Funnel Reboot

Artificial General Intelligence is a term that most of us have heard, a good number of us know how its defined, and some claim to know what it will mean for the average marketer. Here's what OpenAI's Sam Altman said "It will mean that 95% of what marketers use agencies, strategists, and creative professionals for today will easily, nearly instantly and at almost no cost be handled by the AI."

What nobody knows for sure is when it will be here. Some said that GPT5 would herald the dawn of artificial general intelligence.

This episode is airing In mid-2025, and GPT5 has come out…and it is not widely believed to have AGI.

Our guest says AGI is a long way off, and more importantly, that it might not be the sought-for milestone we need for AI to be a revolutionary force in our lifetimes. Today's guest takes us through what it will take for AGI to truly arrive. We also talk about public vs private models, Mixture of Experts (MoE) models, the Branches of AI like Foundational vs generative, Agents and Agentic Workflows.

Today's guest graduated from DePaul with an MBA, has headed the AI/Analytics groups at (EY) Ernst & Young, Gartner, CSL Behring and now at the Hackett Group.

He has written several books and is here to talk about his 5th which came out in 2025.

So let's go to Chicago now to speak about "The Path to AGI" with its author. Let's welcome back for the 4th time on this show, more times than anyone else, John Thompson.

Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode.

Hey, Glenn here. It's the middle of summer when I'm recording this; a time we don a pair of shades, a beach towel and a good book. Funnel Reboot usually shares talks with marketing book authors, but for this show I'm going to share some reads that go a little farther afield.

Come along with me through six books that are all amazing. The subjects range between business, humanities, technology and science fiction.

Chapter Timestamps

0:00:00 Intro

00:01:44 The Discoverers

00:12:43 Blindsight

00:19:05 How Big Things Get Done

00:24:12 Private Truths, Public Lies

00:27:41 Seveneves

00:30:01 Other SF recommendations

00:31:00 Earth Abides

00:34:57 conclusion

Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode, #214.

Sometimes, to reach a solution, we must take unfamiliar paths.

In the early 1940s, a brilliant mathematician named Abraham Wald left his homeland in Hungary fleeing the spectre of war. He moved to the United States, and became part of a team at Columbia University tasked in 1942 with an aspect of the war where the Allies were losing badly to the Nazis. It involved the many Allied planes that would leave from England but never return to their bases, having been shot down somewhere over Europe. These B‑17 and B‑24 bombers had 10-man crews, weighed up to 30-32 tonnes, had wingspans of 100-110 feet, and were defended by machine guns planted along the plane's entire length. Despite all this, they would lose planes every day, presumably because they'd taken enemy fire and either crashed during their campaign or as they headed back over the English Channel.

Wald's team had to determine how to minimize bomber losses. They had been poring over aircraft returning from missions, mapping out the distribution of bullet holes across their fuselages. Their plan seemed logical — reinforce the areas with the most damage. But Wald saw what others missed.

Wald realized their sample set of data represented the survivors — the aircraft that had taken hits and still managed to return safely. There were other planes they weren't examining, ones at the bottom of the channel or in occupied territory, that didn't make it back. This lack of data could be biasing them to look at the problem backward. The planes they couldn't sample could have been struck in areas that were more critical. Maybe the fact they were hit in those vulnerable spots was the reason behind them crashing and that the lack of damage in those spots on the surviving bombers simply meant they'd been lucky! the returning planes weren't the rule, they were the exception.

Having flipped the problem around, the planes received reinforcements where the damage must be catastrophic, and from them on many more B17s and B24s completed their missions, helping the allies to victory in Europe. Some people call what Wald showed intuition, but that's not what saved the allied bombers. Even though his approach seemed counterintuitive, data guided Wald to the solution.

This is Funnel Reboot, the podcast for analytically-minded marketers. Today's episode goes outside our comfort zone, showing statistical tools in the hopes we'll get a bit more comfortable using them.

Our guest today is someone who uses the same kind of critical reasoning - and statistics - to make sense of their product marketing problems. He is both someone who implements analytics tools, having configured over 500 sites, and one who posts prolifically about what he's learned. He has also taught analytics at several New York colleges, and speaks at regional MeasureCamp events. After earning his MBA from Pennsylvania Western University, he spent about 20 years in corporate analytics. Then in 2017 with the support of his wife and three daughters, he set up his own firm, Albany Analytics. Listen now as he teaches you some tools that might help in your own marketing programs.

Let's now go hear from Ateeq Ahmad.

Note: Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode.

We as consumers do a lot of things just because the people around us are doing them. For proof, look no further than some historical examples—from the 17th-century tulip bulb craze in Holland to doomsday cults and prepper movements in the lead-up to Y2K. Buying fads such as pet rocks, fidget spinners, Beanie Babies, and NFTs all show how easily prevailing thoughts influence individual behavior.

The science behind this is well understood. The evolutionary drive to fit in with our peers is very strong. When a group of people's purchases are plotted as a histogram, we always see the majority of them clumped near the centre - we see it so often we came up with a term for it - the Bell curve.

So even when people think they are expressing themselves, showing individuality by their brand choices, they are only veering slightly away from the norm.

Hey, Glenn here—welcome to Funnel Reboot. Our guest today—who I really do think has positively impacted marketers' careers—argues that marketers are just as susceptible to conformity as consumers are. We get caught up in prevailing marketing practices when doing our job, while ignoring better marketing options. That's a recipe for mediocre results.

Our guest is the author of three marketing books and the co-founder of an eight year old digital agency that has attracted clients whose annual spend ranges from thousands to millions of dollars. What does he credit for this marketing success? The time he's spent on the edges of the Bell curve - doing things that most of us view as too far outside of our comfort zone. And he says to be a better marketer, you too should reject the orthodoxy of conventional marketing.

Unorthodox is the name of his latest book, and I'm glad to welcome back for a second time, Gil Gildner.

All people, products and concepts mentioned are available on the Funnel Reboot site's shownotes page.

Most of the leading AI companies tell us how wonderful their technology will make our lives. In a recent post put out by OpenAI's head, Sam Altman called The Gentle Singularity, he says "We will figure out new things to do and new things to want...Expectations will go up, but capabilities will go up equally quickly, and we'll all get better stuff. We will build ever-more-wonderful things for each other."

Of course, these new things need to be marketed and sold. Sam has good news there too, saying: "Generally speaking, the ability for one person to get much more done in 2030 than they could in 2020 will be a striking change"

This all sounds wonderful; it's used so heavily by Silicon Valley, it's been given the title of Effective Accelerationism. It's essential thesis is that AI will cause progress all by itself. So we should just let it take over? Are we willing to bet our livelihoods on that?

Where we are here in 2025, it's a challenge to do sales and marketing work using AI. Very few know how to run entire functions with Generative AI, which is why Sam qualified his 2030 prediction by saying that "many people will figure out how to benefit from [AI]" by then. How do we unlock AI's activation in customer acquisition? How do we get out of the starters blocks?

I had the chance to moderate a panel discussion on "Gen AI Activation in Marketing & Sales" at an amazing event hosted by UC Labs and TCC Canada - please get the links to each of them in the shownotes.

The panel featured myself, Lubabah Bakht, Gary Amaral, Jim Cain, Peter MacKinnon, and Brett Serjeantson, zig zagging through everything from day-to-day challenges to legal and privacy concerns to the lack of skills barring our progress.

I count myself fortunate to not only share a panel with these experts, but for being able to call them friends.

And now, please listen to these experts on Activating generative AI in marketing and sales.

Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode.

Since July 1st, 2023, the world of web analytics has undergone a seismic shift—and if you're still reeling from the transition to Google Analytics 4, you're not alone. In this episode, we unpack what many are calling the 'Armageddon' of digital measurement. You'll hear why GA4 isn't just a new version of an old tool, but a completely different ecosystem

In human years, GA4 is still a toddler. But it is growing rapidly and some are giving it a chance to mature.

Many marketers took their licks in the forced transitioning to GA4 and there are still some raw emotions about how this tool was rolled out. But our guest says that even though change is hard, he guest believes GA4 is the change we didn't know we needed.

Our guest grew up in the New York tri-state area, which gave him two passions. The first one is hockey and watching people grow up playing the game they love - he's a lifelong Islanders fan. Working in Manhattan, he also worked a lot with numbers. Over time, he morphed from analyzing financial data to analyzing digital marketing, in tools like Google Analytics And Adobe Analytics. He built this expertise at industries giants like American Express travel and entertainment's NBC Universal. Wanting to use these skills without the constraints of being in a big corporation, he went independent and relocated to Las Vegas, where he now gives all kinds of companies insights into their analytics data.

Let's go talk to Neil Shapiro.

See Shownotes page for all people and products mentioned in the episode.

Episode 209

When it comes to initiatives humans undertake, we only need to look at a few to see how they can fail spectacularly. One example:

The iconic Sydney Opera House came from a competition won by a young Danish Architect. The board who'd commissioned him to build it was told it would be completed by 1963, but things were so chaotic and so behind schedule, he had to be fired. It is truly a marvel of design, but it's a posterchild for poor projects because it didn't open until 1973.

Another example:

Out of a desire to research high-energy particles and potentially solve the fundamental of physics, the US Government set out to build the Superconducting Super Collider (SSC). A site in Texas was chosen, but after 6 years they had only tunneled a fraction of the 88 kilometres, when the project was cancelled at a cost of $2B.

