks I meet with, in the era of get some again, at least as it relates to AI. How do companies avoid that? mean, we’re talking about getting, building up some knowledge around this by practice, but there’s also pressure coming from boards of directors and CEOs to go figure out AI without necessarily maybe understanding where to apply.

Tim Sanders (15:38.68)

Yeah. Yeah.

Tim Sanders (15:52.174)

Yeah. Yeah.

Scott Kinka (15:57.343)

What do do if you’re an IT leader listening to this right now and you’re like, Tim, give me your words of wisdom. How do I solve this? What’s the first step?

Tim Sanders (16:05.454)

I want to give you two answers here. Let’s go after the get some, because I hear it too. Like I work a lot with private equity. By the way, if you’ve worked with private equity more than four times in the last year, you may be entitled to compensation. No, I’m just kidding. So I get this question a lot. We’ve to get some. And I say, listen, you need to separate the concept of workflows and systems. Systems is a different approach altogether, which I’ll get to in a second.

Scott Kinka (16:06.761)

OK? Yeah.

Scott Kinka (16:17.451)

Hahaha.

Scott Kinka (16:27.03)

Yeah.

Tim Sanders (16:31.692)

Workflows, get some. What does that mean? Make a commitment for every employee in your organization to have enterprise sanction access and enablement to a tool that uses an underlying large language model and teach them the habit of task consideration. Meaning every time they are in task mode, consider the language model solution and learn the concept of prompting. Learn the concept of working with the reasoning model.

learn the concept of iteration, and most importantly, and as you work with reasoning models more and more, learn how to operate simply upon expressing a goal in natural language. That’s the best get some any organization can possibly embark on because you’ll begin to understand the inherent limitations of the technology, and through that lens, you’ll also understand where it fits within our workflows.

Because when I think about AI, the revolution is more in individual workflows than it is enterprise systems. Now let’s talk about systems for a second. When it comes to thinking about production systems inside an enterprise, my advice is always you start with the business problem, you work backwards. For the businesses, what they have to do is

Scott Kinka (17:36.417)

Totally.

Scott Kinka (17:49.11)

Mm-hmm.

Tim Sanders (17:52.814)

nice root cause analysis. It could be the fishbone diagram. I confess I still use those to this day. And I think the most important step one for any enterprise leader is learning how to reframe business challenges, dependencies, how to reframe those as prediction problems, not logistics problems, not information problems, not market position problems. The reason why this is important is because

Scott Kinka (18:15.179)

Yeah.

Tim Sanders (18:22.508)

Recent history is completely populated by misunderstandings of a logistic problem being a prediction problem. And let me give you an example. In the United Kingdom years ago, maybe a decade ago, there was a real crisis in mobility, meaning it was really hard to get a London cab at Piccadilly Square to make it in time for Heathrow to get to your flight. And that’s because there just weren’t enough available drivers on queue.

Scott Kinka (18:31.991)

Sure.

Scott Kinka (18:44.503)

Yeah.

Tim Sanders (18:52.236)

London Cab would have told you, this is a logistical problem. We don’t have enough cars and we certainly don’t have enough drivers. Does that sound familiar to most companies where they say, we don’t have enough capital equipment and even if we did, we don’t have enough talent in the pipeline. And then what happened? Well, what happened was Waze, W-A-Z-E comes out and decouples prediction from judgment in the world of driving and.

Scott Kinka (19:02.113)

Mm-hmm.

Scott Kinka (19:08.033)

Yeah.

Scott Kinka (19:15.415)

Thank

Tim Sanders (19:19.54)

Uber comes on the scene in the United Kingdom, and all of a sudden, in the first year alone, they tripled the number of available drivers in cars. Today, it’s 1,000 % higher than what it would have been with only cab companies. Now, what’s the point here? To be a driver for London cab, and some of you might know this that are watching, you have to go to school. It’s called the knowledge.

and it takes three to four years. Used to take four years, now you can expedite it in three years. So you can do what? Learn how to predict every possible route and every turn to get your passenger to where they want to be. But that’s been eliminated with AI. What does it mean? Mobility was a prediction problem. It wasn’t a logistical problem. So that reframes powerful. And this isn’t the first time we’ve seen it in history. When you think about the concept of taking photos, Kodak thought it was a film problem.

