Category Archives: Technology

The Slowing EV Market

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The Slowing EV Market

GUEST POST from Geoffrey Moore

One initiative that is top-of-mind for curtailing climate change is the electrification of ground transportation. The meteoric rise of Tesla drew the world’s attention to EVs, and China’s fast-follower public-private partnership has taken the industry to a whole new level. But now it is encountering a lull, and that raises the question, where do we go from here?

A couple of years ago LG announced a breakthrough in a battery-manufacturing technology called dry-coating that is expected to lower cost from 17 to 30%, with expected deployment in 2028. In a steady-state market, this would be welcome news indeed, but for a market that is still developing, it is not the sort of risk-adjusted return on investment (ROI) nor the kind of rate of return (IRR) that will attract private equity. LG is looking to future growth in its existing battery business to reward its efforts and counting on its investors to have the patience to wait for it.

Meanwhile, in the US, the first wave of venture returns has come and gone, and the next wave depends upon enormous amounts of capital being invested in very long-term charging infrastructure projects, the kind that are normally funded by bonds. This is reminiscent of the national commitment that underwrote the interstate highway buildout in the 20th century, but it is not clear we have the political consensus to prioritize such a project.

Ironically, we do not lack for capital to fund these efforts. The past several decades of digital transformation have created enormous pools of wealth, and financial managers are anxious to put that capital to work. The problem is that, for this phase of investment, the ROI and IRR performance metrics that accompany the creation of that wealth are neither appropriate nor available.

Private capital, for better or for worse, is driven by its compensation systems. We saw this when we tried to leverage its expertise to create carbon-credits exchanges. Not surprisingly that led to a sustained gaming of the system that has generated plenty of fees but done nothing to improve the climate situation. What we need instead is a private-public partnership based on a genuine commitment to global good.

Such a partnership is possible, but only if our political leaders are committed to such values, something we should all keep in mind when we vote this fall. At present, the electoral conversation is so consumed with personalities seeking to score points with abusive rhetoric that there is little prospect for any such partnership to emerge. But we need not capitulate to that rhetoric. If the time to change course is now, then we should hold ourselves and our country accountable for doing so.

That’s what I think. What do you think?

— Image credit: Pexels

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Future Vision XPRIZE: The Top 50 Optimistic Futures

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$2.6M+ to Film a Future Worth Building — And Why You Should Vote Now

Future Vision XPRIZE: The Top 50 Optimistic Futures

by Braden Kelley and Art Inteligencia

Innovation Shows Up First as a Story

Innovation almost always shows up first as a story. Someone writes down the submarine, the moon landing, the video call — and decades later we build it. Culture dreams of a destination. Engineering finds the path.

That is the remit of the Future Vision XPRIZE: a global competition — with a $3.5M+ prize pool and a $2.6M+ grand prize path to turn a short vision into a feature film — asking creators to show an optimistic, abundant, technology-enabled future worth wanting. Not a utopia without stakes. Not dystopia as the only imagination we fund. A future where humanity faces real danger and still figures something out — or at least believes it can.

The assignment was clear: a trailer of up to three minutes, plus a treatment of up to twelve pages, depicting a compelling hopeful future. Tools were wide open — traditional filmmaking, animation, AI, hybrids. The spirit was Star Trek’s deeper bet: that humanity can evolve intellectually, ethically, and socially toward greater understanding, possibility, and purpose. Judges for the competition include voices such as Neil deGrasse Tyson, Neal Stephenson, Mira Lane, and Rod Roddenberry. Finals land live at Moonshots LIVE in Los Angeles on September 25, 2026.

Why This Competition Matters Beyond the Prize

Incentive competitions are how XPRIZE has long pulled breakthroughs into the open. Future Vision flips the lens: the breakthrough here is collective imagination. Cautionary tales still matter — they help us navigate risk. But builders also need a north star. Soft landings for technology are designed. Soft landings for culture start with stories that make a better Tuesday feel real enough to pursue.

That is why this prize can inspire more than filmmakers and futurists. When the public watches fifty short visions of progress — climate and cities, longevity and memory, oceans and robotics, first contact and consciousness — people are not only entertained. They are rehearsing what “good” might look like. They are practicing hope with craft. Innovators get a richer brief than a white paper. Citizens get permission to want a future that is not only less bad, but more human.

Key Themes Across the Top 50

From more than 2,500 submissions, Range, XPRIZE, and Google selected fifty official films. Browse them and a pattern emerges — not a single prescribed future, but a cluster of human-centered themes:

  • Connection and consciousness — how we stay human to each other when tools get intimate with mind and dream.
  • Memory, biotech, and longevity — what we inherit, repair, and refuse to treat as disposable.
  • Nature, climate, oceans, and energy — technology solving real planetary constraints, not escaping them in costume.
  • AI, robotics, and cities — intelligence and infrastructure as partners in thriving, not only engines of extraction.
  • Space and first contact — exploration as shared purpose, not only conquest theater.

The competition’s own brief says it well: technology solving problems, humanity thriving (not merely surviving), a future worth building, and stories that move people emotionally. The Top 50 are the proof that creators worldwide answered that brief with ambition.

Trailers Beat Paper Scripts — And AI Changed Who Can Compete

For decades, “vision” in innovation and entertainment often traveled as a PDF: treatments, decks, and scripts that only a small room could feel. A trailer is different. In three minutes you either earn belief or you do not. You show the stakes, the texture, the emotional residue. Creative teams get to demonstrate their thinking — world, tone, character, craft — over and above the traditional paper scripts that usually get circulated and politely ignored.

AI did not invent storytelling. It did change the cost of a compelling, professional-looking trailer. Small teams can now prototype cinematic language that once required budgets only studios could touch. That is not a guarantee of soul — fluency without contact is still decoration — but it is a genuine opening. More than 2,500 submissions is not a marketing slogan. It is evidence that the barrier to showing a future dropped, and the world answered.

Used well, AI becomes a soft landing for creation: glue and production friction absorbed so human judgment, taste, and meaning stay in the frame. Used poorly, it becomes dystopia cosplay at higher RPM. The Future Vision XPRIZE rewards the former — visions with craft and hope that still feel earned.

Pairwise Voting Is Open on the 50 Finalists

Right now the public gets a real job. The SPECS Audience Choice Award puts $100,000 behind crowd favorites — $50K for first, $30K for second, $10K for third, and $5K each for fourth and fifth — decided entirely by vote. Crowdvoting is powered in partnership with SIV.org, and the experience is built as a simple, human question: Which future do you prefer?

That pairwise framing matters. You are not ranking fifty titles in the abstract. You are choosing between living visions — side by side — the way culture actually chooses what it wants more of. Watch. Compare. Vote. On September 25, the Top 5 also screen live in Los Angeles, with the grand prize path aimed at producing the winning feature with Range Media Partners and a $2.6M+ package ($100K cash for screenplay development plus $2.5M equity investment toward production), alongside runners-up and Top 10 prizes across the wider pool.

How to Vote — and a Preview of My Top 3

You can watch the Top 50 and cast your Audience Choice votes at https://vote.futurevisionxprize.com/. It takes about a minute to start — and every pairwise choice is a small act of futurology: telling the culture which north stars deserve oxygen.

Here is a preview of my Top 3 from the fifty. These are not the only films worth your time — watch widely — but they are the three I keep returning to:

1. PROVENANCE — Chris Winterton

Themes: Memory · Biotech · Connection. In 2160, ancestral memory is inherited through craft, and a young ceramicist starts inheriting the one memory she cannot hold. Human-centered sci-fi at its best: technology intimate with identity, dignity still in the room.

Watch PROVENANCE on YouTube · Official selection page

2. Somewhere Past Midnight — Felix Roumagnac

Themes: Consciousness · Connection. Two teenagers meet inside on-demand lucid dreams and go looking for the boundless world childhood promised. A reminder that optimistic futures are not only about machines — they are about what we still long for when the tools get powerful.

Watch Somewhere Past Midnight on YouTube · Official selection page

3. Rock Rider — Christof Vonbank

Themes: Space · First Contact. A space trucker hauling a town-sized asteroid to an Earth he’s never touched finds a stowaway carrying proof we were never alone. Exploration with grit, wonder, and a future that still has room for surprise.

Watch Rock Rider on YouTube · Official selection page

Then go vote for the futures you prefer: https://vote.futurevisionxprize.com/. Learn more about the competition at futurevisionxprize.com.

Stories shape what we fund, what we build, and what we forgive ourselves for wanting. Future Vision XPRIZE is a rare chance to put public imagination on a scoreboard that still has money, stages, and makers attached. Watch the fifty. Prefer a future on purpose. Soft landings for humanity start with north stars we can feel.

I’m curious! Please SHARE YOUR TOP 3 in the comments.

