Surprise! Your usual host, the lovely Nick O’Connor, is out today. Well, he’s preparing for a sit-down interview with Eoin Treacy (from Trigger Point Trader and Frontier Tech Investor). Eoin’s flown over from Trump-ravaged America and has a hot new trade he wants everyone to know about.
Nick can fill you in tomorrow. Meanwhile, today’s letter will focus on the accelerating pace of change in the tech sector. Frankly it’s giving me the creeps: robots that are teaching themselves how to walk and then “evolving” the capacity for self-replication. But heebie-jeebies aside, you shouldn’t ignore genuine opportunities to make money.
First, though, let’s deal with “capital”. Did you see that Microsoft sold $17 billion worth of bonds on Monday? The US tech giant is one of just two US corporates with an AAA credit rating (the other is Johnson & Johnson… with ExxonMobil having been downgraded last year thanks to the waning fortunes of petroleum). Microsoft borrowed nearly $20 billion last year to buy LinkedIn.
Borrowing before the bond bust?
There are a couple of interesting points about this. First, you have to wonder if this is a sign that interest rates really are heading up in 2017 and that further carnage in the bond market is imminent. The Federal Reserve says that it will raise rates (three times). And if it does, it raises corporate borrowing costs, which is why it makes sense for Microsoft to lock the low rates in now.
By the way, the company borrowed across a range of maturities, from three to 40 years. The interesting one, for me, is the ten-year bond. Microsoft’s ten-year bonds (issued yesterday) yield 3.3%. A ten-year US Treasury note, by contrast, yields just under 2.5% – that’s after rising from around 1.5% last year.
Here’s a pop quiz: which is the better credit risk over the next ten years? Is it Microsoft or the US government? If Donald Trump is taking a firefighter’s axe to the post-World War Two international order, then AAA-rated corporate borrowers with globalised income streams might very well be a better credit risk than a sovereign power with a $20 trillion deficit and a brewing social meltdown.
Agree? Disagree? Send your vote to email@example.com.
Now back to the capital markets. Both US and European markets were off yesterday. The Dow Jones Industrial Average fell back below 20,000. It was a 122 point fall for the blue chip benchmark, which, to be fair, doesn’t really mean anything. That was a 0.61% fall on the day. A 100 point move in the Dow isn’t what it used to be.
But here’s the real question in capital markets: has Trump’s erratic and fast-paced first week introduced so much uncertainty into the minds of investors that you’ll see a February sell-off? Before you answer that question please look closely at the chart below (especially if you’re a seismologist, volcanologist or a cardiovascular surgeon). What do you see? Look closely.
The CBOE Volatility Index (VIX) hit an all-time closing low of 10.42 in January 2007. It erupted like Pompeii later in the year as the US sub-prime mortgage meltdown hit the investment banks. For the last ten years we’ve been stuck in this monetary no-man’s land of low rates and higher asset prices.
The VIX was up over 12% yesterday. It had hit an intra-day low of 10.33 before then. That was slightly above the intra-day low of 10.32 in July 2014. The obvious question: is a VIX-quake imminent?
I spoke to The Fleet Street Letter investment director Charlie Morris about it on the phone this morning. Charlie said the post-2008 VIX is different than the pre-2008 VIX. More specifically, Charlie says the VIX you see above may not be the best leading indicator of an imminent market sell-off. He said there’s another signal that’s more useful (it was complicated and I’ll have to write about it in a future issue, or have Charlie write about it in The Fleet Street Letter).
In the meantime, the market shrugged off everything Trump said he’d do about trade, immigration, China and Russia. Now that he’s actually doing those things, you may see less shrugging. And more selling.
The robot that evolves itself
Now, set aside your capital market anxiety. Remember that there’s a future out there. And in that future, robots are designing, manufacturing and improving themselves. And now remember that the future is now. It’s called “self-evolution” and here’s how Matthew Griffin describes it:
Experts at the University of Oslo, Norway have discovered a new way for robots to design, evolve and manufacture themselves, without input from humans, using a form of artificial evolution called “Generative design,” and 3D printers – although admittedly the team, for now at least, still has to assemble the final product, robot, when it’s printed.
