It’s been incredible to witness how, over the course of the past six months, Artificial Intelligence (AI) has gone from being a classic Stephen Spielberg movie of the same name to something that has captured the imagination of not just tech heads, but much of the internet population of the world.
Would you believe that ChatGPT (red line) is typed into Google in Australia twice as much as the word ‘footy’ (blue line)?
My first-hand experience of technologies that changed the world (and many that didn’t)
I had the privilege of testing, selling and acquiring disruptive technology from the vantage point of Silicon Valley (USA) and Silicon Docks (Ireland, Europe) for most of the past 20 years.
In particular, in my time at Google (2006-2019), I helped develop, test and sell all manner of technologies that changed the world, either created by Google or one of its competitors.
- The smartphone: Google’s Android Vs iPhone – which took the BlackBerry and PalmPilot into the mainstream.
- Video streaming: Google’s Youtube Vs Netflix and Disney+.
- Cloud productivity: Gmail/Google Workspace Vs Microsoft 365.
- Cloud computing: Google Cloud Platform Vs Amazon’s AWS and Microsoft’s Azure.
I also was privileged to work on the rideshare and “delivering anything” revolution at Uber, and was around the corner from Facebook’s social networking (especially on mobile), Amazon’s “Everything Store”, TikTok’s short form autoplay videos, and Tesla’s electric vehicles (with EVs first brought into the mainstream by the Toyota Prius).
These are all technologies over the past 20 years that have successfully made their way into mainstream usage.
However, I also witnessed all manner of technologies that we thought might change the world, but didn’t – at least yet.
- Face wearables: Google Glass (I won’t show you the photos, but I wore this every day for about six months and was convinced it would change everything).
- Self-driving cars: We thought it would revolutionise transportation, and Google’s Waymo was at one point valued at $70 billion (with Uber in hot pursuit until they closed that division down).
- Virtual reality: This has been just around the corner for the past 30 years. Perhaps Apple’s Vision Pro will be more successful?
- Drones: I saw more use of these for wedding videography (which was cool) than I did for item delivery.
Honourable mention for some other areas Google was not involved with – the blockchain, crypto beyond Bitcoin, Theranos (once the hottest start up in the world), and my favourite, the Segway (remember that awkward motorised scooter you stand on with two feet that was meant to change micro-mobility?).
How does any technology reach the mainstream?
The challenging thing in figuring out if a technology will go mainstream is that the early years seem the same.
During this time, the trajectory of self-driving cars and face wearables was indistinguishable from early Amazon users and iPhone buyers – lots of rapid growth, excitement and potential.
One book that can help us determine the likely success of AI is Crossing the Chasm, by Geoffrey Moore.
It’s based on the The Technology Adoption Life Cycle (TALC).
Originally published in 1956 to help explain why farmers adopted certain agricultural technologies (such as fertiliser and hybrid seed corn) and not others, it was later expanded to encompass technology in general.
Innovators (first 2.5 per cent of population to try the product). They love new ideas, they tend to have more wealth so they can afford to buy something higher-risk that may not have longevity – they enjoy being literally ahead of the curve. Some of us at Elite Retreat are like this – we are risk-takers and are happy to invest time and money to be the first to adopt what could be the next big thing.
Early adopters (next 13.5 per cent). They are less risk-loving than innovators, but still want to be ahead of the curve and the next trend. Unlike innovators, they are not technologists, and they care about the practical advantages of the product.
They hold positions of ‘opinion leadership’ – the majority look to them to determine if a trend is truly credible. Think celebrities – they are not going to be the very first to endorse something, but they might be fast followers, once the product or technology has traction.
Many of us at Elite Retreat share this mindset – not technology centric, but winning-centric, and keen to understand what technologies can do that.
Early majority (next 34 per cent). They are slower than innovators and early adopters, but are still above average in terms of social status and business outcomes. They share some of the early adopters’ ability to relate to technology, but ultimately they are driven by a strong sense of practicality.
Think of many of the successful agents who are early in their career – they are not open to risking too much time or money on something unproven, but once it’s hit the mainstream, they will try to adopt it.
Late majority (next 34 per cent). They adopt after a significantly longer period of time – after the average. They would rather be conservative and late rather than waste time and money on something that is not guaranteed to be useful.
The late majority lack confidence in changing their behaviours. I think many later stage but very successful agents are like this with technology. They will eventually change – and before the laggards – but it has to be well in the mainstream by this stage.