Frequently Asked Questions

What is the Future Vision XPRIZE?

Future Vision XPRIZE is a global sci-fi film competition from XPRIZE (with partners including Google and Range) inviting creators to submit up to three-minute trailers of optimistic, abundant futures. The prize pool is $3.5M+, with a $2.6M+ grand prize path to develop the winning vision into a feature film.

How do you vote in the Future Vision XPRIZE Audience Choice Award?

Go to https://vote.futurevisionxprize.com/ and compare finalist visions in a pairwise “Which future do you prefer?” experience. The SPECS Audience Choice Award distributes $100,000 based entirely on public voting. You can also browse all 50 official selections at https://fvxp.moonshots.com/.

How many submissions did Future Vision XPRIZE receive?

More than 2,500 films were entered worldwide. Fifty were selected as official finalists for public voting and the path toward live finals in Los Angeles on September 25, 2026.

What themes appear in Future Vision XPRIZE finalists?

Finalist themes span AI, biotech, cities, connection, consciousness, energy, first contact, longevity, memory, nature and climate, oceans, robotics, and space — unified by the remit of technology solving problems and humanity thriving in a future worth building.

Can AI be used to create a Future Vision XPRIZE trailer?

Yes. Competition guidelines allowed any production tools — traditional filming, animation, AI, or hybrids. AI lowered the barrier to professional-looking trailers and helped enable the scale of 2,500+ submissions, while human craft and hopeful storytelling remain what the remits reward.

Image Credits: Cursor

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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The Actual Future of AI

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The Actual Future of AI

GUEST POST from Geoffrey Moore

AI is the number one topic in the boardroom this year, and everybody wants to know how it is going to play out and what that means for their enterprise. Well, it’s not as if we have never seen versions of this movie before. How did the Internet play out? The Worldwide Web? eCommerce? Cloud computing? Smartphones? Digital marketing? Ride-sharing? Streaming media?

This is the Technology Adoption Life Cycle at work, for sure, and has been the basis of my life’s work and that of many colleagues as well. That said, however, most of our time has been spent helping clients see how best to navigate the current state of that life cycle, either as a disruptor or a disruptee. That’s all well and good, but what the board really wants to know is what can we expect from the end state. Where will all this land, and what future do we need to prepare for?

To answer this question properly we need to lay out the landscape of work along two axes (shockingly, creating a 2X2 matrix, the gold standard for all consulting models). One axis discriminates between output that is differentiating, giving customers a reason to choose your offers over those of your competitors, in contrast to work that is industry standard, things every competitor is expected to deliver. The other axis discriminates on the basis of risk exposure, distinguishing between outcomes that are mission-critical where failure is not an option, in contrast to outcomes that can tolerate a certain amount of scrap or rework.

We call this the core/context framework, and when overlaid onto the future of AI, it can look something like this:

Core Context Framework Geoffrey Moore

In the context of this diagram here is how I predict the next ten years of AI will play out:

1. Stupid stuff first. One thing to which we will all testify is that there is no lack of stupid stuff encumbering the workflow of our daily lives. Wherever bureaucracy reigns, there will be an unending stream of compliance requirements, be that in the public or the private sector, and we suffer this burden because it does underpin the rule of law, albeit onerously.

Fulfilling these requirements does not take creative genius, it takes attention to detail and considerable patience. Humans are not strong on either front, but AI is, and this is where enterprises are already weighing in and will continue to do so for the rest of the decade and more.

That said, the ROI from such efforts is modest. Basically, we are automating an inefficient system, which does reduce cost, and does free up human talent to be used elsewhere. But what we find is that human talent is not as fungible as we would want, many displaced workers cannot find employment at a comparable salary, and the rest of the enterprise is not improved in any marked way. Things overall are better, but not a lot better.

2. Cool stuff in parallel. When it comes to new technology, wherever the risk barrier is low, technology enthusiasts will lean in regardless, whether that be on their own time or their employer’s. What they are doing is playing with an inefficient system, seeing what the bright new shiny object might bring to the table. This does take creative genius as well as some creative license, so once again, patience is required, but this time it is on the part of the investor or supervisor.

That said, in the domain of consumer products and services, there is the potential for blockbuster ROI here, as any number of freemium plays have demonstrated in the past. Many are short-lived, to be sure (remember ringtones?), but in their short life, they can capture an extraordinary amount of discretionary spend. And others can mature into abiding infrastructure as search, messaging, and file sharing have all demonstrated.

The key here is that these are not mission-critical and, therefore, do not attract regulatory regimes. If and when they should do so, as is happening with social media now, then their economics can turn upside down in a hurry.

3. Serious stuff takes time. This is where most of the big and abiding ROI will come from. When work is mission-critical but not differentiating, it requires considerable investment in table-stakes processes, which garner no premium in the marketplace. To be brutally honest, there is no prize for doing this work well but considerable penalties for any mistakes you make (think ransomware for a bone-chilling example).

In the 1990s, there was an Internet-enabled revolution in enterprise manufacturing that introduced global outsourcing as a high-ROI way to address mission-critical context workloads—move the work from your company, where it is context, to their company, where it is core. This was a hugely disruptive innovation, allowing the economies of China, India, and now Southeast Asia, to expand at exponential rates, creating increasingly vibrant middle classes where there were none before. However, these benefits have come with a price, both in terms of geopolitical risks, supply chain vulnerabilities, and societal downsides, all of which we are still trying to come to terms with.

Here, AI can have a major impact, not by automating an efficient system, but by completely reengineering it. This will entail on-shoring a lot of manufacturing and displacing a lot of call centers as we introduce more and more AI into these mission-critical workflows. The gains will be in faster response to changing market demands, better returns from high-variability small-volume manufacturing runs, and reduced scrap and rework. At the outset, these changes will entail a considerable amount of human-in-the-loop processing, but eventually, because AI-enabled systems need never stop learning, the human will become the biggest source of error in the system, and we will transition to full autonomy (self-driving cars will be a case study of this transition).

4. Game-changing happens when it happens. There is a reason why so many billionaires have appeared in my lifetime (I personally forgot to become one). Their fortunes are the natural outcome whenever a disruptive innovation does not simply reengineer the existing inefficient system but instead completely replaces it. Amazon Prime, Uber, Netflix, Salesforce, NVIDIA, Apple — each company categorically changed the landscape, rendering the incumbents irrelevant, redirecting the cash flows of their entire industry to flow past their doors.

These outcomes are inevitable, but they are also unpredictable. They are what puts the venture in venture capital. The good news for incumbents is that they do not happen very often, and even when they appear to be succeeding, they can still stumble and fall. That said, sooner or later there will be a reckoning, and that is when the incumbents need to activate their Transformation Zone if they are going to survive the transition.

My altruistic wish is that for AI, this time around, the target will be the public sector. Higher education, social services, healthcare, and law enforcement are all staggering under increasingly untenable demands accompanied by shrinking workforces and restricted budgets. All of them are target-rich environments for applying AI both to stupid stuff and serious stuff. We have never disrupted these sanctums in the past, each having been ruled by regulatory regimes and professional guilds that have deeply conservative roots blocking the kinds of changes that are now needed. Displacing these systems will take decades, but the ROI, both financial and social, would be truly game-changing.

That’s what I think. What do you think?

— Image credit: Gemini

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AI’s Absurd Response to an Absurd Question

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AI's Absurd Response to an Absurd Question

GUEST POST from Shep Hyken

This article answers the question: Can AI be fully trusted to handle customer service issues, and what are the limitations of relying on AI for customer support?

As I write this article, I’m at the CX Malaysia conference, where I was the opening speaker, watching Dr. Shreekant Vijaykar of COPC Inc., one of the other speakers, talk about AI and how you can’t always trust its responses.

Dr. Vijaykar said that he asked ChatGPT the following question:

“I just bought a pair of shoes from Amazon, and they messed up the order. The shoe for the right foot was for the left foot, and the shoe for the left foot was for my right foot. What should I do about this mistake?”

He then shared that the response was to contact Amazon to let them know about the mistake, and they will refund the purchase price or exchange the shoes. I didn’t believe him, so I asked ChatGPT the same question. To my surprise, here was the answer:

That’s definitely a strange mix-up – and something Amazon should fix quickly. Here’s what to do:

  • Start the Return/Replacement Process: Go to Your Orders on Amazon. Find the shoe order and click “Return or Replace Items.” Choose the reason: “Wrong item was sent” or “Item defective or not as described.” Amazon will usually offer a free return label and a replacement option.
  • Describe the Issue Clearly: In the notes, explain exactly what happened–something like: “The right shoe is shaped for the left foot and vice versa. The pair I received cannot be worn properly.” That level of clarity helps Amazon’s quality control team flag the issue for the seller or manufacturer.
  • Choose the Replacement (if you still want the shoes): If you like the shoes and they’re still available, request a replacement instead of a refund. Amazon will often ship it out before your return even arrives.