But digital cameras proved it was a mathematics problem. So what I see businesses doing and in my consulting, what I see unlocked in the field every week, we had one of these last week. It was so powerful. When a business leader is able to look at the number one dependency, keeping them from executing their OP1 plan, and they realized that prediction machines could eliminate the bottleneck even faster than getting more money in the bank to spend.

Scott Kinka (20:41.569)

Yeah.

Tim Sanders (20:43.446)

And once they see that aha moment, then they say, all right, now let’s go find the best technology to solve that prediction problem. And that is where I think technology plugs in the best. The problem here, the professor at J. Agarwal, co-author of my favorite books on AI, Prediction Machines, Power and Prediction, he says the problem is if you start with tech, love tech, you start with tech, they think a lot about what data they have that really drives the decision.

or they think about what platforms they either love or they’ve heavily invested in. And we kind of shoehorn the problem into that solution set. If you started the business problem, working backwards, seeing it as a prediction problem, you’re completely agnostic to the solution, which really opens everything up for you and allows you to make the best, most pragmatic decision.

Scott Kinka (21:32.343)

And therein lies the problem of sort of getting enamored with, need to get some of that technology because you’re starting on the wrong end of the equation at the end of the day, right? You started that though with a really interesting point. I mean, if there’s not a use case yet, and generally speaking, the use cases are gonna come out of the business, right? From the people that are chopping the wood. I loved your point a moment ago where you started on the people side and you said, I’m gonna put some words in your mouth, but effectively, if you’re gonna do one thing,

Tim Sanders (21:38.328)

That’s right. That’s right.

Scott Kinka (22:02.119)

focus on AI literacy in the organization at the people level.

Tim Sanders (22:04.942)

That’s right. That’s right. That’s right. Focus on changing everybody’s workflow to constantly be in task consideration to use that tool and give them audacious goals to say, I will live in my AI tool environment 10 % of my working week. It’s a simple goal, but it will revolutionize your company in terms of driving up that native skill set. Because you mentioned use case, Scott. And I hear this a lot, especially from

Scott Kinka (22:16.524)

Yeah.

Scott Kinka (22:25.706)

Yeah.

Tim Sanders (22:32.917)

enterprise business leaders, say, well, Tim, show me one end to end use case for this AI or for agents or for whatever. And I look at them and I say, let me tell you something. There is no end to end use case in the world that I can think of for butter. You can’t eat a stick of butter for breakfast. Can’t even make a decent dessert with it, but tell any chef that they can’t cook with butter and they’ll go on strike. Butter makes everything better. And this is how I feel.

Scott Kinka (22:53.174)

Yeah.

Tim Sanders (23:01.28)

about generative AI tools today. We should think of them less as a silver bullet with an end-to-end use case and more as butter that makes every workflow potentially faster and more enriched for the end user. That’s a better way to think about it. Yeah, well, way tastier.

Scott Kinka (23:17.825)

and a little taste here, maybe. All right, so that brings up, I mean, that’s a great segue. Since the industrial revolution, we have been basically gauging people’s value to a business on hours worked, punch the clock, how hard are we going? mean, we have to be in for a monumental shift.

Tim Sanders (23:41.199)

yeah, we are.

Scott Kinka (23:41.257)

in the way we value humans in our businesses to be outcomes or value creation based. Like how was that going to change in your mind, the employee employer dynamic and what are we going to have to do to support people in that transition as an industry?

Tim Sanders (23:56.952)

Yeah. So there’s been a transition going on since 2020. Hmm. I wonder what happened in 2020. yes, that’s right. COVID. So when we all started to work from home and we would kind of study organizations that seemed to keep on humming, you know, like Google, what was very interesting is that organizations that were really good at leading based on outcomes instead of inputs, they won.

