In general, as technology advances, it is human nature to get excited about the possibilities and to get disappointed when those expectations aren't met.
At its core, the Hype Cycle tells us where in the product's timeline we are, and how long it will take the technology to hit maturity. It attempts to tell us which technologies will survive the hype and have the potential to become a part of our daily life.
Gartner's Hype Cycle Report is one of my favorites. It is a considered analysis of market excitement, maturity, and the benefit of various technologies. It aggregates data and distills more than 2,000 technologies into a succinct and contextually understandable snapshot of where various emerging technologies sit in their hype cycle.
Peak of Inflated Expectations (Success stories through early publicity),
Trough of Disillusionment (waning interest),
Slope of Enlightenment (2nd & 3rd generation products appear), and
Plateau of Productivity (Mainstream adoption starts).
Understanding this hype cycle framework enables you to ask important questions like "How will these technologies impact my business?" and "Which technologies can I trust to stay relevant in 5 years?"
Another methodology uses frequency analysis to identify the "most hyped" concepts and technologies.
VisualCapitalist recently put together an infographic highlighting the most hyped technologies of each year. They call it the "Peak of Inflated Expectations".
2017 – Virtual Assistants, Connected Home, Deep Learning
2018 – Biochips, Digital Twin, Deep Neural Networks
2019* – 5G, AI PaaS, Graph Analytics *Missing from the infographic, but updated by Gartner
As we take our smartphones for granted, it's hard to imagine bluetooth, wireless web, or e-book readers as emerging technologies at this point – but at a time, the lightbulb was an emerging technology.
It's also interesting to look at which technologies peaked in a hype cycle, or which now popular technologies don't show up on this list. Despite Virtual Reality being around since the 80's, I expected to see it on this list.
Cryptocurrencies, "smart homes", and several older examples are fizzling or burnt out – but that doesn't mean they won't have resurgences.
As a reminder, the hype cycle and the innovation/adoption cycle are often on very different time scales. It's very possible that technologies from the early 2000s may still have their heyday.
What are you surprised wasn't on the list? And, what do you think is about to get added?
I went to a conference in Phoenix with my son Zach last week. While there, he noticed something interesting … Lyft is testing Alphabet's Waymo autonomous driving service live on the streets.
Regardless of concerns about the future of the gig economy – this is a glimpse into our not-to-distant future.
Here's what they say about it.
I had to leave early to catch a flight, so my son decided to use Lyft to get around. He accepted the Waymo terms, but didn't get a Waymo vehicle. Still, I thought this was cool.
There will likely soon be a tipping point where autonomous vehicles proliferate. Even though we are not "there" yet … the progress is obvious and the "quickening" is happening.
Meanwhile, examples of innovation and exponential technology successes and adoption are all around us.
On January 2nd, 2020, after many years of hard work, we launched the Capitalogix Absolute Return Fund.
It’s been a long and hard road … but also a labor of love.
On a related note, I’ve talked about moonshots, playing a different game, and getting comfortable being uncomfortable. These themes come up because they define what we do at Capitalogix and what was necessary to create the technology platform that runs the fund.
Speaking of getting comfortable being uncomfortable, I spent the last week on a family cruise through the Caribbean.
We celebrated many important events including my mother’s 80th birthday, my sister’s 50th birthday, and Jennifer and my 12th wedding anniversary.
We happened to be in Grand Cayman on January 2nd, the day fund launched.
I had a chance to stop by Walker’s (our fund lawyers) to commemorate the day with them as well.
In my office, there's a series of artwork I had commissioned from GapingVoid. One important piece states that "wisdom comes from finer distinctions".
The more nuance you can capture, from less, the better – think Sherlock Holmes's perceptiveness compared to a normal police officer or detective. He noticed things others didn't and came to conclusions that others couldn't.
The best detective isn't necessarily the one who looked at the most stuff. It might be the one that was clever enough to ignore the wrong stuff (which led to being able to discern the right answer in less time).
The same concept applies to AI and data. More isn't always better.