You can watch a video as the robot tests different ways to move. It looks awkward and again, somewhat creepy yet amusing. But don’t let the awkwardness fool you about what’s going on. What’s going on is really important and could change the world in radical ways. Why?
A “creative machine” is one that can evolve itself. In the natural world, evolution is “dumb”. That is, it is trial and error over huge swathes of time. Mutations are “selected” based on their usefulness to promote the survival of a species. But the mutations themselves are random.
In “generative design”, the evolution is guided by an intelligence. That intelligence learns much faster than you and I. So while its first steps appear awkward and amusing, you shouldn’t forget that it has the capacity to learn at an exponential rate. The baby steps you see now could quickly become giant strides.
But what does that mean? The issue is whether machine intelligence will accelerate the process of innovation itself. If a learning machine/robot can predict outcomes more quickly (and test them) then a lot of things will change. Here’s the key idea from a recent article called “The Simple Economics of Machine Intelligence”:
Technological revolutions tend to involve some important activity becoming cheap, like the cost of communication or finding information. Machine intelligence is, in its essence, a prediction technology, so the economic shift will centre around a drop in the cost of prediction.
The first effect of machine intelligence will be to lower the cost of goods and services that rely on prediction. This matters because prediction is an input to a host of activities including transportation, agriculture, healthcare, energy manufacturing, and retail.
When the cost of any input falls so precipitously, there are two other well-established economic implications. First, we will start using prediction to perform tasks where we previously didn’t. Second, the value of other things that complement prediction will rise.
It may not be obvious what the value of a “prediction technology” is (it wasn’t to me). And therefore, it’s hard to say what industries could benefit (or be destroyed) by lowering the cost of prediction. The two economic implications follow: that the lower cost of prediction will increase its use; and that the value of things that complement prediction will rise. But how about a little more detail from the authors:
As machine intelligence lowers the cost of prediction, we will begin to use it as an input for things for which we never previously did. As a historical example, consider semiconductors, an area of technological advance that caused a significant drop in the cost of a different input: arithmetic. With semiconductors we could calculate cheaply, so activities for which arithmetic was a key input, such as data analysis and accounting, became much cheaper.
However, we also started using the newly cheap arithmetic to solve problems that were not historically arithmetic problems. An example is photography. We shifted from a film-oriented, chemistry-based approach to a digital-oriented, arithmetic-based approach. Other new applications for cheap arithmetic include communications, music, and drug discovery.
The same goes for machine intelligence and prediction. As the cost of prediction falls, not only will activities that were historically prediction-oriented become cheaper — like inventory management and demand forecasting — but we will also use prediction to tackle other problems for which prediction was not historically an input.
Two concrete areas where this will have implications are transportation and healthcare. Connected machines with intelligence will be better at finding the least-crowded, most efficient route between point A and point B. Whether the cargo is a human being or crate of crustaceans doesn’t matter. Getting things from one place to another should become much more efficient.
This is why I’m convinced that in ten years, 90% of the traffic in central London will be self-driving cars and lorries. The other 10% will be people on bikes because they choose to get around that way. Transportation of good, services and people will become commodified. Owning a car will be unnecessary and rare – which is bad news for the big carmakers.
And health? Improved diagnostics (better prediction) coupled with personalised genetic medicine will destroy the bureaucratic and centralised nature of the healthcare system we have. Costs will go down. People will be healthier and live longer (which has its own set of demographic problems).
None of this, by the way, improves human judgement. That is a quality that I think is not perfectible. And that’s the interesting situation you find yourself in as technology advances more rapidly. Machines are getting smarter and better. Human beings appear to be getting dumber and more violent.
Until next time!
Publisher, Southbank Investment Research