Scott Kinka (24:02.379)

Yeah. Yeah. Go figure. Right. Right.

Tim Sanders (24:26.84)

during COVID because remote work wasn’t disruptive. I if you managed outcomes, you didn’t have to manage by walking around, a la Tom Peters. You didn’t have to see them every day in the office. They did their work. You were good at defining an outcome, coaching to an outcome, resourcing an outcome, giving feedback on outcomes, and being ruthlessly meritocris in terms of how you led those companies. So that’s what meritocracy is. It’s about outcomes. It’s not about potential, right? So I think we started that motion

Scott Kinka (24:33.856)

Yeah.

Tim Sanders (24:56.6)

with COVID and remote work, we can finish that motion now with artificial intelligence, especially with agents. But what I mean by that is we’ve got to become very adept at eliminating what I call AAA leadership. That’s attendance, aptitude, and attitude, all of which are absolutely gameable and lead to big bloated organizations that can’t seem to get anything done, which I shan’t drop any of those names on this call.

12.971)Scott Kinka (25:

Mm-hmm.

25.623)Scott Kinka (25:

You

Tim Sanders (25:25.698)

But my point to you is that if you say it can’t be gamified, go watch an old Seinfeld rerun where George Costanza figures out that the Yankees management is watching to see who leaves the parking lot the latest. What does he do? He leaves his car in the parking lot 24 hours a day, so as if to signal, I’m working 24 hours a day. We’ve got to stop managing based on those.

very antiquated metrics of how hard are you trying, how many hours are you putting in and focus more on outcomes management. I think that’s going to be the future with AI. Nothing will disrupt that. mean, even in a world with agentic workflows, at the end of the day, I as a manager may not have any direct human reports, but I may have 200 agents reporting to me in some hierarchical construct. I’m still going to be measured based on the outcome of me and my ward.

Scott Kinka (26:14.379)

Yeah.

Scott Kinka (26:20.033)

Yeah.

Tim Sanders (26:20.11)

So I think that’s where we’re heading and I don’t think it’s a bad thing. I must say I would much rather be judged based on my outcomes versus based on my inputs because I take so much more satisfaction from accomplishing something than I do from working 100 hours a

Scott Kinka (26:38.327)

Will it take the next generation to be business leaders before we’ll get there? Or do you have any hope for guys our age being able to manage organizations to outcomes?

Tim Sanders (26:49.806)

I don’t think there’s a problem generationally for outcomes. I think it’s more of a skill set. think that once you start to develop the skill set of saying, I’m really good at accurately predicting the potential outcome for Scott and putting it right at 100%, not 120%, not at 60%, 100%. And I’m really good at that. And I’m really good at defining for Scott what the outcomes look like. Like, OK, ours. I’m really good at that.

Scott Kinka (26:54.753)

Okay.

Tim Sanders (27:16.986)

And our one-on-ones are laser focused on what you need from me to achieve your outcomes. And my problem solving across an organization is to unlock outcomes. Once I start to develop all those skill sets, I’m actually going to be quite loyal to that because outcomes have a direct line into compensation and success and shareholder value and everything else we care about. Because stock market, for example, is a voting machine.

It’s all it is, it’s voting machine. And primarily at the end of the day, in the longterm, it votes based on outcome. So I think everyone can participate in it. It’s just an up-skilling challenge.

47.447)Scott Kinka (27:

Sure.

56.907)Scott Kinka (27:

Gotcha. So you, there’s a concept that comes up throughout the things that I’ve seen you write and you’re thinking about digital transformation in general. And obviously we’re, having the AI conversation, but I want to apply it. talk about the concept of optionality all over your stuff, right? As a strategy for navigating just change in general, how to first define that a little bit for us and then sort of attach it to AI as CIOs and tech leaders are thinking about it, retooling their tech stack around.

Tim Sanders (28:05.57)

Mm-hmm. Mm-hmm.

Scott Kinka (28:26.805)

what’s happening right now. If we apply optionality to the way that we’re operating and the big ideas we’re trying to address right now.

