Here at Future Crunch, we prefer not to make predictions. As someone very wise once told us:
Those who live by the crystal ball die by eating broken glass.
Once in a while though, it's nice to break the rules.
So here are some tech predictions for 2018.
Perhaps the most important thing to understand in 2018 is that the main force now driving technological innovation is global connectivity. Most people who have access to the internet have only just gotten it, and that's led to an explosion of new and interesting ways of using it.
In 2018, we're going to see this process accelerate, especially in places like India, China and Africa, where digital services, especially in fintech, are now entering a more mature phase. Almost a billion people have gained access to the internet in the last five years, and we’re starting to feel that.
If 2017 felt like the year that the Chinese tech giants (Alibaba, Tencent, Baidu and Xaomi) became global household names rivalling the big five out of the United States, then 2018 is going to be the year that we see new giants join the global tech elite, most likely from India.
For the established tech giants, 2018 will be another difficult year from a PR perspective. While they're certainly not struggling for profits, their public image has taken a battering. Advanced data analytics, combined with hyper connectivity and social media platforms with billions of users, have made it easy to target people with messages crafted to shape their outlook on the world. For Facebook, for whom this is the foundation of their entire business model, this is going to be a problem, and it looks like the company will continue to be a convenient punching bag for people looking for political scapegoats.
That said, I think we're going to see facts come back into fashion in 2018. People are less gullible than the media likes to make out, and I think we've probably seen peak fake news. People now have a better idea of what to look out for. The same process played out with the printing press, the early days of newspapers, radio, television... and now we're seeing it happen for the internet. That's the nice thing about truth. You can only deny it for so long.
If you think crypto had a big year in 2017, wait till you see what's coming in 2018. Some people are calling it Web 3.0, and while that’s not quite technically correct (the protocols are different) it’s a good term because it gives you a sense of how big crypto is going to be.
We've laid out our argument for this in detail over here.
That said… it’s crucial to ignore the hype, and remember that we're still in the very early stages of this revolution. Most of the serious work going on in the space right now is about building the basic infrastructure for these new platforms. As many other people have pointed out, if you want to compare it to the internet we're still in the early 1990s.
The problem is that everyone has seen this happen before so there's a lot of FOMO, which leads to a lot of speculation. People are taking punts on companies who are promising to build the next Facebook, Whatsapp, or Salesforce, on the blockchain. That's dangerous, because the infrastructure just isn't ready yet.
We are definitely in a bubble - and it's likely to be even larger than the dotcom one, because we're in a world where more than 4 billion people have internet access, and every single one of them can theoretically be an investor. The digital onramps are now good enough to allow almost anyone in the world to speculate on and invest in crypto, which is why it's so crazy and unpredictable.
My prediction is that we're going to see the bubble continue this year - there’s no crash in sight yet. The market cap for crypto is about $600 billion, I think it could go to 3X or 4X of that. If you are looking for a rule of thumb on which coins or platforms to invest in, go for the ones that are building the infrastructure for the systems to run on (ETH, Cardano, NEO), or the ones that offer a totally different type of technology (IOTA, XRB).
2017 was also an astonishing year for artificial intelligence, or what should really be called machine learning. Computer vision, for example, was stagnant for decades, but has made so much progress in the last few years that we now have computers that can classify images and videos with superhuman performance. The same is becoming true in recognising human speech and even generating voice, or reading someone’s mood or mental health status from their voice. These two alone - the ability to understand vision and recognise voice - will fundamentally change how we think of work in general and what our interface with machines will look like in the future. And they're just the tip of the iceberg. There’s a reason that Sundar Pichai, the CEO of Google, says that “AI is more profound than electricity or fire.”
For me, one interesting question is whether we are going to see a sort of 'machine learning black swan' event in 2018. Almost all the AI stories we hear about these days are examples of supervised or semi supervised learning, where the algorithm is trained on large data sets of previous examples, and then uses that ‘knowledge’ to tackle previously unseen examples. Think of the algorithm like a child, which has to learn from its parents, and whose hands need to be held the entire way.
The biggest AI story of 2017 was AlphaGo Zero - the first serious application of unsupervised learning. They gave the algorithm zero knowledge, and within 36 hours it had taught itself to play Go well enough to beat AlphaGo, the algorithm that made headlines in 2016 for defeating Lee Sedol. Within 72 hours, it had defeated AlphaGo 100 games to 0. And all of this was done using 4 chips, rather than 48. In other words, it’s 100 x better, and uses 10 x less power, because it doesn’t have to learn from large numbers of previous examples.