Shep Hyken ChatGPT Cartoon
I took this absurd response a step further and wrote: “Maybe there’s a better response. It’s not really a mistake. … Just put the shoes on the opposite feet and they will work just fine. Thoughts?”

ChatGPT’s response was:

“Ha! That’s a funny way to look at it, but unfortunately, that’s not quite how shoes work. Even if you can technically put them on opposite feet, they’ll be uncomfortable and could mess up your posture or cause pain after a while – especially if you wear them for long periods.”

Dr. Vijaykar’s experiment revealed an important truth about AI: It makes mistakes. ChatGPT treated two normal shoes as if they were defective, offering a detailed explanation of Amazon’s return process.

AI is powerful, but just like humans, it can make mistakes. This ridiculous exchange is why some customers fear using AI for customer support. If you use AI tools like ChatGPT, Claude, or others, you must have a way to check answers. Apparently, common sense isn’t something that can be programmed. At least, not yet!

Image Credits: Pixabay

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Is Your AI in the Zone to Win?

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Whether you are catching up, keeping up, getting ahead, and fending off an existential threat, get in the zone!

Is Your AI in the Zone to Win?

GUEST POST from Geoffrey Moore

We’re already almost three years into the modern AI era, and everyone wants to know — What are we doing with AI? The board wants to know, the sales team wants to know, the customers want to know, the analysts want to know. Heck, you want to know.

So, how do you decide?

Start with a clear-eyed assessment of where your company stands relative to its peers in your industry. Are you behind? Are you on par but need to keep up? Are you out ahead or have a chance to be so? Or, in a darker vein, is AI opening your entire industry up to disruption, putting you and your peers under existential threat? The good news is that there are playbooks for dealing with each of these situations. The caveat is that they are all different. You need to execute them, if not linearly, then at least separately. And that’s where zone management can be a big help.

Catching Up

Industries in catch-up mode with respect to implementing AI might include retail (supply chain management, contact center productivity), higher education (admissions, alumni relations), and public sector (regulatory compliance services). The wolf is not at the door, but with AI moving as fast as it is, time is not your friend, so you need to get cracking. This is a job for the Productivity Zone.

Every organization in this zone — finance, HR, marketing, purchasing, customer success, security, you name it—is a candidate for implementing some kind of AI on a low-risk, get-acquainted basis. None of these applications will be so dramatic as to disrupt normal services, but each will give your AI team hands-on experience with the latest available technology, and most should deliver enough ROI to pay for themselves. Even when they don’t, they will contribute to the “Win or Learn” kitty, and that is what catching up is all about.

The key here is to move fast and on every front. That means every organization in this zone has to participate every quarter. Conduct AI progress reviews to hold each org leader accountable for net new wins or learnings every quarter. The goal is to catch up within a year, and it is important enough to tie performance to discretionary compensation to ensure both prioritization and inspection.

Keeping Up

Industries in a keep-up model with respect to implementing AI might include insurance (underwriting, claims), management consulting (tax, audit), and public sector (tax collection). All these areas represent customer-facing processes that are currently served by humans loaded down with routine work that can be done better, faster, and cheaper by AI applications. This is a job for the Performance Zone.

The goal here is to use AI to increase the competitiveness of your established lines of business, either through materially differentiating your products or dramatically reengineering your processes to lower costs, speed response times, and improve quality. The current generation of AI technology, because it is so remarkably approachable, is ready-made to take on this work. Your job is to make sure you use it to target those opportunities where there is the most trapped value to release. These will likely be a bit more gnarly than the others, but when you are mining for gold, you have to go where the gold is.

Generating such higher returns does not come without taking risk. Good as it is, AI is still a work in progress, so you will likely be taking a human-in-the-loop approach for the foreseeable future. The good news is your workforce is expert in your business, so you have the guard rails you need already in place. The challenge is that we humans are comfortable in our established routines, and you need everyone to break out of the old ways to free your company’s future from the pull of the past. This is more of a change management problem than an AI issue, so you should have no qualms about holding the leaders of this zone accountable.

Getting Ahead

Industries with a lot of built-in trapped value represent opportunities for first-movers to get ahead of their peers by radically reengineering the way business gets done. Examples might include residential real estate (title insurance, buyer agent compensation), health care (value-based care, home care), and public sector (social services). In each case, traditional bureaucracies are at odds with where the industry needs to go next, and implementing AI applications can be highly disruptive. This is a job for the Incubation Zone.

Venture-backed start-ups are normally the fastest movers here, but they take a long time to scale. Established enterprises have the customers, the ecosystems, and the balance sheets to get to the finish line first if they can get out of their own way. That’s what the Incubation Zone is designed to do. As described at length in Zone to Win, it emulates the VC operating model without attempting to replicate its financial model. The goal is to win early market marquee customers and cross the chasm, all without any help (or hindrance) from the core business. You have all the resources you need to do this, but it requires muscles you haven’t used in a long time, so funneling one or more acquisitions into the Incubation Zone is often a good tactic.

The key challenge will come when you reach enough scale to bring the new line of business into the Performance Zone. In the best of circumstances, you can leverage the more forward-thinking elements in your partner ecosystem and customer base to create a soft landing, running both the old and new lines side by side, as Netflix did for some time with its DVD and streaming businesses. Sooner or later, however, you will have to rip off the Band-Aid and make the transition to the new path, again as Netflix did.

Fending Off an Existential Threat

At present, the existential threat posed by Generative AI and its successors is still hard to predict, but two industries that have already sensed it are media entertainment (content creation, acting) and publishing (copyright, fair use). What should their playbook be?

This is a job for the Transformation Zone. The playbook requires all four zones to fly in formation to get through a very rough patch. The Productivity Zone goes into action first, launching legal actions against the invaders and pursuing lobbying efforts to get protective legislation. This is not a long-term solution, but it does buy some much needed time.

Meanwhile, the Incubation Zone is charged with catching up to the new wave as fast as possible. The goal here is not to out-innovate the innovators. That is what Yahoo tried to do to fend off Google, and Nokia to fend off Apple. The attackers are too good at what they do, and you are playing their game. Instead, take a lesson from how Microsoft has played catch-up throughout its storied history, beginning catching WordPerfect with Word, Lotus 123 with Excel, Aldus Persuasion with PowerPoint, and moving on to the Mac GUI with Windows, Novell with Windows NT, and Netscape Navigator with Internet Explorer. Most recently, they executed the catch-up-fast playbook to head off Amazon Web Services with Azure. The key to their success is to get to “good enough, fast enough,” not to out-perform the disruptor but to keep their own existing customer base on their side, again buying time to innovate further once it is clear they are in the game to stay.

One thing you do not want to do as an established enterprise is to merge with a successful disruptor. The Time Warner AOL merger provides a cautionary lesson here. The cultures are too different, and the necessary level of mutual trust just isn’t there, so instead of running in parallel, they work at cross purposes, and the result is a tangled mess.

On the Performance Zone side, you have to keep pedaling (and peddling) the legacy line of businesses. Absent private equity, they are your only source of capital, and they are still providing value. But you have to realize that their profit margins are under direct attack and can only be defended through rear-guard actions. That is, your legacy profit pool is the current site of trapped value, and draining it is what is funding the next wave of innovation. You don’t like it, and your investors hate it, but that’s the hand you have to play.

And that brings us to the Transformation Zone proper. You are going through a transition, the intermediate stages of which are ugly, making everyone cranky, and causing rampant second-guessing of every move you make. This is where the CEO must lead with clarity, transparency, and conviction, rallying the troops, reaching out to the customer base, providing an investable narrative to the stakeholders, and reassuring the partner ecosystem. Moreover, every senior executive must unequivocally support the chosen path or else be asked to leave. When you are under existential threat, there can be no fooling around.

Summing Up

The AI tsunami is upon us, and we can expect wave after wave of disruption for the rest of this decade and the next one as well. Clearly, it offers a wealth of opportunity, but as with all waves, catching it depends on getting your timing right and finding the right angle of attack. The four playbooks outlined above have been tested over many decades within the high sector as it dealt with the disruptive impact of the microprocessor, the Internet, cloud computing, SaaS applications, smartphones, and social media. You may not be as familiar with them as you would like, but the risk of waiting on the sidelines exceeds the risk of taking the plunge, so I can only encourage you to grab your nose plugs and jump in.

That’s what I think. What do you think?

— Image credit: Gemini

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Dead Actors Society

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How AI Synthetic Likenesses, Estate Licensing, and the Experience Economy Are Disrupting the Talent Ecosystem

Dead Actors Society

GUEST POST from Art Inteligencia

I. Executive Summary & Thesis

The Paradigm Shift: Generative AI and real-time neural rendering are fundamentally decoupling an actor’s craft and visual identity from their physical body, availability, and natural lifespan. Cinema is transitioning from a discipline constrained by human logistics to an unconstrained digital canvas.