Tim Sanders (28:34.67)

So optionality comes in a lot of flavors. Okay, so let’s talk about optionality as an organization. So I think there’s an organization of today and there’s an organization of the future. So the organization of today has less optionality the more they depend on full-time human talent as 80, 85%, 90 % of all of the work outputs of their organization.

Companies of tomorrow will have a smaller, but very effective, highly compensated core of full-time employees that will be managing an army of on-demand talents, including agents. And those organizations will be much lighter. They will be much more agile. They will have much more optionality because the one thing you can’t change quickly is talent structure. The one thing you can change quickly is strategy.

Scott Kinka (29:28.503)

Mm-hmm.

Tim Sanders (29:32.428)

So I’m seeing organizations in the future, not so much eliminating headcount because of AI, but not adding headcount that vectors directly to growth plans. In other words, you’ll be able to grow a hundred percent without adding the traditional 25 % of headcount that you normally would. You’ll get there by adding 5 % of the headcount. And what you’ll see in 10 years or a hundred million dollar ARR companies with less than 50 employees.

very happy employees with a lot of scale. That’s an example of optionality. Another example of optionality, which I’m suddenly started to really think a lot about, comes from Jeff Bezos. And I don’t know if he came up with the idea, but it’s sure where I heard about it. And it’s about this concept of like a two way door.

Scott Kinka (30:06.11)

Yeah.

Scott Kinka (30:22.967)

Okay.

Tim Sanders (30:23.928)

So a lot of times we want to create strategies where if we realize in a period of time it wasn’t the right strategy, we can go back through that door and there’s undue. And we have to ask ourselves all the time as leaders when we’re looking at things we’re considering either adding to the plan, pivots to the business model, is that a one way or is it a reversible two way door? So that becomes optionality at the strategy level. And I think that’s very important too.

The last part of optionality, I believe, has to do with decisions we make about partners and ecosystem providers. So one of the things that I’ve loved about cloud computing and SaaS from the very beginning was how much optionality it gives an organization. So that’s a classic example of decisions we make around ecosystem being optionality. I mentioned before on-demand talent. What I mean by that, contractors, freelancers, et cetera.

I was at Upwork for several years. And one of the things I learned is that you want to have optionality around highly specialized ephemeral skill sets like prompt engineers is a great one to use for today. Why would I say that? Because there won’t be prompt engineers in five years, just like there won’t be chat bots in five years, just like there’ll be no such thing as conversational AI if they figure things out. You’re like, really? Why is that the case?

Scott Kinka (31:34.551)

Okay.

Scott Kinka (31:48.715)

Yeah.

Tim Sanders (31:50.434)

The goal of AI is to solve the problem in a single shot. We call it one shot. So when you’re seeing new reasoning models like OpenAI’s O1 or DeepSeek’s R3, what you’re seeing is a model that doesn’t need you to prompt it. It needs you to give it a very clear goal and provide context, whether it’s through RAG or whatever, and it figures everything else. So for example, I wouldn’t want to hire a lot of prompt engineers at a company

Scott Kinka (32:09.579)

Yeah.

Tim Sanders (32:18.498)

because that is going to vanish in just a few years. I would likely, like we do, hire contractors that come in and teach all of us how to write prompts based on the types of models we use, whether they’re pre-chain of thought or chain of thought models, like I mentioned before. That’s an example of optionality and how you think about talent. So it comes in flavors.

Scott Kinka (32:35.425)

Yeah.

Scott Kinka (32:41.921)

I mean, it certainly does. And there’s so many talent corollaries to this. We could just stay around sort of the traditional HR effects of all of this and what’s going on and how we lead. But you’ve written quite a bit about business leadership.

Tim Sanders (32:55.082)

IT is going to be an HR department all by itself. It’s going to be exciting.

Scott Kinka (32:58.903)

It’s kind of where it’s going, right? How do you, it is certainly exciting. You, I do want to just, I had to get here because I know we’re going to run out of time soon and I have to do it. I mean, you, so I’m going to just pull up Love Is The Killer app. know for some of you, you might be tired of talking about it after all these years, I’m sure.