The algorithm is like a child that learns to read and write entirely unassisted. It taught itself, from scratch. The reason that matters? We might be able to apply machine learning to domains where little data exists or the data is hard to get for structural or legal reasons, greatly widening its applicability in all business and societal processes. David Silver, the lead researcher on the project, put it as follows:
By not using human data, or human expertise in any fashion, the algorithm has removed the constraints of human knowledge and is able to create knowledge itself from first principles; from a blank slate.
Unfortunately, away from those kind of sci fi headlines, the reality is that most companies simply don’t know how to use machine learning effectively yet. In part, that’s due to the shortage of people with the right skills; according to a recent DigitalOcean report, only 17% of developers worked with artificial intelligence or machine learning in 2017. More importantly, the vast majority of businesses, large and small, don't yet have sophisticated enough data collection and analytics operations to take advantage.
Everyone’s talking a big game in their marketing materials and Powerpoint presentations, but the reality is that very few companies are digitally mature and equipped to harness the incredible abilities that machine learning actually offers. Most are still trying to get their heads around 'digital' and aren't nearly ready yet for 'cognitive.' In 2018, successful businesses are going to be the ones that start investing properly into data analytics. It's no longer a back office function - it's central to the effective functioning of any 21st century organisation.
Virtual Reality/Augmented Reality/Mixed Reality
The virtual reality hype machine ran into trouble in 2017, after a very good few years. The blindspot wasn't technical - the technologies are progressing at a fast clip - but personal. Much to the shock of the Silicon Valley brogrammers, people who aren’t gamers and geeks don’t really want to immerse themselves in a purely digital environment for hours on end. Having been released into the wild, it turns out that virtual reality is not the empathy machine, or next computing platform, or new medium that was promised. It's an interesting tool - for example, there have been some great applications in counselling and training. But it is certainly not a new, general purpose technology along the lines of the iPhone or the PC.
What's coming after VR could be. The big story this year is going to be the race for dominance for mixed reality: a set of always-on glasses that will blur the line between the physical world and a digital contract made of pure information. Right now it’s looking like a throwdown between HoloLens and Magic Leap. The former has a headstart, with the advantage of a few years of production and real world testing. However, the latter has released a protoype, and now it’s game on. Benedict Evans says that Magic Leap is "a bit like experiencing multi touch for the first time” and if that’s the case then we’re only a year or two away from the equivalent of the iPhone.
Alphabet is also a dark horse here, with their Glass product. It may have been ridiculed on launch a few years ago, but it didn’t die. Instead Glass has quietly persisted, and has been getting great reviews from serious businesses, like manufacturing and health care, giving them an apparent edge in actually field-testing the glasses concept.
2018 is the year that the rest of the world wakes up to what the tech companies have known for some time now… that a persistent, wearable mixed reality device is the next big thing in hardware. Whoever wins this race will dominate computing platforms in the same way Apple did for the decade following the launch of the iPhone.
Definitely my candidate for the most under-appreciated area of technological progress in the modern economy right now. The last few years in this space have been about getting the surface tolerance and tensile strength of 3D printing to the point where we can make it genuinely useful. That's been a huge success. Quietly, almost unannounced, we’ve moved from plastic figurines to structural airplane parts in the space of about a decade.
We haven't yet seen that make a major impact in manufacturing. The harsh reality is that it’s really hard to move things like 3D printing forward at the same speed as software. Most companies are still using 3D printers for accelerating product development, such as prototyping and for proof of concept. For the technology to be truly disruptive at a commercial level, products need to be flawless and intuitive from the get-go and all engineering, technological, and design problems need to be solved at lightning-quick speeds. That takes huge amounts of research, investment, patience, and iteration, and you can't rush that.
Fortunately, the march of technological progress continues inexorably onwards. We are now tackling some of the fundamental limitations of the technology, such as cost per part, materials we can use, and the removal or avoidance of necessary support structures. All of these make 3D printing cheaper, easier and more applicable.
In the last 12 months, things have gotten really interesting because we've seen more and more production parts made by additive manufacturing. Right now it's performance critical parts that can't be built with traditional methods - for example - turbine parts, rocket engine components, and implants. These have acted as catalysts to start moving the industry from prototyping to manufacturing. And as we climb down the cost curve, an ever greater number of parts that used to be hard to customise, or consisted of multiple assemblies, will start being built with these machines. As 3D printing gets more use in these areas, it’s possible the ship will really begin to sail.
