# Another AI Winter?

Robin Hanson has been
[suggesting](https://aiimpacts.org/conversation-with-robin-hanson/)
recently that we've been experiencing an AI boom that's not too
different from prior booms.

At the recent [Foresight Vision
Weekend](https://foresight.org/vision-weekend-2019/), he predicted [not exactly - see the comments] a 20%
decline in the number of Deepmind employees over the next year
(Foresight asked all speakers to make a 1-year prediction).

I want to partly agree and partly disagree.

I expect many companies are cluelessly looking into using AI in their
current business, without having a strategy for doing much more than
what simple statistics can do. I'm guessing that that mini-bubble has
peaked.

I expect that hype directed at laymen has peaked, and will be lower for
at least a year. That indicates a bubble of some sort, but not
necessarily anything of much relevance to AI progress. It could reflect
increased secrecy among the leading AI developers. I suspect that some
of the recent publicity reflected a desire among both employers and
employees to show off their competence, in order to attract each
other.

I suspect that the top companies have established their reputations well
enough that they can cut back a bit on this publicity, and focus more on
generating real-world value, where secrecy has some clearer benefits
than is the case with Go programs. OpenAI's attitude about disclosing
[GPT-2](https://openai.com/blog/better-language-models/) weakly hints at
such a trend.

I've [written
before](http://www.bayesianinvestor.com/blog/index.php/2016/08/28/ai-phase-change/)
that the shift in AI research from academia to industry is worth some
attention. I expect that industry feedback mechanisms are faster and
reflect somewhat more wisdom than academic feedback mechanisms. So I'm
guessing that any future AI boom/bust cycles will be shorter than prior
cycles have been, and activity levels will remain a bit closer to the
longer-term trend.

VC funding often has boom/bust cycles. I saw a brief indication of a
bubble there ([Rocket
AI](https://medium.com/the-mission/rocket-ai-2016s-most-notorious-ai-launch-and-the-problem-with-ai-hype-d7908013f8c9))
three years ago. But as far as I can tell, VC AI funding [kept
growing](https://news.crunchbase.com/news/ai-companies-raise-more-money-across-fewer-rounds/)
for two years, then stabilized, with very little of the hype and
excitement that I'd expect from a bubble. I'm puzzled about where VC AI
funding is heading.

Then there's Microsoft's \$1billion [investment in
OpenAI](https://openai.com/blog/microsoft/). That's well outside of
patterns that I'm familiar with. Something is happening here that leaves
me confused.

AI conference attendance shows
[patterns](https://aiimpacts.org/ai-conference-attendance/) that look
fairly consistent with boom and bust cycles. I'm guessing that many
attendees will figure out that they don't have the aptitude to do more
than routine AI-related work, and even if they continue in AI-related
careers, they won't get much out of regular conference attendance.

A much more important measure is the trend in compute used by AI
researchers. Over the past 7 years, that happened in a way that was
[obviously
unsustainable](https://aiimpacts.org/interpreting-ai-compute-trends/).
In some sense, that almost guarantees slower progress in AI over the
next few years. But that doesn't tell us whether the next few years will
see moderate growth in compute used, or a decline. I'm willing to bet
that AI researchers will spend more on compute in 2024 than in 2019.

I invested in NVIDIA in 2017-8 for AI-related reasons, until its
price/earnings ratio got high enough to scare me away. It gets 24% of
its revenues from data centers, and a nontrivial fraction of that seems to be
AI-related. NVIDIA experienced a moderate bubble that ended in 2018,
followed by a slight decline in revenues. Oddly, that boom and decline
were driven by both gaming and data center revenues, and it eludes me
what would synchronize market cycles between those two markets.

What about Deepmind? It looks like one of the most competent AI
companies, and its money comes from a relatively competent parent. I'd
be surprised if the leading companies in an industry experienced
anywhere near as dramatic a bust as do those with below-average
competence. So I'll predict slowing growth, but not decline, for
Deepmind.

The robocar industry is an important example of AI progress that doesn't
look much like a bubble.

This is not really a central example of AI, but clearly depends on
having something AI-like. The software is much more general purpose than
AlphaGo, or anything from prior AI booms.

Where are we in the robocar boom? Close to a takeoff. Waymo has put a
few cars on the road with no driver already. Some of Tesla's customers
are already acting as if Tesla's software is safe enough to be a
driverless car. In an ideal world, the excessive(?) confidence of those
drivers would not be the right path to driverless cars, but if other
approaches are slow, consumer demand for Tesla's software will drive the
robocar transition.

I expect robocars to produce sustained increases in AI-related revenues
over the next decade, but maybe that's not relevant to further
development of AI, except by generating a modest increase in investor
confidence.

Some specific companies in this area might end up looking like bubbles,
but I can't identify them with enough confidence to sell them short.
Uber, Lyft, and Tesla might all be bubbles, but when I try to guess
whether that's the case, I pay approximately zero attention to AI
issues, and a good deal of attention to mundane issues such as risk of
robocar lawsuits, or competitors adopting a better business strategy.

I wish I saw a good way to bet directly that the robocar industry will
take off dramatically within a few years, but the good bets that I see
are only weakly related, mostly along the lines I mentioned in [Peak
Fossil
Fuel](http://www.bayesianinvestor.com/blog/index.php/2017/09/22/peak-fossil-fuel/).
I'm considering a few more bets that are a bit more directly about
robocars, such as shorting auto insurance stocks, but the time doesn't
yet look quite right for that.

Finally, it would be valuable to know whether there's a bubble in AI
safety funding. This area has poor feedback mechanisms compared to
revenue-driven software, so I find it easier to imagine a longer-lasting
decline in funding here.

[MIRI](https://intelligence.org) seems somewhat at risk for reduced
funding over the next few years. I see some risk due to the effects of
cryptocurrency and startup sources of wealth. I don't see the kinds of
discussions of AI risk that would energize more of the MIRI-oriented
donors than we've seen in the past few years.

I'm less clear on FHI's funding. I'm guessing there's more institutional
inertia among FHI's funders, but I can easily imagine that they're
sufficiently influenced by intellectual and/or social fads that FHI will
have some trouble several years from now.

And then there are the safety researchers at places like Deepmind and
OpenAI. I'm puzzled as to how much of that is PR-driven, and how much is
driven by genuine concerns about the risks, so I'm not making any
prediction here.

Note that there are [more AI safety
institutions](https://www.lesswrong.com/posts/SmDziGM9hBjW9DKmf/2019-ai-alignment-literature-review-and-charity-comparison),
most of which I know less about. I'll guess that their funding falls
within the same patterns as the ones I did mention.

In sum, I expect some AI-related growth to continue over the next 5
years or so, with a decent chance that enough AI-related areas will
experience a bust to complicate the picture. I'll say a 50% chance of an
ambiguous answer to the question of whether AI overall experiences a
decline, a 40% chance of a clear no, and a 10% chance of a clear yes.