Tim Sanders (33:01.303)

exciting!

Tim Sanders (33:12.92)

So, no, I appreciate getting to talk about it after these years.

Scott Kinka (33:16.041)

And for me, I think I first read the book maybe right after it came out, by early 2000s, I think was the published date on this. And for those who don’t know the book and we’ll tag it in the notes, it’s, I believe, still very pertinent today. And my reread this past week was super interesting, but effectively about spending your time gaining knowledge, sharing without expectation of anything in return, and building your network through that, which as your premise states, and

frankly your career dictates that that will come back to you and it will and it has in your case come back to you. Interesting, Reed, it’s 20 years old, right? And here we are in the age of LinkedIn and social and you know, how do you feel? Have you read this book back today? How does it hold up in your mind?

Tim Sanders (33:53.896)

Mm. Mm-hmm.

Tim Sanders (34:02.584)

I read it kind of recently because I’m working on a book proposal for my next work, which will be kind of a follow-up to it. So I read it recently. some of the language, the jiggy biz love, the kind of way I talk, that’s back when I had the big sideburns and I wore sunglasses all the time like I was Bono. That guy, that guy, that’s a little dated. However, the underlying philosophy in the book, I have to say Scott’s never been more.

Scott Kinka (34:07.095)

Okay. Yeah.

Scott Kinka (34:17.879)

Good profile pic on the back. Excellent work. Yeah.

Tim Sanders (34:31.116)

And that is, if you want to be a success story, make everyone in your life success stories and trust the system. When you do something for someone, never let them pay you back. When they offer, coach them to pay it forward because you’ll never be disappointed. You’ll never know if they didn’t. And that means you’ll scale your ability to be a generous leader for your entire career. None of those things have changed. Just the ways we do it have changed, right? So,

You know, when I wrote the book, for example, knowledge was best gathered by reading entire books. So you had to read through a book and you had to make notes and then you had to take all of that time to gather long form knowledge because I believe that and I still do, that long form knowledge enriches your conversational abilities and teaching others much more than short form knowledge. Networking’s changed a lot. Networking’s when you put

Scott Kinka (35:17.303)

Mm-hmm.

Tim Sanders (35:23.896)

you know, a couple of people together because they should meet and you get out of the way with no expectations and you create magic in their lives. It’s not you as Keith Farazzi, my friend, he wrote Never Eat Alone, as he says, it’s not you walking through a party with a cocktail in one hand and stack of business cards in the other. Networking, so you can get ahead, right? So networking.

Scott Kinka (35:40.097)

Yeah. Right.

Tim Sanders (35:45.506)

In my day was done by people going to lunch together, making introductions at conferences. Later on, it was email. Today we can do networking in so many new ways, like LinkedIn you mentioned, but it’s still a critical function. Your network is still your net worth. And I believe in this age of AI, your network of relationships becomes something that technology will never disrupt. It can only enrich your ability.

to learn more, solve more, run faster and adapt even more effectively. So yeah, I think all the principles are the same today. I think however, the urgency for us to say first and foremost, I’ll gather knowledge. I’ll take every waking moment I can, which I do. I take every waking moment to gather knowledge. I did that back at the turn of the century because the internet was changing everything, Scott. It was changing e-commerce.

It was changing the way we did advertising, it was changing communications, it was changing the way we build communities. But it’s going to be a blip on the radar those days. And those things I just mentioned, compared to what’s coming with agents. It’s not even in the same category of exponential. And that means the trust gap implications we talked about earlier are even bigger.

So if the trust gap for cloud was 44 billion US a year, what do you think that trust gap is for agents? It’s going to be 10 to 100 X higher. So I believe there’s even more urgency today for leaders to say, I don’t know nearly enough about the economics of artificial intelligence, how to reframe a business challenges a prediction problem. Just understanding some of the basic building blocks of what is RPA automation?

13.719)Scott Kinka (37:

Mm-hmm.

22.294)Scott Kinka (37:

Yeah.

Tim Sanders (37:34.456)

How can I put machine learning to work in legacy systems? What is generative AI true capacity to increase velocity, say in marketing, sales, even product design? And then most importantly, how can agentic workflows 10x what we deliver for every dollar of capital we deploy? That should be a thing that keeps you up at night. I talk about this idea that we should have FONU, not FOMO.

Scott Kinka (38:02.839)

Okay.

Tim Sanders (38:04.12)

FOMO, fear of missing out, who cares about missing out? As a leader, you should have a fear of not understanding and it should be profound. All the things I just mentioned aren’t that hard to catch up on even if you’re not a technical person. One of the biggest insights I got around this was from Dr. Sadal Neely. She’s a teacher at Harvard Business School. She wrote a book called Digital Mindset and she talks about this thing called the 30 % rule of tech and it’s this.

You don’t need to know all of the technology to leverage it as a business leader. You just need to know 30 % of the technology, which is usually it’s economics, it’s application and how to reframe your business challenges with it as a considered solution. That’s the 30 % you need to know. You need to know it immediately. So, you know, love is a killer app. Preach, go get knowledge first and everything else comes next. I’m still preaching that. It’s just, you got to get more knowledge. You got to get it quicker. TikTok. And I’m not talking about the video service.

Scott Kinka (38:58.027)

love it. And to your point, no more pertinent than it is today. felt like, I felt like my reread was, was a kind of a walkthrough of the early part of my career. mean, you’re, you love your quotables in this book. And it was, it was basically, I was telling you in the pre-show, all the authors who are behind me, Gladwell and Peters and Spencer Johnson and even Ray Kurzweil, like Covey, obviously. And I bet half of the audience is nodding as I’m mentioning those names. So I have to ask you what’s on your end table right now.

Tim Sanders (39:01.934)

It’s no more pertinent.

Tim Sanders (39:16.152)

Yeah. Yeah. Yeah. Yeah.

Scott Kinka (39:27.585)

What are you reading?

Tim Sanders (39:28.728)

All right, so this is crazy. Not an AI book, but whatever. I love this book. If you haven’t read it, Better Data Visualizations, talk about, it’s for scholars, researchers, and wonks, and wonks. Okay, I’m struggling through why machines learn. my God, I should have finished trigonometry in college. It’s a fantastic book that really helps you understand this nature of machine learning, but it’s really hard.

Scott Kinka (39:31.735)

There it is.

Tim Sanders (39:56.854)

What books do I recommend? That’s the pertinent thing, right? You have to go read Power and Prediction. Read that first. Then you got to go read Co-Intelligence by Ethan Molyk. Read that second. Then third, you need to read Applied AI. It’s for business leaders. It’s a really good book. There’s another book that just came out recently. If you have any trepidations and you’re like, man, I’m so worried that AI is going to destroy the world, read Hoffman’s new book, Super Agency. What could go right?

with AI could reframe everything you think of. For those of you out there that did chat GPT images last week to make you impress all your friends, go read The World’s IC by Fifi Lee. It’s a fantastic book that helps you understand just the nature of synthetic media, which I think is one of the most important developments in the future. Synthetic pictures, synthetic video, and coming soon’s got synthetic data and synthetic audiences.

Scott Kinka (40:26.583)

Yeah.

Scott Kinka (40:39.756)

Yeah.

Scott Kinka (40:56.193)

find some of this so interesting because well first off we will get all those recommendations in the notes because that is basically the principal tenant of this book which is go get knowledge and share it so we’ll make sure that the people listening to this have all of that I will say speaking of a book that holds up

Scott Kinka (41:13.259)

If you want to get scared, here’s the singularity is near by raker. Pull that one out. it is it’s a this one is chock full of notes in the in the sidebar there.

Tim Sanders (41:17.288)

Absolutely! By the way that’s a thick book man, that’s that’s one of those books.

I you, now you got me going. Let me tell you, no, I know we’re running out of time. You want another thick book? whoa. CEO at G2 Goddard, sent me this. Nate Silver, this is the guy from 538, remember the super forecasting guy who also made it to the final table at World Series of Poker on a lark. Why? Because he’s great at something called expected value calculations, EV.

Scott Kinka (41:28.513)

Yeah, don’t do it. Your time. Yeah.

Okay.

Tim Sanders (41:53.634)

So everything I do now, I run EV. I run expected value calculations using chat GPT usually. And whether I’m buying a car and not paying for the extra warranties in the finance room or whether I’m making an investment decision on capital equipment, this book has changed my life in terms of thinking about the nature of taking calculated bets. So there you go, a bunch of books. I should be getting an affiliate.

Scott Kinka (42:11.895)

Okay.

If it changed your life, I’m writing that one down right now. Two last things before I let you go, because I know we’re running out of time. I asked this question of a lot of our guests. That’ll be fun from you. You know, this eventuality happens. Whatever dystopian future you believe in, there’s only one app remaining working on your phone at the end of all that. And you get to choose which app it is. What would be the one app you could not live without?

Tim Sanders (42:41.176)

me feel bad when I say this. It’s called Koala. It wakes me up every night when I’m snoring. It makes my watch tap really loud. It’s part of my successful 30 plus year marriage. I consider that app something I can’t live without. You asked me which app? My snoring app, okay?

Scott Kinka (42:44.907)

Okay?

Scott Kinka (42:59.041)

There, no there, you know what, might download that now as well. You are a wealth of knowledge every time because this is my morning argument with my wife.

Tim Sanders (43:02.112)

yeah. Yeah, it’s not my Slack app. Some people ask me, like, what would you do if you made $10 million and I’d be like, I would turn off Slack permanently. That’s what I do.

Scott Kinka (43:17.079)

Tim, I’m gonna, we’re right up against it. So I’m just, I’m gonna end with one thing, which I love. And I, you know, I have a, again, the principle intended of love is the killer app is that you’re gonna scribble all over this. I have turned down pages and all this, but nice smart people succeed at the end of all of this. Karen, just explain a little bit about that.

Tim Sanders (43:32.75)

That’s That’s right. Yeah, you know, people always say, nice people finish last. I say, yeah, but nice smart people succeed. People say to me, Tim, I tried everything in your book. I read, I mentored people, I made introductions. No one said thank you. It’s not working out. I said, you’re not doing it right. It’s a technique problem. Nice smart people succeed. I want you to thank every one of you watching. Think of the most

valuable, significant, to use a Covey phrase, significant people in your life. They were nice, meaning they approached the world with kindness, they assumed the best intentions. They were smart. They had a sense of acumen and taste and timing, and they knew when to make a suggestion and when to simply listen, when to offer to help, and when to let someone just have agency in their own situation.

They succeeded, didn’t they? They developed a wealth of relationships and most importantly, a sense of satisfaction at the end of their days, which is really how we think about it. know, nice people may get run over, but my smart people, watch out for them. They run the world. They really do.

Scott Kinka (44:43.243)

Tim, as a nice smart person, I thank you for the time. This has been super valuable. I was thrilled to be able to get you on the show. Hopefully we can have the opportunity to do it again. And we’re excited to hear about your next book. When that comes out, I’ll be first in line. It was an absolute pleasure talking with you today. Thank you so much. You got it. Well, that is a wrap on this episode of the Bridgecast. If today’s conversation gave you something to think about, certainly hit subscribe, leave us a review, share it with your team.

Tim Sanders (45:00.076)

My pleasure too, Scott.

Scott Kinka (45:11.031)

And I think also importantly, Tim, one last question for you. Where can our listeners find out more about your work at G2, your books, your speaking engagements, how best to get a hold of you?

Tim Sanders (45:20.462)

TimSanders.com, come get it. Or follow me on LinkedIn, just put in Tim Sanders and G2.

Scott Kinka (45:27.42)

Fantastic. Well, thank you so much, Tim. It was great talking to you.

Tim Sanders (45:30.51)

Thank you.

Scott Kinka (45:32.491)

All right, well that was it, man.