The Core Thesis: In the emerging era of AI-generated full-length feature films, the estates of deceased cultural icons—unencumbered by living human limitations, scheduling conflicts, or creative resistance—are uniquely positioned to lead the charge in licensing synthetic likenesses for entirely new cinematic roles.

The Macro Impact: This transition extends far beyond Hollywood production budgets. It represents a fundamental restructuring of the creative talent ecosystem:

  • Talent Ecosystem Disruption: Living performers will no longer compete solely against current peers, but against a century of cinematic legends performing at their peak aesthetic and charismatic influence.
  • Strategic Career Pressures: Mid-tier and emerging actors face extreme wage deflation as synthetic legacy assets provide predictable, risk-mitigated alternatives for studios.
  • Likeness Securitization: A high-yield financial marketplace—directly mirroring the multi-billion-dollar music catalog acquisition booms—will emerge to monetize, package, and trade post-mortem digital likeness rights as long-term yield assets.

II. Introduction: The Arrival of the Synthetic Cinema Era

The Friction of Change: For decades, visual effects relied on heavy post-production labor to achieve incremental milestones—de-aging an aging star for a brief flashback or rendering a digital double for high-risk stunt work. Today, generative neural rendering and real-time motion synthesis have crossed a critical threshold. We are shifting rapidly from post-production touch-ups to full-synthesis cinematic creation, where generative models can power entire lead performances across full-length feature films with hyper-realistic emotional fidelity.

The “Dead Actors Society” Phenomenon: As production costs drop and synthetic rendering capability matures, a new content category is taking shape: the deliberate, high-budget revival of iconic performers in entirely original narratives. This is not about re-editing archival footage or stitching together outtakes. This is the era of the Dead Actors Society—a dynamic marketplace where legendary figures from film history return to headline original screenplays, cross-genre experiments, and modern franchises decades after their passing.

The Human-Centered Lens: From an experience design perspective, human beings do not connect merely to high-resolution pixels; we connect to narrative resonance, archetypal familiarity, and shared cultural memory. In an increasingly fragmented media landscape, iconic stars carry immediate emotional context and built-in trust. For studios navigating rising development risks, leveraging synthetic legacy talent offers a powerful mechanism to derisk major film slates while tapping directly into deeply ingrained audience nostalgia.

III. Why Estates Will Lead the Licensing Charge

Incentive Alignment (Frictionless Talent): Unlike living performers who manage complex personal brands, physical constraints, and evolving artistic ambitions, estate management operates primarily as an intellectual property enterprise. For estate trustees, licensing a digital likeness eliminates traditional production friction: there are no onset delays, travel requirements, physical exhaustion, or behavioral liabilities. The actor becomes a predictable, high-performing digital asset capable of infinite deployment.

Algorithmic Consistency & Archetypal Clarity: Iconic stars of cinema’s Golden Age—such as Humphrey Bogart, Marilyn Monroe, or James Dean—possess clearly defined, universally understood cultural archetypes. Because their screen legacies are static, generative models can synthesize their specific charismatic signatures, vocal cadence, and emotional range with remarkable precision. Studios gain access to instant brand recognition and established storytelling shorthand that requires zero audience warm-up.

Economic Incentive for Heirs: For heirs and asset managers, passive ownership of legacy rights often faces diminishing returns over time as catalog titles recede from active streaming discovery. Transitioning static IP into active, synthetic licensing models transforms dormant archives into dynamic, high-margin revenue streams. Through royalty-per-frame or box-office participation models, estates can capture continuous commercial value across future generations of media.

IV. The Squeeze on Living Talent: Pressures and Disruption

The “Infinite Competition” Problem: Throughout cinema history, living actors competed primarily against their contemporary peers for coveted roles. In the synthetic cinema era, that competitive arena expands infinitely backward across time. Emerging and established talent will find themselves auditioning not just against current box-office leads, but against a century of screen legends preserved at their peak aesthetic, physical, and charismatic influence—available to perform on demand without fatigue or scheduling conflicts.

Bifurcation of the Acting Profession: The economic pressures of synthetic competition will restructure the performer labor market into two distinct tiers:

  • The Ultra-Elite Tier: A small upper crust of living megastars whose commercial value relies on genuine human presence, active cultural commentary, live press tours, and authentic real-world fan connections.
  • The Squeezed Middle and Entry Level: Character actors, supporting talent, and working professionals who face severe wage compression and diminishing opportunities as studios opt for cost-effective, risk-mitigated synthetic legacy models for mid-tier roles.

The Experience Value Proposition: As synthetic performances achieve technical parity with human delivery, experience design forces a critical question for creators and audiences alike: What is the intrinsic value of human vulnerability in art? While mass-market entertainment may readily accept polished synthetic performances, a premium live-action market may emerge, marketing the deliberate imperfection, unpredictability, and lived experience of authentic human performers.

V. The Financialization of Likeness: Wall Street Meets Hollywood Catalog Sales

The Music Industry Blueprint: Over the past decade, financial institutions and private equity firms created a multi-billion-dollar asset class by purchasing the publishing rights and master recordings of legendary musicians—from Bob Dylan to Bruce Springsteen. The core thesis was simple: predictable, long-term cash flows from enduring cultural IP. Synthetic cinema opens the exact same financial playbook for screen performance, transforming an actor’s visual and vocal identity into an yield-bearing financial asset.

Likeness Securitization & Valuation Models: As generative models require clean, high-density training data, an actor’s digital archive becomes quantifiable. Wall Street valuation models will price an actor’s “Synthetic Future Cash Flow” based on three core variables:

  • Training Data Quality: The depth, resolution, and emotional range captured in their historic filmography.
  • Archetypal Demand: How universally their persona maps to high-converting narrative genres.
  • Cross-Generational Longevity: The projected retention of their cultural relevance across global markets.

Pre-Mortem Rights Offloading & Likeness Royalties: Living actors will not wait for death to monetize their synthetic value. We will see performers offload their post-mortem rights—or even license mid-career synthetic clones—early in life to private equity funds for immediate lump-sum liquidity. This will give rise to complex likeness royalty structures, fractionalized ownership of synthetic talent libraries, and secondary derivative markets trading on the future performance of digital personas.

VI. Strategic Foresight: Governance, Ethics, and Experience Design Challenges

Human-Centered Change Management for Hollywood: Navigating the synthetic era requires robust governance frameworks that balance creative freedom with ethical stewardship. Labor unions like SAG-AFTRA, estate trustees, and legislative bodies will be forced to continually redefine right-of-publicity laws, digital consent boundaries, and posthumous labor rights to prevent non-consensual exploitation while enabling legitimate commercial innovation.

Audience Fatigue & Experiential Saturation: From an experience design perspective, over-relying on familiar digital ghosts carries significant narrative risk. When iconic faces become ubiquitous across cheap spin-offs, interactive media, and localized ad campaigns, “nostalgia overload” sets in. This erosion of scarcity dilutes the actor’s original cinematic legacy and risks numbing audience emotional engagement through synthetic repetition.

Authenticity vs. Convenience: As synthetic content generation accelerates, experience designers and filmmakers must intentionally craft the boundary between efficiency and artistry. The challenge will not be technical feasibility, but human resonance—ensuring that synthetic revival serves a genuine artistic purpose rather than functioning merely as a frictionless, algorithmically optimized cash grab.

VII. Conclusion: Framing the Future of Talent

Summary of the New Landscape: The arrival of synthetic feature films does not spell the end of human performance, but it marks the definitive end of its monopoly. Cinema is entering a hybrid era where living performers, purely synthetic AI-generated entities, and licensed digital revivals of historic legends co-exist within the same creative ecosystem. Success in this environment will require a fundamental shift in how studios, managers, and audiences conceptualize talent, IP, and performance art.

Call to Action for Leaders and Creators: As leaders in media, technology, and human-centered innovation, our responsibility is to guide this transition with intentionality. We must build business models and governance frameworks that honor human legacy without stifling artistic evolution. By prioritizing authenticity, ethical consent, and meaningful experience design over mere algorithmic convenience, we can ensure that synthetic cinema expands the horizons of human storytelling rather than cheapening it.

Frequently Asked Questions

Why are the estates of dead actors more likely to license AI likenesses than living actors?

Estates operate primarily as intellectual property enterprises focused on asset maximization without the physical, emotional, or ego-driven constraints of living performers. Unlike living actors, deceased legends face zero physical friction—there are no set scheduling limits, press junket obligations, physical aging, or behavioral liabilities, making them predictable, high-performing digital assets for studios seeking to derisk major film investments.

How will the rise of synthetic legacy actors impact living performers?