The evolution of mastery requires gaining more from a dataset.
Don't get me wrong – data is a precious commodity, and more (and different) data is often better – but it means much less when you're not using it right.
More Data = Less Visibility
We're living in the best era (so far) but people are increasingly frustrated and unhappy. They're less happy in their relationships, they're less happy in their jobs, and they're more depressed than recent generations.
Comparison is the Thief of Joy
It's too easy to say it's because they're "snowflakes", but it makes sense. They're surrounded by people yet have less meaningful connections due to so much of it being "online". They see everyone's highlight reels and feel like they can't live up. They're inundated with media from all directions and they have infinite options in our hyperconnected society.
Data is exhausting. Choice is exhausting. It's the reason willpower doesn't work, and it creates anxiety and lack of movement.
It's the same with data – the more data an AI has to sift through the harder is to separate the signal from the noise. It's why daily optimization is harder than monthly optimization.
That doesn't mean that there's not value in more data (or in daily optimization) – but it means you need to be calculated about it.
It needs to be built on solving the right problem.
We Don't Have More Problems Than Ever, We Have More Complex Problems
As a society, we've solved so many of the low hanging fruit that we're having to solve more complicated problems. From a theory of constraints viewpoint, we've fixed a lot of life's bottlenecks and we're now dealing with the real underlying issues.
Solving for the "local optima" is fixing a symptom – we have to look for the global optima and solve for the disease.
Today, I'd rather have a 10-layer algorithm than an algorithm that looks at 10 datasets … creating finer distinctions.
Sparse Data + Agile Decision Engine = Better Outcomes
You'll get a better outcome by focusing on answering the right question with the right datasets than by throwing a mountain of data at a random neural net.
Scale matters, but is secondary. It's built on the back of finer distinctions.
The more noise you can remove before feeding information to an algorithm the better.
It seems simple – but that's the game, and it's the one most aren't playing.
From governments, to Google, to Facebook … it feels like it's impossible to have any expectation of privacy today. Amazon knows what I want before I do.
This is an issue that cuts both ways. On the one hand, increased surveillance means we are arguably safer – because the digital omniscience makes it harder to get away with crimes … but all this extra data on us makes it easier to commit other crimes and to suffer from the increasing lack of privacy.
Unsurprisingly, 8 of the top 10 most-surveilled cities in the world were in China. It's even less surprising with the Hong Kong protests and the new social credit system.
Hedge Funds – and active managers in general – have been under fire for several years. Almost 50% of Hedge Funds saw a decline in assets under management (AUM) in 2018.
On the surface, it makes sense … during a long-term bull market, indexes and other passive options like ETFs become en vogue. During a bear market, active management offers more opportunities to outperform the market.
Hedge funds are designed to, you guessed it, hedge risk. So, when investors see less risk in indexes, the demand for active management declines. Especially when performance declines as well.
When something monumental changes the past is left behind and you begin a new future. When electricity was created, no one was going to make candles the primary form of lighting. After the introduction of the car, horses & buggies were never going to be the #1 mode of transportation, and we're also seeing that with the adoption of AI & automation.
Most changes aren't monumental.
I have a fundamental belief that things go in and out of phase and that what's once old is often new again. You see it with fashion, music, phones – etc. First phones got bigger in order to do more, then smaller for convenience, and then larger again so that old dudes like me can read the text.
I believe it's the same with active management – the techniques may have gone out of phase – but active management still offers the potential outperformance. The trend mirrors the stock market; bulls turn to bears when buyers run out – so as outflows from funds continue to peak, and funds continue to close, it seems reasonable that there will come a time when demand rises again.
At that point, "active management" will give way to "Active Switching™" (which goes beyond stock picking to choose the markets, techniques, time frames, risk levels, allocation strategies, etc. using a variety of techniques, data sources, and real-time contextual clues).
This is part of what's covered in my upcoming book, "Next On Wall Street: Understanding AI's Inevitable Impact on Trading."
Looking forward to launching that book in early 2020.