A last example:

In 1998 NASA's Mars Climate Observer travelled about 200M miles and was about to start researching the red planet. But the software setting its orbital altitude had been given imperial units instead of metric. This error in the code made it come in too steep, destroying the $328M probe.

These failures are so huge, it's bound to bring out our inner cynic. It's natural to pose questions of those leading the projects, like: "what were they thinking?"

I don't scoff at the people who headed these projects, because I experienced something in my youth that showed me how humans sabotage missions.

When I was 15 I attended a camp that took us through exercises to cultivate teamwork. I thought I knew what teamwork was; I was not prepared for what awaited.

Two twenty-something Senior Counselors named Leo & Bob were in charge of it. We left the camp which was in rural New York State and drove in a van a few hours away. The van crossed into Pennsylvania, left the highway for a sideroad, then onto a dirt road and finally to a clearing somewhere in the backwoods. It was early afternoon by the time Leo dropped us off, leaving 4 of us and Bob to calmly walk for about 30 minutes, and we stopped to relax in a clearing in the forest.

At that point, Bob stood facing us and told us about this simple exercise we were about to do. He said, 'you are stranded in a forest a few miles from a stationary van which contains food and medical provisions. You have to locate the help, which will signal its location by a horn-blast every 15 minutes until sundown. You'll succeed in your mission if you reach the van by then. He didn't tell us what would happen if we didn't.

All of this seemed doable, until Bob said one of your team is incapacitated due an injury.' and then he closed his eyes, fell to the ground, and didn't say a word. I's hard to be to say what the next couple of hours was like, as we tried to find the van, carrying this 180lb man through the brush. Suddenly, it became important to recall the way we'd come, or how to lash branches together to form a stretcher, or whom among us should decide which way we should go. Each time we heard the horn, we felt a bit more exhausted and acted a bit more panicked, knowing that the horn-blasts would stop and we'd resort to screaming in the dark. The way we interacted with each other in every way, from rational to tense to hysterical. At several points in the day, I was convinced we'd never get to the van. But by some miracle we reached the van just before sunset.

Each of us had time during the trip back to reflect on how we worked as a team. I no longer wonder why people have difficulty collaborating on projects, especially as the stakes get higher.

My guest also believes it's our fault that projects fail as they do, and she's got principles she teaches that make everyone clear on the task we're all undertaking, significantly improving odds of success.

She is founder and CEO of Spring2 Innovation, is an award-winning design thinking and innovation expert, as well as a TEDx and TEC/Vistage speaker. With over 25 years of experience, she has driven innovation in telecommunications, application development, program management, and IT, helping public and private organizations shape strategy, drive change, and launch new products and services. Let's go now to speak with Nilufer Erdebil.

Chapter Timestamps

0:00:00 Intro

00:06:38 Welcome Nilufer

00:10:16 Poor design in showers and on projects

00:20:12 customers' unspoken needs

00:25:07 PSA

00:25:40 Devoting more of our time to communicating

00:28:49 Mistakes stemming from bad Workflows

00:37:39 Is our UX as disorienting to customers as a foreign language?

00:43:12 AI's potential role

00:47:55 About Nilufer, book

Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode.

Episode 208

People resist change.

They only stop resisting when they're convinced the change is needed.

They're only convinced change is needed when they grasp the truth.

The best way to present them the truth is with data.

You might think that what works on people is a dry statistical presentation of the data in all its Indisputable, inscrutable glory.

Nope. Those avoiding change give themselves offramps by arguing about your data. History shows that to persuade people to take an action, it takes taking them through data in a way that grabs them emotionally. Some examples include:

Florence Nightingale, 1854

Al Gore, 2006

Princess Diana, 1997

Numbers prove, but a story compels.

This has so much to do with marketing. Here's why. To do what we do, our bosses / clients must be convinced in how our work is yielding results. That is the core of every story that a marketing presentation tells. Our guest is a Data Storyteller. After graduating from Massey University in 2002, she moved into data analytics. She earned a digital design degree in 2015, combining her design and analytics skills, which led her to specialize in data storytelling. In 2016, she founded Rogue Penguin, a company focused on bridging analytics and business operations.

She now leads workshops for professionals in data science, marketing, and design. And she's the author of "the data storytelling handbook"

Let's go to New Zealand to speak with Kat Greenbrook

Chapter Timestamps

0:00:00 Intro 00:05:48 Welcome Kat 00:07:45 when data storytelling is needed 00:09:00 two ways of communicating data 00:13:55 Data stories improve communication between groups 00:26:38 PSA 00:27:18 Canvas for making time stories 00:30:05 making visuals relevant to the business 00:33:19 How to present when you only have part of story 00:39:06 Conserving data-ink 00:43:00 More you show - the less you contrast 00:48:20 Getting the book or contacting Kat

Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode.

