Why AI could be the saviour of public transport – if we let it

When ChatGPT first came out, I asked it about my family.

My dad was reasonably well-known but not my mum. Initially, ChatGPT denied that my mum had even existed. When I pressed, however, it co-operatively relented and generated an entire biography.

It was brilliant – and entirely made up. I don’t have a sister, my mum is not a professional translator and certainly isn’t the author of “The Night I Danced with Rommel”.

*My mum’s biography as imagined by early ChatGPT*

Being honest, ChatGPT’s fictional Thomas’s mum is a lot cooler than the real one.

Because Large Language Models have a tendency to spout intelligent-sounding nonsense, some people think they’re not useful.

That’s short-sighted: Boris Johnson also has a tendency to spout intelligent-sounding nonsense, and we made him Prime Minister.

The reality is that this is the first time AI has lived up to its hype, and its going to transform multiple industries.

If we let it, it could be the saviour of ours.

What’s the problem?

Public transport has two big issues:

1) It’s too expensive

2) It’s not convenient

I’m being brutally honest here.

In my spare time, my wife and I use our CarefreeCarfree blog to help convince people it’s possible to live without a car. But for most people, public transport just isn’t good enough.

AI has the potential to transform both these issues.

To understand how, let me do some futurology and tell you how I think AI could pan out in transport, if we want it to. As these are changes that will be happening in other sectors, if we resist these changes, we will fall further behind. It could be terminal for our sector. But if we embrace this change, it could be trigger for public transport becoming the mainstream travel choice for the first time since World War Two.

Obviously, I’m wrong.

No-one can predict the future, which is why our whole approach to business cases is flawed. But hopefully I’ll be close enough to be a useful indication of what’s coming

The Hype Cycle

Before I get into the impact of AI, though, let’s just address the elephant in the room: will anything happen at all? After all, AI’s been promised for a very long time, and not much has happened.

Well, there are two reasons I think that’s wrong.

The first is that AI is here, now. As Mustafa Suleyman, founder of DeepMind, puts it “AI is ‘what computers can’t do.’ Once they can, it’s just software.”

Like you, I can check my PIN number on my phone using facial recognition, have Gmail autocomplete by emails for me and - as I wrote on this blog previously - startups are using an increasing range of AI-powered applications.

But this isn’t the main reason I’m convinced AI is about to do big things.

As a former startup founder, I have a lot of startup founders in my friendship group. Every single one of them (100%!) is either working on an AI startup or is planning one. Not all of them will succeed. But then, I’m not friends with all the startup founders. If my friendship group is in any way representative, in five years time, we’re going to be living through an explosion in new AI products covering every business sector.

Including ours.

Here’s what I think that means…

What AI will do to the organisation

“Your job won’t be taken by AI, it’ll be taken by someone using AI”

Whole swathes of work are about to be transformed by AI.

The types of work undertaken by teams often called Business Services (HR, finance, legal) are the types of work that generative AI will be able to do instantly and effectively.

This is where a lot of people worry about accuracy. However, AI will not be unsupervised. Humans will be in the loop. But a single HR person will be supervising, instructing and interacting with an AI, which is doing the actual work. It’ll be a bit like being able to recruit an unlimited number of HR junior admin folk or paralegals. They’ll still need keeping an eye on but they have unlimited productivity.

Head offices will become much cheaper.

Service will be better

Customer Service jobs will still exist but the customer service agent will be supervising the AIs that do most of the work of interacting with customers. Early on, in risk averse sectors like ours, AI will be making suggestions to the agent, as it will take time for the AI to learn what good looks like (other sectors have already let the AIs loose). After a while, the organisation will be confident to let the AI interact directly with customers, and for the AI to flag the edge cases that need human supervision.

The company will have no choice on this: because customers will also have their own AIs, the volumes of correspondence will increase exponentially. Every delayed journey, every bad experience will automatically generate a compensation claim, so companies that aren’t ready will be swamped.

But if that sounds depressing, let me cheer you up.

Internal training will cease to be penitential. No more tedious online courses for the many, with personalised (expensive) training for the elite (directors and graduates). Instead, everyone will get personalised, highly specific lifelong learning to help them maximise their own skills and capabilities. Lifelong learning will be real, for the first time, with everyone working with their own personalised development AI coach.

Processes will be streamlined

Transport is bureaucratic. The bureaucratic processes will continue but the forms will be populated by AI, working to human instruction. Traffic orders, environmental impact assessments, regulatory submissions: all the paperwork that lubricates our industry will filled in by AI not by people.

Some people will deny this and say that it’s not going to be possible to trust the AIs. But that’s because people are comparing it to ChatGPT, which is designed to always come up with a plausible answer.

These AIs will be highly controlled and will flag to a human when they aren’t confident that they know what to do.

Often the entire interaction between two organisations will be between their AIs, with humans barely involved.

Those of you thinking “I’d never trust a computer to do this stuff”, imagine a steam train driver being put into a class 700. When driving a steam train, our driver, personally, pulled every lever and through his own muscle power physically controlled the engine. That guy would never trust a class 700, in which the console in the cab simply sends requests to a computer, which drives the train on the driver’s behalf.

But he’d be wrong – we’re not going back to steam, and we’re not going back to manually written traffic orders either.

A transformation in technology

AI will transform organisations’ relationships with technology in the way that Excel transformed its relationship with finance.