2018 is the year that metal printing becomes a viable option for mainstream designers and engineers. New metal 3D printers cost 10 times less than the previous generation, and are 10 times easier to use. This intersection of low cost and ease of use will drive a wave of adoption in industry. Mechanical engineers are going to become considerably more efficient. There are 2 million mechanical engineers who can design a part in hours, but have been waiting four to six weeks to get that part out of metal. They’re all about to have next-day access. It’s not just metals either. This year we're going to see major advances in 3D printing aluminum alloys, automotive parts, precious metals, ceramics, and the expansion of advanced thermopolymer printing capabilities.
Industrial 3D printing will also expand in 2018 to new countries, making it a truly global disruption. Advances in technology will significantly increase the speed, reliability and capability of 3D production. That's going to start forcing some fundamental questions around supply chains, and will change conventional wisdom like benefits of scale, locations, and schedules for manufacturing, supply chains, spare parts, or maintenance. Do we need to make shoes in China for US consumption or can they be 3D-printed locally and customized to each foot? Do we need to stock every spare part for a Boeing 727 in every airport in the world? Should it take six months to get a sofa manufactured in China only to see it does not fit in your small studio apartment?
The economics of 3D printing will continue to improve, making digital manufacturing more cost-effective for more types of companies in more places. More companies will enter the 3D materials marketplace, helping to drive innovation, lower costs and increase materials diversity. Commercial applications of 3D printing will become more defined across major industries such automotive, medical and aerospace, making the digital industrial revolution a growing part of our everyday lives.
If you thought 2017 was a big year for robots, wait till you see what’s going to happen in 2018. The prices of the various sensors, and in particular, camera systems and 3D acquisition technologies, have dropped significantly in the last decade (Chris Anderson, the drone pioneer, calls this the “peace dividend of the smartphone wars). New battery technologies and material advances mean that robots are more useful, for longer periods, and 3D printing has improved to the point where it’s easy to design and test prototypes, allowing labs to get their robots out the door a lot more quickly. Robotics labs around the world have now had a few years to take advantage of all of this.
It's not just the hardware. Perhaps more importantly, we're seeing incredible improvements in the software that drives robots, thanks to machine learning. This allows robots to operate in messy, real-world environments and to quickly acquire new skills and capabilities via experience, as opposed to the carefully-controlled conditions and the small set of hand-programmed tasks that have always characterised our machines. That means that it’s now possible for robots to flip burgers, pick up eggs, fold clothes or operate on a factory floor without being dangerous.
Specific robot highlights for me from 2017 were the Minitaur (cute, simple, incredible functionality), Ocado’s packing robots (the future of warehousing) and Boston Dynamics’ backflipping Atlas (WTF). Those robots are a very different breed. Formerly, we got precision from adding tighter motor control or heavier arms. These robots have cheaper, lighter components, are able to handle very flexible tasks, and deal with environments that used to be thought of as impossible. This year, look out for a new class of 'soft' robots that take their inspiration from nature, moving past traditional pneumatics, rotors and servos to more biologically inspired forms.
I think that the biggest robot story of 2018 isn’t going to be technical, but economic and political. Fear of automation is going to go into hyperdrive this year. The tech backlash will move from fake news to lost jobs. Careful though... this has far more to do with media fear mongering than reality. The threat is overblown, because while automation does kill jobs, it also creates new ones. From 2014 to mid-2016 for example, a period in which Amazon added 45,000 robots to its warehouses, it also added nearly 250,000 human workers.
There’s another, perhaps more important point here, which is that automation (and the productivity gains that accompany it) change the nature of the economy itself. One of my favourite recent statistics for example, is that there are more fitness instructors in Australia than engineers. Turns out that as the economy changes, the demand for new kinds of services changes too.
In the digital age, the new jobs aren’t those on assembly lines or behind desks like in the Fordist economy. The jobs with a brighter future are those that involve frequent, direct, routine-breaking interactions with demanding customers. These are now found in food, hospitality, healthcare, childcare, personal care, education, and urban logistics. Nicolas Colin calls this the 'proximity services industry,' and the skills required to thrive in it are excellence in attention, empathy and care. In other words… things that robots will never be able to do. These skills are overlooked as “soft,” but they’re exactly what grease the wheels in the next economy.
Those who talk about robots eliminating human workers are missing the point, and their businesses will suffer for it in the long run. We shouldn’t worry about robots replacing us—rather, we need to focus on how we can work together with machines to improve efficiency and performance. As Tim O’ Reilly says, using technology to do the same thing more cheaply is a dead end. If you’re a business looking to take advantage of automation in 2018, keep his maxim in mind: