I’ve been thinking a lot about where AI will have the most impact. Will some industries be more impacted than others? Will certain functions be quicker to adopt AI than others?

Penetration of new technology isn’t usually uniform. Forming a view of where it will happen faster puts you at an advantage for spotting opportunities and getting your timing right. Most emerging economies skipped the PC revolution and went straight to mobile. We had a bunch of exciting products launch to address that — think UPI in India.

AI is useful because it is a 24/7 machine. It doesn’t get tired, and it makes no judgement on the task it is given. This means it’s useful because:

Customer support is an example of a repetitive task that is expensive. Applying AI to customer support helps automate the repetitive stuff. It creates two advantages: 1) lower cost for companies to provide support, and 2) better quality because your support team spends time on the complicated stuff.

Drug discovery is an example of an application where there are too many inputs. Scientists discover drugs using a combination of academic research, trial and error, and experimentation. AI is helpful because it can trawl through more information than a human can. In doing so, it reduces the number of options on the table for the scientist to test and improves drug discovery. Incidentally, Insilico Medicine was the first company to be granted a license by the FDA for a drug discovered and designed using AI.

AI’s speed of penetration will be driven by the following:

One of AI’s biggest short comings today is the hallucination problem. It can sound very right but be very wrong.

There’s a bunch of people working on this — I have no doubt it will be solved with time. However, it does mean that AI will be more useful in some industries vs. others. You do not want people asking ChatGPT for medical advice because if they do, and it gets it wrong, it could lead to a terrible outcome.

On the other hand, if you are writing a marketing email, it’s absolutely fine to get ChatGPT to write your first draft and then iterate.

Jobs where a large portion of the work is done in front of a computer will be impacted first.

When you think about actions AI can automate, it’s obvious that tasks that can be completed using a computer alone (”knowledge tasks”) are easier to automate.

On the flip side, consider a factory. It’s going to take a while until AI can run and optimise a factory. Even if the software existed, plugging it into the hardware in the factory presents a whole new set of challenges.

On a similar note, I suspect we will see regulated industries move slower than others. Consider an industry like healthcare. Before deploying AI in a user facing environment, you will need to get it approved by a bunch of regulators. There is important nuance here — a regulated industry might adopt AI very quickly if a qualified individual remains in the loop. Legal services is the best example of such an industry as we will see below.

Industries where access to a good or service is prohibitively expensive will be disrupted faster. If it costs you $1,000 to do a task today and that task can be automated even partially, it’s going to be disrupted. Someone somewhere will leverage AI to make the task more efficient. The provider of the good or service, over the long-term, has to pass on that benefit to the customer. If not, someone else will eat their lunch.

To summarise, AI will move faster in industries that:

To form some view on the actual industry, I figured two data points would be useful: 1) how many jobs exist in the industry, and 2) what is the size of the industry.

For the former, the best data I could find was from the Bureau of Labour Statistics. The distribution of labour by industry in the USA.

In addition, the graph below shows the contribution of different industries to GDP. Generally speaking, I suspect that we’ll see more impact in the larger industries (more incentive for entrepreneurs to go and build in them).

Let’s get the easy stuff out of the way. “Information” which includes technology is going to move the fastest. They are closest to AI and are usually the first segment to utilise new technology.

The next big industry to be impacted will be professional, scientific and technical services. This sector is the 3rd largest employer and contributor to GDP. Services like accounting or legal are ripe for disruption with AI. Two recent examples to drive this home: Harvey - a legal AI assistant - just signed a huge deal with Allen & Overy, a magic circle law firm in the UK. Bain and Company signed a deal with OpenAI to utilise their AI technology.

Manufacturing presents one of the largest opportunities. It is the biggest segment by value and the second biggest employer. As discussed above, I suspect it will take a bit of time because it’s a more complicated problem space and because companies will take longer to make changes.

Financial services is ripe for disruption. It is a regulated industry but there are many problems that are not strictly regulated and could benefit from AI. Areas where AI could contribute meaningful value: fighting fraud, completing paperwork, investment research etc. All of these activities can provide meaningful value for companies in the space without requiring regulatory approval.

The most impactful industry is likely to be health care. Health care is not accessible in most countries. It is definitely not accessible in the US. AI could help provide first line health care for citizens and several companies are working on this. The challenge, I think, will be convincing regulatory bodies to allow AI to make decisions.

Finally, I think state and local government will move the slowest. The inherent processes and bureaucracy will mean that AI simply does not move as fast.

For folks like me who work in tech, AI feels like it is everywhere. This is to be expected: every person in tech has their eyes on the latest software and how they can leverage it. I do hope people look beyond technology because there are many opportunities to innovate with AI. If you want to get a solution out to market quickly, choose an industry like tech. The con here is that you will face a lot of competition and will need to win distribution.

On the other hand, you want to dedicate several years to building a company, choose an industry that is large and slow to move. Choose manufacturing, healthcare or financial services. It will take longer to get these folks onboard but the scale of impact is very, very large.