How to Monetize AI

Full article “How to Monetize AI” is below, underneath the video. Also, check out Alex Hammer’s 18 books on Amazon (including the book “How to Monetize AI”). Also, check out the Alex Hammer Future of Technology Podcast, with close to 500 episodes. Alex Hammer is the CEO of Exponentials.

“Show me the money” — Tom Cruise in Jerry Maguire

AI By Itself is Not a Business Strategy

AI is a tool. Tools are more or less effective based upon how they are used. Better AI doesn’t make you money. Better AI monetization does.

Those who best understand the monetization of AI will be poised to profit from the use of AI in business, careers and investing.

In order to be able to optimally monetize AI in your business, your career and your investments, it is helpful to first understand how the largest technology companies in the world are monetizing and hope to monetize AI in theirs.

Enhancement and replacement AI monetization

Many of the world’s top technology companies are using AI to make their existing products better. Google Search, Facebook social graph and news feed, Amazon Ecommerce website, etc. This is what I call the enhancing function of AI, and related to monetization it is enhancing monetization.

Enhancing AI also relates to individuals. Doctors will use AI to become better doctors and earn more money. Lawyers will become better lawyers to earn more money, etc.

Of course the flip side of enhancement is diminishment, which carried to its logical conclusion is replacement. The money that a profession earns is siphoned off by AI to an alternative source. In other words, lawyer revenues transfer to legal AI. Healthcare provider revenues transfer to health based AI.

And this transfer is not limited to the specific domain. It is easy to imagine a broader based AI which could facilitate transfer of monetization from a combination of domains.

AI monetization in Google, Facebook, Amazon and Uber

Google search monetizes AI by using AI to make search and its search products more relevant, precise and efficient.

Google’s core emphasis on search provides it with a major AI advantage baked into its business model. Search is a data scientist and machine learning professional’s dream.

What’s not to love? Your entire core (search) business is data.

Google Search has data on a perhaps unprecedented scale, because of the volume of its users and the volume of searches. The scale of data is the critical (or at least a critical) factor for successful AI in regards to the success of machine learning algorithms and processes. Search by its nature is algorithmic, which is at the core of AI. Google search data for any individual is gained over time, allowing machine learning to gain greater insight (actually predictive understanding, insight still remains a human characteristic) into how each person learns and evolves in their search behavior, from which it can infer thinking patterns — including a refined understanding of the holy grail of AI monetization in consumer behavior — intent.

Intent is the holy grail of AI monetization for consumer behavior because pattern recognition without intent is like correlation. Which doesn’t prove causation. It’s like the phone before it became connected to the Internet and became a smart phone.

Data without intent is what you say. Data with intent is what you mean.

It is directional. It is focused.

Intent is at the heart of today’s efforts in “smart AI” because it goes beyond the pattern recognition dating mining of large data sets, to forming hypotheses in real time in regards to future actions that are tested and refined to become more accurate.

We call this learning.

Intent needs to be at the heart of AI monetization for Google search. Google search results have traditionally returned many many pages of results for search queries — sometimes millions of results — because Google has been better at knowing what you say than what you mean.

And because what you say can have many many different interpretations in regards to what you mean, many responses are required because Google does not have the higher degree of confidence in regards to which response is correct (although ranked responses does reflect a hierarchy of confidence for each answer).

This lack of understanding of intent gives rise to Google’s large number of responses. Maybe you mean this, maybe you mean that.

Human beings are naturally hardwired as a survival mechanism to continually make judgments in regards to intent. Although there are many cognitive biases in human thinking — and perception is not the same thing as reality - there is a generally shared consensus that we all operate with in the world. For example, while we each may have different associations etc. for the word sad, if someone asked you, “Can you tell me a time when you felt sad?” most of us would generally have a shared understanding of what this question means.

Google would recognize “Can you tell me a time when you felt sad?” is a question, but in its current query based form has a harder time with context. Is the questioner asking when Google felt sad? Is it asking for information about when other people felt sad — and if so, which other people? Is the question referencing something from popular culture — such as a line in a movie? Is it teasing? Is it being sarcastic? It is a rhetorical question?