Living actors will no longer compete solely against current peers, but against a century of film history preserved at peak aesthetic and charismatic performance. This will likely bifurcate the talent market: an ultra-elite tier of living megastars whose value lies in authentic human presence and live connection, and a severely squeezed middle tier of character and entry-level actors facing wage compression as studios adopt cost-effective, risk-mitigated synthetic models.

Will AI actor likenesses generate a financial market similar to music catalog sales?

Yes. Just as financial institutions transformed musician song catalogs into multi-billion-dollar yield-bearing assets, Wall Street will monetize actor likenesses based on training data quality, archetypal demand, and historic box office impact. Living actors and estates will offload post-mortem rights to private equity funds for immediate liquidity, creating a robust secondary market for likeness royalties and fractionalized talent libraries.


Image Credits: Gemini

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Amazon Connect Combines Human Empathy with AI to Redefine Service

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Amazon Connect Combines Human Empathy with AI to Redefine Service

GUEST POST from Shep Hyken

Will AI replace people?

This is a question I’m often asked. In the customer service world, there are many who say AI will replace human-to-human support. It’s been predicted by the world’s most reputable consulting firms. However, executives from some of the largest and most recognizable brands on the planet have said that while AI is making for a better customer experience, people are still needed and their companies are continuing to hire.

That sentiment was recently confirmed when I interviewed Pasquale DeMaio, vice president and general manager of Amazon Connect, on Amazing Business Radio. His specific approach to AI and human-to-human customer service is summed up in two words: Better Together.

Human and AI: Better Together

Customer service works best when technology and humans come together. While AI and automation can make things faster and easier, there will be times when customers still want to talk to a live customer support agent.

The point is to automate the simple requests and questions and empower agents to manage more complex issues. DeMaio said (referring to customer service agents), “No one is enjoying a password reset, neither talking to someone about it nor listening to the request.” Let AI take care of the simple questions and requests, and let humans manage the more complex problems and emotional issues.

AI Can’t Do Empathy — That’s What People Do

DeMaio said, “People aren’t really looking for technology to form that emotional connection when they’re trying to achieve an outcome.” While AI can talk to a customer and sound like a human, the customer knows it’s just a machine. It can say, “I’m sorry,” and sound empathetic, but it’s not, and the customer knows it. Authentic empathy is a human-to-human experience.

DeMaio shares his philosophy of friendly, empathetic service. He says, “At Amazon, we actually tell people to treat the customer on the phone like they’re your friend. But what we don’t say is the person on the phone is your friend. … What’s natural is to treat them the way you would treat a friend.” And that is how empathy begins.

Customer Support Doesn’t Cost — It Pays

Traditional contact centers have focused on quick, efficient resolutions. Metrics like AHT (Average Handle Time) are efficiency measurements. The goal of handling as many calls as quickly as possible is not as effective as using customer support to not only solve customer issues but also enhance customer relationships. Once again, let AI-fueled self-service tools handle simple problems and have people (customer support agents) spend a little more time with customers to drive repeat business and loyalty. In addition, DeMaio points out that businesses should aim to understand why a customer might want to leave and proactively create positive experiences well before they escalate, to get customers to want to come back. For example, Amazon Connect’s real-time analytics empower agents to detect customer sentiment, identify at-risk relationships and take the necessary steps to save the customer.

Finding the Balance Between Technology and Human-to-Human Conversations

The balance between technology and human support will vary. However, the future of customer service is not about choosing between AI and humans. It’s about using the strengths of both to create a convenient, efficient and seamless experience. Customer service is not just about fixing. It’s about caring and building long-term, loyal relationships. DeMaio summed it up by saying, “Think about the long-term value of the customer. And then think about how you would want to be treated as a human being. And then think about how AI can help you do that better.”

This article was originally published on Forbes.com.

Image Credits: Shep Hyken

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AI Will Create a More Human Future, Not a Less Human One

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An AI Soft Landing Scenario

AI Soft Landing Scenario
by Braden Kelley and Art Inteligencia

What If the Future Gets More Human?

We spend a remarkable amount of time rehearsing the wrong ending.

In one popular story, artificial intelligence hollows out work, flattens craft, and leaves people performing the emotional leftovers of automation. That is a hard landing: humans demoted by systems that do more of everything, including the parts of work that used to make us feel useful.

There is another future available to us, what I call an AI soft landing. In that future, organizations and societies deliberately design AI to absorb fragmentation, acceleration, and low-judgment transaction. What returns to humans is not emptiness. What returns is depth: larger blocks of time for insight, empathy, decision making, direction setting, problem definition, creativity, and collaboration. The future becomes more human, not less, because human attention is finally reserved for human work.

This is not a naive techno-optimism. Soft landings are designed. Hard landings arrive when efficiency is the only value on the dashboard.

The Hidden Enemy Was Never “Work.” It Was Fragmentation.

Most knowledge work did not become less meaningful because people stopped caring. It became less meaningful because attention was diced into tickets, pings, updates, status rituals, and micro-approvals. We mistook motion for progress and responsiveness for value.

Task switching is expensive. Every context shift asks the brain to unload one problem and reload another. Multiply that by a day of chats, forms, triage, and administrative glue work, and you get a workforce that is always “on” and rarely deep. Strategic thinking does not fail only for lack of talent. It fails for lack of contiguous time.

AI’s first gift, if we use it well, is not genius on demand. It is fewer interrupted minutes. When drafting, scheduling, summarizing, searching, classifying, routing, and first-pass analysis get accelerated or handled, the calendar can stop looking like confetti. Bigger time blocks reappear. And bigger blocks are the raw material of original insight.

AI Future of Work

What Humans Should Own in a Soft Landing

A soft landing is not humans “using AI better.” It is a clear division of cognitive labor, one that protects the uniquely human contribution instead of competing with the machine on volume.

In a more human future, people spend more of their capacity on:

  • Insight development — connecting weak signals into meaning, not merely producing more output
  • Empathy — understanding stakes, dignity, and lived context that no dashboard fully captures
  • Decision making — choosing under uncertainty with values, tradeoffs, and accountability
  • Direction setting — naming where we are going and why it is worth the journey
  • Problem definition — asking better questions before rushing to automated answers
  • Creativity — combining perspectives in ways that are novel, useful, and humanly resonant
  • Collaboration — building trust, resolving conflict, and making progress together

Notice what is missing from that list: being the fastest typist in the room. Soft landing excellence is not measured in tokens per minute. It is measured in clarity per hour — and in whether people leave interactions more capable, more trusted, and more oriented than before.

From Transactional Lives to Strategic Ones

When small tasks expand to fill the day, even senior roles become transactional. Leaders spend their best hours approving instead of directing, reacting instead of sensing, facilitating meetings about work rather than doing the work of judgment.

AI can reverse that inversion, but only if organizations stop using every efficiency gain to stuff more micro-tasks into the same damaged attention budget. Saving ten minutes and immediately filling them with ten more interruptions is not transformation. It is denser exhaustion.

The soft landing asks a different operating question: What human capability do we want more of, now that machines can carry more of the glue?

If the answer is “more throughput at any cost,” you will automate people into thinner slices of busyness. If the answer is “more strategic quality, better problem framing, deeper customer and employee understanding,” AI becomes a scaffold for human depth. Less task switching. More deliberate thinking. Fewer performative updates. More real collaboration around decisions that matter.

AI Human Endeavors

How Leaders Design a Soft Landing (Instead of Hoping for One)

Human-centered change makes soft landings practical. A few design moves matter more than tool catalogs:

  1. Automate the glue, not the judgment. Route AI toward fragmentation: search, draft, summarize, schedule, classify, prepare. Keep humans responsible for choices with ethical, relational, or strategic consequence.
  2. Protect deep-work blocks as policy, not privilege. If AI creates capacity, calendar culture must not immediately reclaim it for more meetings.
  3. Redefine roles around human endeavors. Job descriptions should emphasize insight, empathy, problem definition, and direction — not inbox velocity as a proxy for value.
  4. Measure success in human outcomes. Track decision quality, customer trust, employee agency, and innovation usefulness — not only cost per interaction.
  5. Teach the craft of better questions. In an AI-rich world, problem definition becomes a core leadership skill. Bad prompts and bad frames still produce confident nonsense.
  6. Build collaboration for synthesis, not status. Use reclaimed time for cross-functional sense-making, not another dashboard review theater.

This is experience design for the future of work: design the system so people can be fully human on purpose.

The Choice Ahead

Futurology is not prediction cosplay. It is responsibility with a longer horizon.

We can use AI to compress people into ever-faster transaction machines. Or we can use it to return something modern work has been quietly stealing: the ability to think, feel, decide, and create with integrity.