Episode 207

Those of you who know me outside of this podcast, know that if I'm doing anything that involves advertising, whether it be in a classroom or a consulting setting, I think of ads as a complicated puzzle that is never fully solved. While it may not have a predictable outcome, there are a few key principles about it that are always true.

I've picked up these lessons one at a time, either by studying competitors or through the brands that entrusted me to run their ads—sometimes through painful trial and error. The models and principles that emerge from this process become a valuable piece of baseline knowledge, allowing you to make case-by-case decisions.

However, it's hard to pass these insights along to others. They're often too abstract, and the examples become stale and dated as campaigns retire.

Does this mean anyone wanting to adopt this perspective on advertising must go through the same process I did? Not necessarily. Thanks to someone with a gift for brevity and illustration, these principles have been distilled into a book.

As I leaf through its pages, I'm delighted to see many concepts I've known given clear shape and an easy-to-remember form.

Our guest graduated from Cambridge University with a Masters of Arts. He has worked in marketing, market research and brand consultancy for 30 years. He uses imaginative visuals to bring marketing concepts to life.

He's one of the nicest authors I've had on, and he's back on this show for a third time. Let's go to England to speak with Dan White.

Timestamps/Chapters:

0:00:00 Intro

00:02:27 Welcome Dan

00:04:40 Oldest known advertisement

00:09:18 Uber's clever transit ad

00:11:15 Positive and negative impacts of ads

00:22:47 using advertising to build brand asset

00:23:49 PSA

00:30:46 Many ways ads can tell a story

00:33:19 How brain perceives messages

00:37:43 Learning about ads through metaphor

00:45:45 Getting the book or contacting Dan

For links to the people, products or concepts mentioned in the show, head to episode 207's shownotes page on the Funnel Reboot website.

Episode 206

There's no denying that ChatGPT and other GenerativeAI's do amazing things.

Extrapolating how far they've come in 3 years, many can get carried away with thinking GenerativeAI will lead to machines reaching General and even Super Intelligence. We're impressed by how clever they sound, and we're tempted to believe that they'll chew through problems just like the most expert humans do.

But according to many AI experts, this isn't what's going to happen.

The difference between what GenerativeAI can do and what humans can do is actually quite stark. Everything that it gives you has to be proofed and fact-checked.

The reason why is embedded in how they work. It uses a LLM to crawl the vast repository of human writing and multimedia on the web. It gobbles them up and chops them all up until they're word salad. When you give it a prompt, it measures what words it's usually seen accompanying your words, then spits back what usually comes next in those sequences. The output IS very impressive, so impressive that when one of these was being tested in 2022 by a Google Engineer with a Masters in Computer Science named Blake Lemoine, became convinced that he was talking with an intelligence that he characterized as having sentience. He spoke to Newsweek about it, saying:

"During my conversations with the chatbot, some of which I published on my blog, I came to the conclusion that the AI could be sentient due to the emotions that it expressed reliably and in the right context. It wasn't just spouting words."

All the same, GenerativeAI shouldn't be confused with what humans do. Take a published scientific article written by a human. How they would have started is not by hammering their keyboard until all the words came out, they likely started by asking a "what if", building a hypothesis that makes inferences about something, and they would have chained this together with reasoning by others, leading to experimentation, which proved/disproved the original thought. The output of all that is what's written in the article. Although GenerativeAI seems smart, you would too if you skipped all the cognitive steps that had happened prior to the finished work.

This doesn't mean General Artificial Intelligence is doomed. It means there's more than one branch of AI - each is good at solving different kinds of problems. One branch called Causal AI doesn't just look for patterns, but instead figures out what causes things to happen by building a model of something in the real world. That distinguishes it from GenerativeAI, and it's what enables this type of AI to recommend decisions that rival the smartest humans. The types of decisions extend into business areas like marketing, making things run more efficiently, and delivering more value and ROI.

My guest is the Global Head of AI at (EY) Ernst & Young, having also been an analytics executive at Gartner and CSL Behring and graduating from DePaul with an MBA.

He has written five books. His 2024 book is about the branch of AI technology we don't hear very much about, Causal AI. So let's go to Chicago now to speak with John Thompson.

Chapter Timestamps

0:00:00 Intro

00:04:36 Welcome John

00:09:05 drawbacks with current Generative AI

00:16:09 problems causal AI is a good fit for

00:22:47 Way Generative AI can help with causal

00:26:50 PSA

00:28:08 How DAGs help in modeling

00:38:36 what is Causal Discovery

00:47:52 contacting John; checking out his books

Links to everything mentioned in the show are on the Funnel Reboot site's page for this episode.