Understanding intent is critical to monetization. The greater I understand your intent the greater I understand your wants and needs. The greater I understand your wants and needs the more precisely and fully I can meet them — which is another form of saying the better I can monetize them.

The less I understand your intent the more diffuse my response and the less confidence I have in any one response. Did you mean this? Do you want that?

Every search is an intention. The searcher wants to know, for example, what the upcoming weather forecast is in Cincinnati. Or who are the top prospects currently in the next NFL draft. Or what are some recipes for quiche.

Intention provides structure. Otherwise it is, as the famous expression states, only “garbage in, garbage out”. A data mess scales as the data itself scales. So, if your data is a mess, going to big data just creates a hot mess.

Google Search is the heart of Google, but for its AI processes it is also additionally powerfully advantaged for monetization by a host of other inputs about you from its other services, including Gmail, YouTube and Google maps to name only a few. But only if it understands your intent. When your behavior on Google search, Gmail, YouTube and Google more accurately defines your intents (as intent is plural) and combines these where possible, then fewer but more targeted product offerings can meet more of your needs with greater efficacy in less time.

Google potentially knows an incredible amount about you — even the far-reaching Android could perhaps contribute to the mix — but Google’s lack of reach and success in product based Ecommerce relative to Amazon, for example, indicates that Google’s monetization results with AI are narrow.

But money is money, and AdWords for example is still an incredible source of monetization upon which to build.

The point is that the real cash cow of the future or Google would be its ability to use AI to invade and conquer other large markets in which it is currently not a major player.

Here’s a critical takeaway: Just as technology has promoted scale and consolidation within most industries, entire industries themselves have the possibility of being consolidated in the future through the successful implementation of AI. The monetization effects of that would be unprecedented.

Although Facebook has been around a lot less time than Google has, it would not surprise me if Facebook has more actionable data than even Google. Just consider the amount of users that Facebook has and the amount of time which they spend collectively on the platform.

And they have the potential to merge data points from a compilation of properties which is even more impressive than Google’s (and that is saying something): Instagram, Messenger, WhatsApp, Oculus, etc.

Facebook has an analytics precision which lends itself naturally to successful AI monetization. This precision tops even leading precision expert Google. Google can provide an AdWords customer a detailed keyword cost (and in the future performance) analysis of an AdWords buy etc., but Facebook takes their advertising precision to a greater level of targeting and granular detail. Reaching exactly who you want to reach is the ROI holy grail of advertising, and Facebook is without equal in that realm.

In fact, Facebook’s two major rivals for monetization of AI through precision (and thus predictive) analytics are Amazon and Uber, each discussed below in regards to their own monetization efforts with AI. While targeting on Amazon is not as precise as it is on Facebook, Amazon possesses a much more valuable data set in regards to monetization than does Facebook — your purchasing history.

Facebook’s monetization model in a more general sense is very similar to Google’s in that both are extremely dependent upon advertising and have not significantly expanded monetization into Ecommerce and other areas.

Both company’s failure to do so is based upon two primary factors. For one, Google and Facebook are far and away the two largest monetary advertising platforms on the Internet. This dominance for each has given rise to so much financial success that monetization expansion and diversification has not been necessary. When you are one of the two 800 pound gorillas in such a huge market, protecting your place by focus rather than possibly slipping by expanding into other monetization markets is the easiest and by far the safest strategy.

But only in the short term. In the longer term the more effective strategy is to have a monetization strategy that includes or combines multiple markets.

One might argue that Facebook and Google have thrived for years with their current strategy. True, but look at the landscape. Technology breeds consolidation through efficiencies. Both Facebook and Google have been the beneficiary of this consolidation principle until now, forcing small players out of the market or to the margin. But what happens in the future as this consolidation continues and there only a very few most significant major players left (someday even the IBM’s of the world are likely to acquired by one of the largest remaining tech companies)? When this occurs then it will be a battle to see who is the strongest among the very few giant companies which remain. That is, when the market is reduced to only a few players — and I’m talking about the entire technology market here in this example — then the only way to have future growth in market share is at the expense of one of the other huge giants still remaining.

And at that time a broader monetization capability can be the differentiating advantage.