The soft landing is the second path, a future where machines handle more of the small so humans can do more of the meaningful. Where strategy is less of a slide ritual and more of a practiced habit. Where customer and employee experience improve not only because algorithms personalize, but because people finally have the attention required for empathy and judgment.

A more human future will not arrive by accident. It will be designed by leaders who refuse to confuse automation with progress, and who insist that the best use of artificial intelligence is the expansion of human capacity where it still matters most.

Frequently Asked Questions

What is an AI soft landing?

An AI soft landing is a future in which artificial intelligence absorbs fragmented, transactional tasks so humans can spend more time on deeper endeavors — insight, empathy, decision making, direction setting, problem definition, creativity, and collaboration — making work more human rather than less.

How does AI reduce task switching at work?

AI can handle or accelerate small tasks such as drafting, summarizing, searching, scheduling, classifying, and routing. When organizations protect the time this frees, instead of immediately filling it with more interruptions, people gain larger blocks for strategic thinking and higher-quality collaboration.

What should leaders do to make the future more human with AI?

Leaders should automate glue work rather than human judgment, protect deep-work capacity as policy, redesign roles around human endeavors, measure human outcomes as well as efficiency, invest in better problem definition, and use reclaimed time for real collaboration and decision quality — not denser busyness.

Image Credits: Cursor

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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Why Collective Intelligence is the New Scarce Resource in an Age of Abundant AI

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The Coordination Dividend – An AI Soft Landing Scenario

Why Collective Intelligence is the New Scarce Resource in an Age of Abundant AI

by Braden Kelley and Art Inteligencia

Throughout history, every major technological revolution has fundamentally altered the landscape of scarcity. The Industrial Revolution transitioned physical labor from a precious commodity into an abundant input; the Information Age did the same for data; and the Internet democratized communication, rendering the friction of distance nearly obsolete. Today, we are witnessing the latest iteration of this pattern: Artificial Intelligence is rapidly making individual intelligence, once our most guarded and limited asset, an abundant utility.

But if intelligence is becoming commoditized, what becomes scarce next? Many leaders are still trapped in a race to build “smarter” systems, yet the evidence suggests that smarter algorithms alone will not generate the competitive advantage we seek. The real bottleneck for progress in the coming decade is no longer how smart we are, but how effectively we coordinate our human and AI systems toward shared goals.

I call this the Coordination Dividend. It is the measurable surplus value created when diverse groups of humans and autonomous agents align seamlessly, communicate with minimal friction, and operate within shared mental models. As we navigate the AI transition, the winners will not be those with the most powerful models, but those who design the best operating systems for collective intelligence. Innovation, leadership, and organizational design are no longer just about optimizing technology, they are about perfecting the human-centered architecture of our future collaboration.

Section 1: Why Intelligence Is No Longer the Bottleneck

For years, we have been conditioned to believe that the primary lever for organizational success is the acquisition and application of specialized intelligence. We hired for it, we optimized our internal processes around it, and we built our competitive moats upon it. However, we are now entering an era where expert-level reasoning, sophisticated code generation, and nuanced creative synthesis are becoming commoditized utilities, accessible to anyone with an internet connection and a subscription.

The danger in the current market environment is the pursuit of the “Solo Genius” myth — the belief that an individual, super-powered by an AI agent, will be the primary driver of value. While AI augmentation significantly boosts individual output, it does not inherently solve the challenges of friction, misalignment, or slow execution that plague most organizations. In a world where intelligence is abundant, the strategic advantage shifts from the individual to the system.

This creates a critical pivot point for leaders:

  • Moving Beyond Capability: We must stop asking “How can AI make our people smarter?” and start asking “How can we orchestrate our people and AI together to move faster?”
  • The End of the Intelligence Moat: If your organizational strategy relies solely on being the smartest player in the room, your edge will evaporate as those capabilities are integrated into foundation models.
  • The Shift to Agility: The true test of an organization is now its ability to reconfigure itself in real-time. We must transition our focus from maximizing raw intelligence to maximizing organizational agility — the capacity to pivot, integrate new tools, and align collective energy without the usual administrative drag.

When intelligence is everywhere, the most successful entities will be those that master the flow of information and intent between human operators and synthetic agents. The future belongs to those who recognize that the intelligence itself is merely the raw material; the finished product is the coordinated outcome.

The Scarcity Shift Matrix

Section 2: Anatomy of the Coordination Dividend

To capture the Coordination Dividend, we must move past the idea that AI is a tool we “use” and begin to see it as a partner we “integrate” into our operational fabric. Coordination is no longer just about human-to-human interaction; it is about establishing a high-fidelity interface between human intent and synthetic execution.

The architecture of this dividend rests on three foundational pillars:

  • Shared Mental Models: In a hybrid workforce, humans and AIs must operate from the same baseline of context. This requires a shift in how we document strategy, culture, and operational constraints. If the AI doesn’t understand the “why” behind the “what,” it will optimize for the wrong outcome. Building a shared mental model is about encoding human values and strategic intent into the persistent memory of our systems.
  • Adaptive Governance: Traditional, top-down hierarchies act as friction points that prevent the rapid exchange of information necessary for coordination. We need to transition toward fluid, purpose-driven collaboration where decision rights are clear but execution is decentralized. Governance in this new era means setting the boundaries and the goals, then empowering human-AI teams to navigate the space in between autonomously.
  • Low-Latency Feedback Loops: The speed of business is accelerating. The organizations that win will be those that have engineered out the “wait states” in their decision-making processes. By creating real-time feedback loops — where performance data is instantly processed by AI to inform the next human action — we turn planning into a continuous, iterative flow rather than a static, periodic event.

Ultimately, these pillars define the difference between an organization that is merely “using AI” and one that is “AI-coordinated.” The former will continue to struggle with siloes and misalignment, while the latter will discover the efficiency gains that come from true systemic harmony.

The Anatomy of Human-AI Orchestration

Section 3: Impact Across the Ecosystem

The Coordination Dividend is not merely an internal efficiency metric for corporate operations; it is a fundamental restructuring of how value is created across every layer of modern society. When we solve the coordination problem between human intent and synthetic intelligence, the ripple effects transform everything from enterprise strategy to civic infrastructure.

Consider how this dividend manifests across key dimensions of our economic and societal ecosystem:

  • Innovation & Product Design: The traditional innovation pipeline is notoriously clogged by friction — the delay between ideation, prototyping, testing, and scaling. In an AI-coordinated environment, teams can run hundreds of parallel experiments simultaneously. The bottleneck is no longer generating or executing ideas, but curating the highest-impact concepts and aligning multidisciplinary teams around rapid deployment.
  • Organizational Design & Culture: Traditional departmental silos are the ultimate tax on coordination. The Coordination Dividend dismantles rigid organizational charts in favor of dynamic, cross-functional “pod” structures where human domain experts, experience designers, and specialized AI agents form transient units around specific outcomes, dissolving once the goal is reached.
  • Leadership & Change Management: The role of the leader fundamentally pivots from “commander of resources” to “architect of coordination.” Tomorrow’s leaders will win not by issuing directives, but by designing the collaborative systems, guardrails, and psychological safety needed for humans and AI agents to co-create without friction or paralysis.
  • Civic Infrastructure & Public Systems: At a societal scale, the inability to coordinate remains our greatest challenge — evident in healthcare delivery, urban planning, and educational equity. When local governments and institutions leverage low-latency, AI-augmented coordination, we can optimize complex public networks (from smart traffic management to personalized learning pathways) in real time while maintaining a deeply human-centered ethos.

Across every sector, the lesson remains constant: technology supplies the velocity, but coordination supplies the vector. Without systemic alignment, speed simply leads to faster friction.

The Coordination Dividend: Ecosystem Impact

Section 4: Measuring the Dividend

If coordination is the core source of competitive advantage in an AI-abundant era, we must develop new frameworks to measure it. Traditional productivity metrics — focused on output volume, lines of code, or hours logged — are entirely obsolete when generative systems can flood an organization with synthetic artifacts in seconds. Measuring volume only incentivizes noise; we must instead measure alignment and velocity.

To quantify the Coordination Dividend, forward-looking organizations will monitor key operational indicators:

  • Coordination Friction Index: Calculating the latent delay between intent and execution. How many handoffs, approval bottlenecks, or misaligned rework cycles occur between a strategic decision and its initial market feedback?
  • Context Parity: Assessing how accurately human teams and AI agents share operational context. High context parity eliminates hallucinated priorities and ensures autonomous workflows remain tightly bound to strategic goals.
  • Adaptive Velocity: Measuring an organization’s ability to reconfigure workflows, redeploy human talent, and integrate new AI models without triggering operational paralysis or cultural burnout.

Crucially, this dividend must be rooted in human-centricity. High-tech coordination without human-centered design risks creating hyper-efficient panopticons — systems that optimize for throughput at the expense of psychological safety, creativity, and trust. The ultimate metric of a successful coordination model is whether it frees humans to focus on judgment, empathy, and strategic intuition, or simply traps them in a high-speed hamster wheel of machine management.

Measuring the Coordination Dividend

Conclusion: The New Operating System for Civilization

As we navigate the ongoing shifts of the AI transition, it is easy to become captivated by the exponential performance curves of new models and raw processing capabilities. Yet, history reminds us that technology alone is never the destination — it is merely the catalyst. Just as steam power required the invention of the factory, and the Internet required the creation of networked platforms, artificial intelligence demands a radical overhaul of our collaborative architecture.

The Coordination Dividend represents the next frontier of organizational and societal evolution. In a world of abundant intelligence, value migrates to those who can master the art and science of synthesis — uniting human empathy, judgment, and creativity with machine scale, precision, and speed. The defining challenge of the next five years will not be building smarter algorithms, but designing better systems of human-AI orchestration.

For leaders, innovators, and experience designers, the directive is clear: stop obsessing solely over AI tools, and start designing for systemic alignment. By prioritizing low-latency feedback loops, shared mental models, and human-centered governance, we can ensure that artificial intelligence does not fragment our efforts, but elevates our collective capability. Intelligence provides the raw energy for our future, but coordination is the steering system that ensures we achieve a soft landing — and build a resilient, high-performing society on the other side.

Frequently Asked Questions

What is the “Coordination Dividend”?

The Coordination Dividend is the measurable surplus value created when groups of humans and AI systems align seamlessly, communicate with minimal friction, and operate toward shared goals. As AI makes raw intelligence abundant, competitive advantage shifts from individual smarts to collective coordination speed and efficiency.

Why does intelligence cease to be the primary bottleneck in the AI era?

Generative AI democratizes access to expert reasoning, code generation, and strategic synthesis. When expert-level capability becomes a low-cost utility available to everyone, having intelligent individuals or models is no longer a distinct moat; the true bottleneck becomes how effectively an organization can connect, align, and execute across human-machine teams.

How do organizations measure and capture the Coordination Dividend?

Rather than tracking traditional volume metrics (e.g., hours logged or lines written), organizations quantify coordination by measuring the Coordination Friction Index (delay between intent and execution), Context Parity (shared context between humans and AI), and Adaptive Velocity (speed of reconfiguring workflows without burnout).


EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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Why Conversations Are the New Digital Gold

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The Big New Revenue Opportunity for Google, OpenAI and Anthropic

Why Conversations Are the New Digital Gold

by Braden Kelley and Art Inteligencia

I. Introduction: The Disruption of the Clickstream

For over two decades, the digital economy operated on a straightforward, predictable currency: the clickstream. Organizations built vast marketing engines, customer experience frameworks, and product strategy backlogs around keyword volumes, cost-per-click (CPC) bidding, and web analytics. If you could capture user search intent at the top of the funnel and guide them through a sequence of web pages, you owned the customer relationship.

That paradigm is experiencing an irreversible structural breakdown. We are witnessing a profound behavioral migration away from typing fragmented queries into a text box toward engaging in fluid, multi-turn dialogue with generative AI assistants. Whether users are speaking directly to Gemini on Android and iOS devices or consulting ChatGPT and Claude for complex decision-making, the mechanics of discovery have fundamentally changed.

The Death of “10 Blue Links”

The traditional search results page — dominated by ranked links, banner inventory, and sponsored listings — is giving way to synthesized, conversational answers. When users speak to an ambient assistant, they aren’t looking for a list of websites to evaluate independently; they are seeking a resolved outcome. Speech-to-text, natural voice interaction, and inline AI reasoning mean problem-solving happens within the dialogue itself, drastically reducing the need to visit external brand properties.

The Shrinking Digital Surface Area

This rise in zero-click interactions presents an existential challenge for traditional web analytics and performance marketing. As consumer click-through rates decline, brands face a dramatic reduction in their visible digital touchpoints:

  • Attribution Blindness: Traditional conversion tracking breaks down when the research and evaluation phases occur entirely inside an AI model’s context window.
  • Diminishing SEO Returns: Optimizing for keywords and site traffic yields shrinking returns when AI models synthesize answers directly without referring users to source URLs.
  • Loss of Direct Engagement: The digital surface area where brands can present their unique visual identity, messaging, and experience design is rapidly compressing.

The Foresight Premise

In any major technology transition, structural shifts create immediate information asymmetries. Every change initiative produces winners and losers based on who recognizes where value is re-aggregating. The primary battleground of the AI era is no longer about driving traffic to a destination — it is about controlling, understanding, and translating the rich context of human conversational intent.

II. The Blind Spot: How Brands Are Losing the Voice of the Customer

The transition from traditional web search to ambient AI interaction is creating an unprecedented intelligence blackout for commercial enterprises. For years, organizations refined their understanding of consumer behavior by tracking the digital breadcrumbs left across search engines, landing pages, and digital storefronts. As customer decision-making migrates into private, dynamic AI dialogues, that pipeline of actionable data is drying up.

This shift represents far more than a marketing disruption — it is a fundamental erosion of the qualitative feedback loops that drive modern product innovation and experience design.

From Keywords to Unfiltered Intent

Keyword search was always a compromised, low-fidelity medium. Users learned to compress their complex human needs into unnatural, fragmented phrases meant to nudge a search algorithm into producing useful links. The language of traditional search was structured around constraints rather than context.

Generative AI and voice interfaces have eliminated those constraints. When individuals speak to an assistant like Gemini, ChatGPT, or Claude, they express their needs with full nuance, nuance, and emotional framing. Consider the structural difference between these two modes of inquiry:

  • Traditional Search Query: best running shoes flat feet
  • Conversational Intent: “I’m training for my first rainy marathon in three months, but I have mild overpronation and a old knee injury. What shoes under $150 will give me enough stability without causing blisters on long runs?”

The conversational prompt contains rich layers of context: budget parameters, timeline constraints, physical vulnerabilities, weather considerations, and personal goals. However, because this interaction takes place within an AI context window rather than on a brand’s website or an open search results page, the business whose product is being evaluated receives zero visibility into the exchange.

The Customer Insight Vacuum

As consumer preference formation moves into continuous multi-turn conversations, brands are losing access to critical moments of truth across the buyer journey. This creates three severe operational blind spots:

  • Unseen Feature Trade-offs: Brands cannot see which specific product attributes, specifications, or pricing structures cause a potential customer to eliminate them from consideration during an AI dialogue.
  • Invisible Competitive Comparisons: When an AI assistant evaluates three competing solutions side-by-side for a user, the losing brands receive no signal explaining why the model recommended an alternative.
  • Obsolete Voice-of-Customer (VoC) Data: Traditional surveys, focus groups, and social listening tools capture lagging, highly filtered opinions. They fail to reflect the real-time, unvarnished friction points articulated during natural conversations with AI.

The Experience Design Risk

Without access to the rich contextual signals embedded in everyday user prompts, corporate experience design initiatives risk operating on outdated assumptions. Customer journey maps, persona frameworks, and friction-point analyses quickly become stagnant snapshots of an obsolete digital funnel.

To design meaningful, human-centered experiences, leaders must understand the authentic language and evolving expectations of their audience. When that language is spoken exclusively to third-party AI assistants, organizations that fail to secure access to conversational intelligence will find themselves innovating in the dark.

III. The Big Pivot: Monetizing Context, Not Clicks

Every major shift in technology redistributes economic value. As traditional cost-per-click advertising yields diminish under the pressure of zero-click conversational answers, the business models of the AI platform giants — Google, OpenAI, and Anthropic — must evolve. The next multi-billion-dollar monetization opportunity will not come from placing banner ads inside conversation flows, but from harvesting, structuring, and licensing the vast reservoir of real-time human intent being shared with their models every second.

Human conversation is the new digital gold. For businesses desperate to recover lost visibility into the buyer journey, aggregated conversational intelligence represents the ultimate strategic asset.

The New Revenue Engine for AI Titans

Advertising models built on static keyword triggers are fundamentally mismatched with fluid, multi-turn AI reasoning. Forcing intrusive sponsored links into a personalized voice response destroys the user experience. Instead, AI providers are positioned to monetize the output side of their platforms by acting as enterprise data brokers, transforming raw dialogue logs into high-value intelligence feeds.

By capturing how millions of people naturally discuss needs, compare options, and express frustrations, platform owners can package anonymized context into enterprise-grade analytics products that command recurring software-as-a-service (SaaS) subscription premiums.

