Waymo and Uber just announced a partnership that will bring Waymo's autonomous vehicles to the Uber app in Austin and Atlanta. Notably, this is an exclusive partnership, meaning the only way you'll be able to summon a Waymo vehicle in these cities will be through Uber.

The people who follow this space closely, people like Reilly Brennan of Trucks (VC) and Harry Campbell (The Rideshare Guy), think this is a really big deal for a number of reasons.

One, it signals a bifurcation in the industry where there will be companies, like Waymo, that supply autonomous vehicles, and companies, like Uber, that operate them and manage the overall ride hailing marketplace. As part of this deal, Uber is going to handle all of the maintenance and cleaning of the vehicles. This split is similar to the airline industry.

Two, it suggests, and this is Harry's argument, that Waymo needs Uber more than Uber needs Waymo. One of the reasons for this is that a 100% AV fleet is simply too expensive to operate if you're solving for peak demand loads. Because during off-peak times, you then need to pay for downtime.

Uber, on the other hand, doesn't pay for downtime with its human drivers. Most of its drivers are part-time and only plug in when they want to or when the surge pricing becomes too attractive to pass up. So they're the perfect compliment to an AV fleet. Harry argues that this is part of Uber's competitive moat.

And three, it signals that AVs are really starting to arrive, if not already here. The hype cycle certainly hit its trough of disillusionment and everyone switched to thinking that AVs weren't going to happen for many years, if not decades. But now it's happening. City by city.

Construction is an essential sector of the economy, responsible for building and maintaining the physical infrastructure that underpins our society. However, it's no secret that construction productivity lags behind other sectors of the economy, such as manufacturing and information technology. So why is this the case?

One of the main reasons for the productivity gap is the unique nature of the construction industry. Unlike other sectors, construction projects are often one-off, bespoke endeavors, making it challenging to achieve the economies of scale that are typical of manufacturing or technology. Each project requires a different set of skills, tools, and materials, which can be costly and time-consuming to source and manage. This leads to a lack of standardization and efficiency, which can hinder productivity.

Another factor that contributes to low productivity in construction is the reliance on manual labor. Despite the increasing use of technology and automation, much of the work in construction still relies on physical labor, which is subject to human limitations and the potential for errors. This can result in delays, rework, and additional costs, all of which impact productivity.

Moreover, the construction industry faces challenges in terms of supply chain management and workforce development. The industry relies heavily on a complex network of suppliers, subcontractors, and laborers, all of whom must be coordinated and managed effectively. This can be difficult, particularly in light of the current labor shortage and skills gap in the industry.

To address these challenges, the construction industry needs to embrace innovation and new technologies to improve efficiency, standardize processes, and reduce waste. There is also a need to invest in workforce development and training to upskill the existing workforce and attract new talent to the industry.

In conclusion, the construction industry faces unique challenges that make it challenging to achieve the productivity gains that are typical of other sectors. However, with the right investments in technology, training, and process improvement, the industry can overcome these challenges and continue to build the infrastructure that our society relies on.

Maybe you didn't notice. But if the above doesn't sound like me and my writing, it's because today's blog post is brought to you by ChatGPT (AI). The prompt I used was, "write a short blog post about why construction productivity lags other sectors of the economy."

On some level, it's unsettling that AI can now, almost instantaneously, spit out a blog post like this. It would now be pretty easy to set up a daily blog, like this one here, and use ChatGPT to populate it each day.

But of course, while that might be interesting initially, it would quickly become a banal baseline. Anyone and everyone could copy what you're doing. AI is going to change a lot. But our jobs remain the same: find new ways to create value and be remarkable.

Machine learning is one of the most important trends in tech right now. But like anything new, it naturally raises a number of important questions and concerns. Benedict Evan's most recent blog post provides a good explanation of what he refers to as the artificial intelligence bias. Here are a couple of excerpts that I found interesting.

What machine learning does:

With machine learning, we don’t use hand-written rules to recognise X or Y. Instead, we take a thousand examples of X and a thousand examples of Y, and we get the computer to build a model based on statistical analysis of those examples. Then we can give that model a new data point and it says, with a given degree of accuracy, whether it fits example set X or example set Y. Machine learning uses data to generate a model, rather than a human being writing the model. This produces startlingly good results, particularly for recognition or pattern-finding problems, and this is the reason why the whole tech industry is being remade around machine learning.

The rub:

However, there’s a catch. In the real world, your thousand (or hundred thousand, or million) examples of X and Y also contain A, B, J, L, O, R, and P. Those may not be evenly distributed, and they may be prominent enough that the system pays more attention to L and R than it does to X.

What AI isn't:

I often think that the term ‘artificial intelligence’ is deeply unhelpful in conversations like this. It creates the largely false impression that we have actually created, well, intelligence - that we are somehow on a path to HAL 9000 or Skynet - towards something that actually understands. We aren’t.

The conclusion:

Hence, it is completely false to say that ‘AI is maths, so it cannot be biased’. But it is equally false to say that ML is ‘inherently biased’. ML finds patterns in data - what patterns depends on the data, and the data is up to us, and what we do with it is up to us. Machine learning is much better at doing certain things than people, just as a dog is much better at finding drugs than people, but you wouldn’t convict someone on a dog’s evidence. And dogs are much more intelligent than any machine learning.

Photo by Ales Nesetril on Unsplash