Amazon, who we will examine next, by being the extremely dominant player in Ecommerce and then beyond this extending their monetization capability well beyond Ecommerce as well is positioned extremely well for this type of scenario.

The Limits of Facebook in AI Monetization

Have you ever known people in whom their greatest strengths are also their greatest weaknesses? It is the same principle with companies.

Think about this: In regards to scope, how much of your life resides on Facebook? While some Facebook users certainly seem to share “everything” on Facebook, many do not.

Yes, you say, but the Facebook universe now includes much more than just the original iconic social network.

True enough.

Facebook as a company is broadening. Instagram, Messenger and WhatsApp are closer to extensions to Facebook’s social network function than expansion into new markets, but they do build out capabilities and reach new (but also overlapping) audiences. Oculus is a different animal because Zuckerberg has realized that there will be another dominant platform after mobile, and he is betting that VR will be it.

Google has YouTube, but if VR does become the next major platform then YouTube and other major properties across the Internet would also have to become a VR platform to keep up with Oculus and VR pure plays, and Google while it is investing in VR does not seem to have the industry leading VR capability which an Oculus, if successful, would have.

Amazon would potentially appear to be in worse shape than Google in regards to vulnerability to VR as the next disruptive platform, although Amazon may not be in as bad a condition in this area as it would first appear.

Although Amazon’s core Ecommerce business has essentially no current VR capability, Jeff Bezos has taken actions which demonstrate that he may well be smart enough to overcome this. In particular the breakout hits of Alexa and Amazon Web Service, and also Amazon studios each could be bridges to VR success. Alexa’s voice system (which is also a major threat to text and link based seach) can expand into VR capabilities in the future with voice as the bridge. VR will be hosted on the cloud eventually (decentralized and/or distributed networks, by the way, seem to be the future of everything, even money) and Amazon Web Services expertise and dominance provides Amazon a VR bridge there. Amazon Studios is a further VR bridge. So if and when VR becomes the next major platform, Amazon is in fact (although on the surface it does not appear to be) well positioned to develop and then monetize VR.

The above will (or could) help Amazon develop VR, and its Ecommerce dominance, integrated with VR, could if things break right easily become the world’s dominant AI monetization engine.

But let’s finish our examination of Facebook AI monetization before we examine Amazon AI monetization also in more depth.

The Interest Graph

The social graph can be more than just a social graph. It can be an interest graph as well. In Section two below, “AI Monetary Disruption by Industry or Larger Scale” I will discuss how the interest graph is a and possible the critical factor in AI monetization in regards to moving from a push to a pull business model.

In regards to Facebook specifically, let’s just say that when you know what a person is interested in then you are close to identifying their wants, aspirations and needs (remember before when we talked about intent).

And these are the things that you need in order to most successfully monetize AI.

We talked above about Amazon’s AI monetization advantages through both Ecommerce and monetizing across other major and emerging huge markets.

The importance of both is difficult to overstate.

Jeff Bezos has shown that he is a disruptor among disruptors. First it was just about books on the Internet, then it became about Ecommerce. And then he dominated cloud computing (increasingly the utility of technology).

Let’s examine this before going on to Amazon’s other breakthrough areas.

Cloud computing is becoming the utility of technology. Just as every business needs electricity it will need cloud computing. This puts Amazon’s tentacles, if it retains its position, in every type of business if not every business. This itself is a monetization coup. But the data associated with this position is potentially even more valuable from a monitization standpoint.

The owner of McDonald’s once said that the company was not a food company, but a real estate company, due to the real estate value of all of its (prime) locations.

Similarly the value of Amazon is largely, and increasingly so, not as a platform for commerce but as a data company.

What Amazon knows, and in particular how to monetize this knowledge, is extremely valuable. Bezos has already talked about how Amazon is using AI to make thousands of company systems more efficient. Does that mean that there will be thousands of more potential AWS’ that Amazon could role out in the future and monetize.

When you combine this leading efficiency with the world’s most obsessive customer facing giant company in the world, the opportunity to use those efficiencies towards customer empowerment and monetization looms very large.

Think for example of Prime and what that program might look like utilizing all that Amazon is learning and will learn about monetizing AI.