Packaging the “Digital Gold”

This new intelligence layer will yield actionable commercial products tailored for product strategists, marketers, and executive leaders:

  • Brand Health & Recommendation Telemetry: Real-time quantitative dashboards tracking how frequently a brand is mentioned during advice seeking, the sentiment surrounding those mentions, and the exact contexts in which competitors are favored.
  • Unmet Need & Latent Demand Mapping: Algorithmic extraction of emerging consumer pain points long before they manifest in formal search trends, support tickets, or market research reports.
  • Decision Boundary & Friction Analysis: Synthesized reports detailing the specific trade-offs (price points, missing features, usability concerns) that systematically cause prospective buyers to reject a product during AI-driven evaluations.

Democratizing Enterprise Intelligence

The power of conversational analytics lies in its scalability across the economic spectrum. While enterprise corporations will pay premium tiers for custom API integrations and real-time category alerts, small and medium-sized businesses (SMBs) will finally gain access to market research previously reserved for Fortune 500 budgets.

A local bike shop or boutique software firm could subscribe to a regional category feed to instantly discover the precise features or price barriers driving customer choices in their specific niche. By turning unvarnished human dialogue into structured insight, AI platforms will unlock an indispensable revenue model powered by authentic human context.

IV. Human-Centered Change & Ethical Governance

Unlocking the commercial value of conversational data requires navigating a complex intersection of consumer trust, regulatory compliance, and organizational transformation. Because natural language dialogue contains deep personal context, commercializing this information demands rigorous ethical boundaries. The success of conversational intelligence as a revenue model hinges on maintaining strict user privacy while helping enterprises build the internal capabilities needed to act on these new insights.

Privacy by Design: The Ethical Imperative

Monetizing conversational context cannot come at the expense of individual privacy. AI platform operators must engineer robust data architecture standards that prevent the exposure of personally identifiable information (PII) while preserving strategic utility:

  • Differential Privacy & Aggregation: Injecting mathematical noise into datasets so macro-level consumer trends can be analyzed without ever exposing individual user transcripts.
  • Synthetic Data Modeling: Generating artificial, representative datasets derived from real conversation patterns, allowing brands to analyze buyer behavior without touching live user interactions.
  • Strict Brand-Level Anonymization: Ensuring that enterprise dashboards expose category-level intent and competitive positioning without revealing specific user identities or sensitive personal attributes.

Overcoming the “Surveillance” Backlash

Public perception will determine the speed at which conversational analytics becomes mainstream. If consumers view the monetization of their conversations as invasive surveillance, user churn and regulatory pushback will quickly follow. AI providers and brands must collectively frame conversational analytics around mutual value creation.

When customer intent data is anonymized and applied ethically, it leads directly to better product design, more intuitive user interfaces, and the elimination of persistent market friction points. The objective must be presented clearly: using collective, human-centered feedback to build products and experiences that better serve actual human needs.

Managing Organizational Readiness

Accessing conversational intelligence is only half the equation; corporate leadership teams must also transform how they make decisions. Applying the principles of Human-Centered Change™, organizations must actively prepare their cultures, workflows, and talent to interpret fluid conversational data rather than static web metrics.

This operational transition requires shifting leadership focus away from legacy digital KPIs like bounce rates, page views, and click-through rates toward modern conversational indicators: share of voice in model recommendations, prompt inclusion rates, and conversational intent fulfillment. Companies that successfully align their internal culture around these human-centered insights will build an enduring competitive advantage in the AI era.

V. FutureHacking™: Strategic Implications for Business Leaders

To navigate the shift from transactional clickstreams to continuous conversational context, executive leadership cannot afford a reactive stance. Applying a FutureHacking™ lens — scanning weak signals around emerging user behaviors today to anticipate the structural realities of tomorrow — reveals a multi-phase transformation in how organizations will make decisions, design experiences, and compete for market share.

The transition toward conversational intelligence will unfold across three distinct horizons over the next decade.

Near-Term Horizon (1–2 Years): The Rise of Generative Engine Optimization & Intelligence Pilots

In the immediate term, traditional Search Engine Optimization (SEO) will yield ground to Generative Engine Optimization (GEO). As organic web traffic declines, brands will pivot from optimizing page headers and backlinks to structuring brand narratives and product specifications so they are accurately ingested and cited by foundational AI models.

Concurrently, early adopter enterprises will join private pilot programs hosted by Google, OpenAI, and Anthropic. These initial telemetry dashboards will give brand managers their first high-level visibility into prompt inclusion rates, category mention frequencies, and overall model recommendation sentiment.

Medium-Term Horizon (3–5 Years): Synthetic Focus Groups & Simulated Customer Journeys

As the granularity of anonymized conversational datasets improves, market research will undergo a radical evolution. Rather than waiting weeks to conduct traditional focus groups or analyze retrospective survey results, product strategy teams will query specialized AI models trained on billions of real-world conversational signals.

Organizations will routinely run product concepts, pricing adjustments, and brand positioning messaging against synthetic persona populations. These simulated customer panels will instantly predict friction points, feature trade-offs, and competitive migration risks based on real-time consumer intent trends, drastically compressing product development cycles.

Long-Term Horizon (5+ Years): Closed-Loop Innovation Systems

Over a five-year horizon, conversational intelligence will move from a passive diagnostic tool to an active driver of automated organizational workflows. Leading enterprises will construct closed-loop innovation engines where real-time conversational data directly informs cross-functional operations:

  • Automated Backlog Prioritization: Product engineering roadmaps will dynamically re-prioritize feature requests based on unprompted feature complaints captured across category-wide AI dialogues.
  • Dynamic Experience Adaptation: Digital touchpoints and customer service flows will auto-tune their messaging and support options based on emerging friction patterns identified by ambient assistants.
  • Continuous Portfolio Alignment: Mergers, acquisitions, and line extensions will be evaluated using continuous, real-time demand signals extracted directly from human-AI problem-solving sessions.

By anticipating these structural horizons today, forward-thinking leaders can begin building the data infrastructure, talent capabilities, and agile decision-making frameworks required to turn conversational signals into market leadership.

VI. Conclusion & Key Takeaways for Innovators

The transition from transactional keyword search to ambient, multi-turn AI dialogue represents one of the most profound structural shifts in the history of the digital economy. As consumers speak directly with Gemini, ChatGPT, and Claude on their mobile devices and desktop interfaces, the clickstream era is drawing to a close. Waiting for traditional web traffic, cost-per-click efficiency, and search ad impressions to recover is not just an ineffective strategy — it is an existential risk.

The organizations that thrive in this next era will be those that recognize where strategic value has re-aggregated: away from driving website visits and toward capturing, understanding, and acting upon authentic conversational context.

Key Takeaways for Business Leaders

  • Acknowledge the Intelligence Blackout: Traditional SEO, web analytics, and click-through attribution models are providing a rapidly shrinking window into true customer behavior. Accepting this loss of visibility is the first step toward building modern, conversation-aware capabilities.
  • Prepare for the Conversational Data Economy: As traditional search advertising revenues face long-term pressure, Google, OpenAI, and Anthropic will monetize anonymized conversational data. Forward-thinking leaders should allocate budget now for emerging conversational telemetry feeds and Generative Engine Optimization (GEO).
  • Embed Human-Centered Change™: Shifting an organization from static KPIs (page views, bounce rates) to conversational metrics (share of voice in model answers, prompt inclusion, intent fulfillment) requires intentional change management. Re-align leadership, cross-functional teams, and innovation pipelines around these new signals.
  • Rethink Experience Design: Continuous multi-turn dialogues reveal unvarnished human friction points, budget constraints, and feature trade-offs. Integrate these real-time qualitative signals into your customer journey maps and product development roadmaps to eliminate customer friction faster than competitors. Invest in a Customer Experience Audit to find where you fall short.

Data was the primary oil of the early web era, but synthesized human conversation is the true gold of the AI era. By pairing ethical governance and human-centered design with the rich intent embedded in everyday dialogue, innovative organizations can illuminate their blind spots, transform their decision-making, and create products that resonate with authentic human needs.

Frequently Asked Questions

Why are traditional search advertising and click-through rates declining?
As users shift from keyword-based search boxes to ambient AI assistants like Google Gemini, ChatGPT, and Claude, they receive direct, synthesized answers rather than a list of web links. This rise in zero-click interactions significantly reduces website referral traffic and traditional ad impression volume.
How do AI platforms like Google, OpenAI, and Anthropic plan to monetize conversational data?
AI platform providers can package anonymized, aggregated conversation logs into enterprise intelligence feeds. By selling brand health telemetry, unmet need analytics, and consumer friction insights to businesses, AI companies create a massive new recurring revenue stream to complement or offset declining search ad yields.
How can businesses prepare for the shift from keyword search to conversational intelligence?
Organizations must transition their digital strategy from traditional SEO to Generative Engine Optimization (GEO), adapt internal change management frameworks (such as Human-Centered Change™) to track conversational metrics like model share-of-voice, and subscribe to emerging conversational analytics feeds to inform product design and experience strategies.


Image credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Gemini to clean up the article.

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