A lot of more tenured agents – who have tried and true methods that don’t involve much technology – are here.
Laggards (the last 16 per cent). Focused on traditions and the status quo, laggards will adopt when they absolutely must. Think of the last adults you know to have gotten a smartphone … holding on until it became frustrating to not be an adopter.
The key insight of this research was to understand that the five different groups had different needs. In short, innovators didn’t need much beyond the possibility that the technology could be the next big thing – the combination of relatively deep pockets and high tolerance for risk made it attractive enough.
But early adopters wouldn’t take as much risk – their credibility is linked to the success of the technology, so they need a higher probability of success, lest their brand and time be tarnished.
By the time you are hitting the early and late majorities, the technology has to make sense and be materially better value than the current way of doing things.
So, what was the difference between game-changer and nothing-burger?
What Geoffrey Moore was able to do is adapt the curve and identify the most difficult jump, which he called The Chasm.
The Chasm is the gap between the early adopters, which he called visionaries, and the early majority, which he called pragmatists.
It was a tipping point when something truly becomes mainstream (see here for a dancing video illustration).
Put simply, technologies only cross The Chasm if they can bridge the gap between adoption by the visionaries and the pragmatists.
Based on his work and similarly theories, I have identified 5 characteristics of technologies that have been able to cross the C.H.A.S.M:
- CURRENT – Is the current technology pretty painful? For the early majority, how painful is the problem that’s being solved? One of the reasons Uber succeeded was because taxi availability and reliability was so poor.
- HIGHER QUALITY – Is this new tech much better than the current solution? How much more satisfying is the new technology? The iPhone was leaps ahead of the BlackBerry and Windows Phones at the time. Amazon brought you the same product at the same price you would buy it for at the store, except delivered within 48 hours.
- ADOPT – Is it easy to adopt? The first 16% are happy with spending time and effort to learn how to use a new technology – the pragmatist early majority are time poor and risk-avoiding, even though they want to be ahead of the average. The brilliance of the iPhone (and iPad) was its simplicity – no instruction manual needed, just intuition!
- SPEND – Will this displace existing spend? In general, it is much harder to get consumers to pay for something new unless they can stop spending on something else. Amazon’s AWS allowed companies to shut down their data centres and pay-per-use instead, so it didn’t require additional spending, just a diversion of existing spend. Uber was twice as easy to use as taxis at half the price.
- MARKET SIZE – Will this serve a large market/set of needs or just niches? Innovators and early adopters are typically happy to use the new technology for niche use cases. Even Google+ (Google’s Facebook competitor) got a lot of traction with photographers (a visionary group) initially, but it was not able to break into the mainstream. Electric vehicles have started to gain traction because it’s not just for those who care about the environment (niche), but the larger market of folks looking for quieter cars, which are more efficient (majority).
The $1 trillion question – will AI cross The Chasm?
For simplicity – let’s apply the crossing-the-chasm framework to what we know of AI so far. We’ll focus on the agent journey, even though there is a parallel analysis on how AI could disrupt the experience for the buyer/seller (e.g. improving home findability on a portal).
- CURRENT – Is the present pretty painful? Moderately. As a real estate agent, there is a lot of copywriting (writing the listing description) and assembly of marketing materials. It’s the same with email responses. These take time but must be customised. We have already seen many AI-driven tools that can save material time.
- HIGHER QUALITY – Is this tech much better than the current solution? Probably. The speed that generative AI can generate presentations and context is impressive. The quality is somewhat comparable to humans. When the quality becomes the same or better, then we may have a slam dunk.
- ADOPT – Is it easy to adopt? Yes. There has been an explosion of great apps, extensions and websites who have made it beautiful and fun to try AI out.
- SPEND – Will this displace existing spend? Probably. It’s not clear that marketing or copywriting departments can shrink just yet, though it’s clear AI can probably help them be somewhat more productive. When AI is actually shrinking marketing departments – in the same way some property management solutions have actually allowed PMs to double how many properties they can manage – these cost savings will accelerate adoption.
- MARKET SIZE – Will this serve a large market/set of needs, or just niches? Definitely lots of needs and potentially a very large market. In less than six months, I have seen many useful real-estate-specific AI tools, and hundreds of tools that could be useful in real estate. This is just the beginning.
The verdict: You won’t be left hungry!
At this point in time it’s clear AI is not going to be a nothing burger. Yet, I think it’s too early to be sure whether this changes everything, or is simply very useful for numerous niche use cases.