Amazon traditionally has a problem with profitability, or ROI.

It’s not a good monetization record even if you are able to someday make a zillion dollars but it costs you a zillion dollars (or more) to make it.

You may have heard the famous expression: “We lose money on every sale, but we make it up in volume.”

Amazon has had a number of profitable quarters in a row now, but it has a history of chronically losing money. It’s not just Amazon, which has become the dominant Ecommerce player, but rather losing money has become endemic to Ecommerce players generally both large and small. There are a host of reasons why Ecommerce companies lose money, but the number one reason is customer acquisition costs. It is expensive for Ecommerce companies to gain and retain their customers (even a tremendously successful program such as Amazon Prime, for example, is expensive to run).

As a result of high customer acquisition costs, Ecommerce is considered a low ROI investment, which is why many VC’s have stayed away from making investments in Ecommerce.

A current limitation of Amazon’s AI monetization is that is more of a comglomerate than an integrated company. Although history is full of examples in which knowledge in one area was later applied to another, it is not immediately clear how Amazon’s AI monetization in Ecommerce would substantially help or even impact their AI monetization in cloud computing, voice or other hardware or other areas.

As every major technology company is getting into their competitors’ spaces — the more lucrative the space the greater the competition — this lack of cohesiveness among Amazon’s activities could be a strong limiting factor in the future in regards to Amazon’s ability to maximize the revenue and rewards, company-wide, from its efforts to monetize AI

Of the four: Google, Facebook, Amazon and Uber, Uber (along with Amazon) has the best chance of monetizing AI on the grandest scale.

This might surprise you.

Due to the law of accelerating returns, the rate of change is itself increasing. One result of this is that companies can become bigger and more dominant in less time than ever before.

Think for a moment about the implications of that.

Just as (see above) McDonald’s may be a real estate company masquerading as a fast food company (a good part of the value of McDonald’s is due to its real estate holdings), and Amazon may be largely a data company masquerading as an Ecommerce company, Uber could already easily be considered an AI company (some might use the term logistics company) masquerading as a transportation company.

Things get very interesting as a result.

Uber uses AI, or logistics, to run its transportation system. But the same system which applied to rides could be either applied or adapted to anything that moves.

Food delivery for example, which Uber is already involved in.

It it dedicated itself to it as a priority, Uber might well deliver packages more efficiently than UPS. It might fly planes as or more efficiently than American. It might — no easy task — create autonomously driving cars as efficiently as Telsa.

Beyond transportation the opportunities are endless. Uber is essentially a marketplace to match buyers and sellers. So is eBay. So is Google. So is Amazon. There is no practical reason why Uber, as an AI based company — if its AI is or becomes superior to these other companies — could not outcompete any of them in their core businesses.

As a result there is no technology company today that should not fear Uber as a competitor, or any other company which demonstrates leading competence with AI.

Although Uber has not even yet put the taxi industry out of business, they may be well on their way. The same with Airbnb with hotels.

(And there is the interesting question also of whether as premier on demand marketplaces, whether Uber and Airbnb are on the path towards future collision even though today they focus on separate industries).

Here’s the real AI monetization secret. If you have superior enough intelligence you can build anything which already exists (as well as a lot of things which don’t).


Why not? What core underlying skill set does either Google or Facebook or Amazon have that Uber, if it chose, utilizing and adapting its AI architecture, could not develop?

And you thought Instagram copying Snapchat was a big deal.

Uber has the additional important AI monetization advantage in that it can more easily leverage having a physical presence in the world. Can Google do that (autonomous car involvement notwithstanding)? Can Facebook do that?

When the Internet of Things becomes prominent and powerful, Uber as a physical presence will have a leg up with this which Facebook does not have.

Uber That

Google was not the first search engine, but it is the best one so far. Facebook was not the first social network but it is the best one so far. Uber is so young still, relatively speaking, and not even a public company yet, that it is difficult to predict its longer term trajectory. And it faces strong, well funded competitors in its core business which have ganged up against it.

Uber is not all knowing. It doesn’t for example have the information that you shared with Google or Facebook. And there is a lot else of course which Uber doesn’t know. But when you have the best intelligence systems, then you have the best means of figuring these things out.

AI Monetization Disruption by Industry (or Larger) Scale

Replacement refers to AI monetization displacing individuals or individual businesses (or a collection of such).

Replacement functions on the more micro level.

Replacement on the macro level, of entire industries, governments, economic systems etc. is termed disruption.

This is where the fun starts.

Our company, Ecommerce ROI, is built utilizing AI as an industry disruptor. We don’t have Amazon, Alibaba and eBay etc. quaking in their boots (at least not yet) — but the power of disruption is not that it will beat the incumbents at their own game. No, instead it creates a new game which subsumes the old.

As Henry Ford famously said, his customers didn’t ask for a car. They wanted a faster horse.

You’re not going to OutGoogle Google and you are not going to outAmazon Amazon. The next Google will be either a different type of search engine or something different which includes Search within its overall framework. Already search is beginning to migrate from a text and link based system (including also images and video) to a voice based system, and it is not at all clear whether Google will retain their dominance in voice based search or not (Amazon already seems to have the very early lead in voice based search). If the next search platform beyond voice is VR or something else, will Google remain in the same position to lead and/or compete?

Similarly, the next Amazon will either be a different type of Ecommerce or something which subsumes Ecommerce more efficiently within its overall framework than Amazon can do. Just as Google has kept a text and link based system for search for many years now, Amazon has done an analogous thing utilizing web and now mobile for many years. What happens to Ecommerce when web and mobile is not the dominant Ecommerce platform? As one example, think about the Internet of Things (IOT). When all (or most or many) physical products become smart and connected, Ecommerce will be a lot different than it is today. But how? Can Amazon utilize its AWS and/or other AI expertise to dominate Ecommerce in the IOT? What about Ecommerce in VR? Ecommerce in additional future platforms?

Just as adoption rates and product cycles are shorter than ever, incumbency rates of market leadership tend to be shorter and shorter as well. Both Google and Amazon have put themselves, frankly, in a rather precarious position — as dominant players generally do — by putting legacy systems above future platforms. Google should have become the clear leader in voice search years and years ago. Same for Amazon in Ecommerce for the Internet of Things? Do we even know how Amazon intends to sell in an IOT world? In a VR world?


Hey, that’s my company.

Exponentials is the world leader in content based Ecommerce (AI driven).

So what? What good is that?

Earlier I talked about high customer acquisition costs being the bane of ROI for Ecommerce companies. It turns out — as Exponentials is demonstrating — that customer acquisition costs can be reduced by a factor of close to four with — and this is the difficult part — specific business modeling practices pairing content effectively with Ecommerce.

To think about it in the most general terms — this is not how it is done but to start to appreciate the concept — think about if Amazon (Ecommerce) had sex with either Pinterest, Google, Facebook, Wikipedia or The New York Times (content).

Or each of them. All of that Ecommerce merged with all of that content.

“You got peanut butter in my chocolate. No, you got chocolate in my peanut butter”.

But not just thrown together. AI driven. (I’ll explain the general details as we progress in this chapter).

Ecommerce is only one industry. Any industry, potentially, can be disrupted via superior AI monetization.

Ecommerce is Broken for Investors. How Better AI Monetization Could Fix That.

Resistance is futile????

Amazon is like the Borg. They subsume everything in their path.


Amazon might well be the smartest and most powerful company in the world.

And importantly one of the most adaptable as well.

In 2015 I wrote about how any Ecommerce company is vulnerable, including Amazon or Alibaba, to an Ecommerce company who can flatten customer acquisition costs. That is, specifically, improve customer acquisition costs by several orders of magnitude over the largest incumbents.

This is because customer acquisition costs are the major pain point for Ecommerce companies.

The pain of high customer acquisition costs

In essence, the disruption comes from removing and reworking much of the cost structure, such that there is a new financial model of much greater efficiency.

And from removing much of the friction, also, at the same time.

Uber — on several key dimensions — is more efficient than taxis, which is why despite being a huge underdog from the key regulatory perspective which strongly props up taxis, in only eight years has become a company that by February 2017 had a reported valuation of almost $70 bilion dollars (and Uber has plenty of competition from others in this disruptive business model)


Let’s examine this.

Is Ecommerce ripe for disruption?

Ecommerce, despite being a $1.5 trillion global annual market growing 20% a year, is generally considered a low or lower return on investment (ROI) category, scaring away investors requiring greater returns. Ecommerce is a low return on investment industry most significantly because of high customer acquisition costs. Simply put, in Ecommerce it is expensive to gain and retain your customers.

Does it make any sense to you at all that a market this huge — $1.5 Trillion annually — and growing this quickly, is not a good return on investment for professional investors?

Shouldn’t somebody be able to change that?

Why Does Ecommerce Have High Customer Acquisition Costs?

Many reasons.

Competition for one. The types of items that one Ecommerce player is selling are generally sold by many other companies, both online and off. It is expensive to break through the clutter so that your offerings can be found. It is expensive to provide your products at a price that is attractive relative to your competition. Once you gain your customers it is expensive to retain them from the offers and promotions of others.

These costs are of course much more daunting if you are a newcomer as opposed to an incumbent. Although theoretically new business is only a click away, switching costs exist. Especially for customers who are engaged in strong loyalty programs such as Prime. The advantages of such loyalty programs, coupled with the selection, markets, technology and pricing advantages of an Ecommerce leader like Amazon, make it cost-prohibitive for a newcomer to compete. Unless they compete on the margins in a fringe market, and what type of investor is excited about that?

Flattening Customer Acquisition Costs to Disrupt Ecommerce

If flattening customer acquisition costs were possible, wouldn’t the large incumbents have already done it?

Yes, of course. Unless doing so meant essentially starting from scratch and abandoning the “cash cows” that form the basis of one’s business model and leadership position.

If it isn’t broken…

Is Amazon Too Big to Fail?

Was the Titanic too big to sink?

We know in life, and in technology and in business that, as is famously said, the only constant is change.

We all know that every industry is eventually disrupted. The greatest companies regularly disrupt themselves before someone else does it. But we all know that this is extremely difficult for incumbents to do. Immensely successful companies can become victims of their own success, tied to legacy systems. Outsiders call them legacy systems. Insiders call them cash cows.

If it isn’t broken…

Just as a faster horse was not the answer to competing against automobiles,

a larger Amazon is not the answer to competing with anyone who can solve Amazon’s major pain point — and the major pain point of Ecommerce companies generally — customer acquisition costs.

Those who believe that scale alone or even fundamentally is the cure for flattening customer acquisition costs are wrong. While certainly greater scale DOES lead to the removal of redundancies and greater cost efficiencies, it does not alter the fundamental business model of Ecommerce from which high customer acquisition costs arise.

And that business model is a push dynamic — you have to go out and find your customers, which is expensive:

Customer acquisition costs can be flattened by leveraging the trend — not widely recognized and even less utilized — that Ecommerce is transitioning from a push to a pull activity

Ecommerce is high customers acquisition cost because it is push technology. You have to go out and find your customers.

If Ecommerce could become, instead, a pull technology, where your customers find you rather than you having to find them, then customer acquisition costs could flatten.

We call this “smart ecommerce”.

Without giving away the secret sauce I can tell you that this is done in the context of AI leveraging what might be termed the the interest graph — tapping into intrinsic interest — via the algorithmic pairing of content and ecommerce.

The idea is simple but the successful implementation is extremely hard (as evidenced, for example, that it had not been prominently done to date).

Monetization (as with everything else) is evolving and changing

A few videos which demonstrate how change (and thus disruption) has become exponential rather than linear (the famous “law of accelerating returns”).

AI isn’t fundamentally a tool which companies utilize in their business operation. Rather, it is a transformation that alters our very way of life.

There was a long period of time in human history before money existed. Money became a useful and necessary construct when the human species moved towards specialization of tasks. In specialization you need a way to assign value to specific varying task outputs, and money accomplishes this. The market decides that this output is worth 20 units, while this output is worth 427 units.

Money is an idea. A dollar is a piece of paper that is worth as much or as little as our markets — which we have created and maintain — assign it.

Money used to be gold coins. Or tulips.

Job displacement by autonomy is a fact, and an accelerating one. Whether enough new jobs are created to maintain the present economic system or whether we transition, over time, to largely workerless societies remains to be seen.

What would money look like in a post work world? Are we given a salary or, if AI has moved us beyond the scarcity that exists in the world today, do we just take what we need?

Let’s take a look at the Internet, perhaps the clearest example today of the connections between us.

The Internet is becoming like electricity or the air, it will just be everywhere as a utility or unseen presence in the background. Wearables will put the Internet on us, nanotechnology will put the Internet in us, robotics will put the Internet next to us and the Internet of Things will embed us within the Internet all around us.

And there’s more.

VR will create an Internet of our thoughts, visions, ideas and fears. Technological implants in humans and genomic/genetic alterations will create new types of experience beyond our current understandings in an already Internet connected world.

Artificial intelligence will be central to all of this and more.

If you just want to make money from artificial intelligence today then there are a set of principles that one can follow, but it is important to also keep in mind that AI is beginning to change the world and the rules of how it operates — I call it bending the future — in unpredictable and non-envisioned ways.

Consider this: The mass Internet is only a human generation — 30 years — old. And yet the mindset (collectively) of those who grew up totally without it and those who grew up knowing nothing else but it appears profound.

The law of accelerating returns teaches us that the pace of change is itself increasing — and not in a linear but in an exponential fashion.

Ultimately AI does not solve our problems, so that we remain the same but better. Instead it changes everything. It changes everything we know, touch, see, sense, feel and believe.

Biology brought us out of the oceans. Controlling fire and using tools (and eventually language) separated us from the animals. Farming took us out of the caves. Industrialization grouped us in cities. The knowledge economy accelerated social mobility and “opportunity”.

It is famously said that the amount of change is overestimated in the short term but underestimated in the long term.

At some point — and we don’t know what that is, what used to be experienced as a billion years of change will take place in an hour. Or a minute. Or a second. Or a nanosecond.

But as we’re alive today, let’s think about monetization over perhaps the next 10–20 years.

First, money is going digital. Speed is power (think electronic trading on the stock market) and cash is on its way to becoming obsolete.

Credit cards. Smartphones. Bitcoin.


Money has always been a synonym for efficiency (sometimes referred to as value). AI is an organizing principle. Which is another name for efficiency as well.

In a world already where people won’t wait three seconds for a website to load, money needs to also make us feel good.

Stay with me here. I know that that probably isn’t intuitive.

Money is moving beyond a transaction-based system to an experience based system. Uber is (or is becoming) a money based system. So is Amazon. So is Airbnb.

Remember, money is just another term for efficiency or value.

The percentage of GDP which these enormous companies are on track to acquire going forwards means — in practical terms — that governments are becoming feeders for multinationals.

Already the market value of the largest technology companies dwarfs a large number of countries.

As companies become more and more valuable, it will at some point force a re-examination even of the notion of the nation-state.

Why would Jeff Bezos want to step down and be President of the United States (although there are rumors that Mark Zuckerberg wants to be President) when Bezos is already the founder and CEO of Amazon?

Or this:

What does investment look like when ICO’s and/or other forms of crowdfunding become larger than VC raised funds?

Or this:

We had only three major TV stations not so long ago and now every brand is their own media company.

Does this trend translate into every individual someday being their own NASDAQ?

If the future is decentralized networks — are present applications built upon Ethereum the Kitty Hawk of today?

In the next 10–20 years we will increasingly recognize that money cannot be fixed — cannot be made static. It flows and moves to where it is best treated (just as people do, by the way).

This has always been the case, but is more apparent now in a streaming based world.

Money is made by harvesting flows, like a river moving downstream shapes all with which it comes into contact.

Finally, AI and monetization are like two sides of the same coin. Heads and tails. More directly: Any act of intelligence is by definition an advancement and move forward. Monetization is an expression — a verification — a record — an indicator if you will— of that movement.

When you see monetization you know that intelligence has been there.




Alex is the Founder and CEO of Exponentials.

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Alex Hammer

Alex Hammer

Alex is the Founder and CEO of Exponentials.

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