Before Excel, finance was a specialist function. Today, it is expected that everyone will work with numbers. Excel made that change possible. There are few teams in few organisations that today don’t have an Excel whizz, able to do calculations and simple models on behalf of their team.

An organisation that went back to the days in which complex calculations were the preserve of a group of elite specialists would be seen as bizarre.

In the same way, AI will enable every team to become its own technology function.

The reason is that AI enables computers to be coded in plain English for the first time.

To explain: computers actually work through binary code: strings of 1s and 0s, representing open and closed transistors. As it’s beyond human capability to programme using just strings of 1s and 0s, programming languages exist which enable a human to write in English and the language translates their words into binary computer code.

However, when I say, “English”, it’s not the English you and I speak. Here’s a small chunk of the language “Python”:

That’s a language for specialists, which is why technology firms and technology teams exist.

But AI enables humans to write in standard English, and for AI to translate the meaning into computer code. This is a spectacular transformation in the way technology is created. Suddenly the barrier that used to exist between a “developer” and an ordinary member of staff will evaporate.

Now, obviously, complex computer code will best be written by a specialist. But it will be more like finance is today: anyone can work with numbers but specialists will do the complex stuff.

Traditional IT departments will need to adapt to their new role. Organisations whose IT departments try to retain power will stifle their companies’ progress. Customers will expect companies’ technology to be dynamic, flexible and responsive. Teams will expect to be able to create their own technology products, with the role of IT being to support them and make it happen.

The Central Brain

Luckily, IT teams will not be under-employed. Because while they will lose their exclusivity as technology creators, they will gain a role as a gatekeeper of the company’s ‘central brain’. Increasingly, colleagues won’t be using email or Teams to communicate with each other – they’ll be stages in semi-automated workflows coordinated and managed by the AI central brain of the company. That central brain will be responsible for taking business processes from start to finish, inviting human input as and when required.

Now, I should be clear that what I’m describing here is the end of a journey. AI adoption will take time. Sectors like ours are rightly being careful, as there are many risks. The distinction between careful and conservative is a fine one, but important to get right. Caution: good. Rejectionism: risky.

What this all means…

Organisations will be able to do a lot more with the same number of people, or what they do now with many fewer.

This has triggered a jobs panic. I confidently predict that - across the economy - the answer will be “a lot more with the same number of people”. Why? Because every single technology efficiency has resulted in more work, not fewer people.

Just think what our grandparents would have said if we’d told them that, by the time their grandchildren were adults, energy could be generated without coalminers, documents produced without typists and production lines staffed by robots. They’d believe the jobs apocalypse would already have happened. Yet Britain is currently suffering a labour shortage.

That’s because our grandparents didn’t know about baristas.

As work is automated, work that never used to exist becomes a thing.

I can’t predict what that work will be, any more than I’d have been able to convince my grandad that it would one day be unacceptable to serve a coffee without first drawing a leaf on it, and that as many people would be employed to draw leaves in coffee as work in steel mills.

But some new work will emerge to replace the work that was lost. It always does.

What AI will do to TRANSPORT

A transformation in reach

Because it’s expensive to operate, public transport is niche. Only a tiny proportion of Brits live within walking distance of high-frequency public transport (I’m lucky enough to be one of them). If you do have frequent public transport accessible locally, you choose to use it. That’s why most journeys in London are made by public transport but not most journeys in Leicester.

The problem is that providing high-frequency services to most locations is unaffordable. The main driver of the cost is the driver. Take that out, and the position is transformed.

Now, I’m not saying that we’ll take the driver from existing services. Most services currently operate with one human and a large number of passengers (as services with few passengers are unviable). One member of staff is probably the lower acceptable limit. That’s why both the DLR and Victoria line have operated with staff on board since they opened decades ago, even though the trains have always been automatic.

But what driverless public transport will enable is an extraordinary expansion of service to places that are unaffordable when staffed, but affordable when automated.

It’s not about cars

There’s a lot of focus on autonomous cars. However, the main problem with cars is physics: they take up too much space. The logic of this famous 1960s advert still holds true:

Autonomous cars have the potential to create gridlock. If people like me who don’t drive start taking autonomous cars, no-one will ever move. AI can optimise but it can’t solve impenetrability: the law of physics that states that only one object can occupy a physical space at a time. Only one car can occupy each space on the road. Autonomous or otherwise, cars have a percentage point of the capacity of buses.

Moreover, just with other forms of AI, autonomous vehicles will continue to make mistakes. That’s why it’s taking so long to get beyond a few robotaxis in two American cities to global adoption. But public transport autonomous vehicles can have the backup of a room full of video operators. One operator can be responsible for escalations from hundreds of vehicles. If the vehicle isn’t sure what it can safely do in a particular situation, it can propose a solution to a human and the human can check.

A shuttle that stays within a defined suburb will have far fewer issues than a Tesla that needs to know every streetscape on Earth.

That means that each suburb, town and village can be connected by high-frequency autonomous shuttles to the core public transport network. This would be transformational. And it will become possible.

The reason is that it will be possible is the cost of public transport without the cost of fuel or drivers (obviously these vehicles will be electric) is a fraction of the current cost. Imagine the places that it will be possible to profitably connect! If public transport connectivity can be provided for £1 per vehicle mile, the density of the network will transform. As the peripheral network becomes larger, the core network becomes stronger. It’s the opposite of the Beeching Cuts. I can’t wait.

My only slight worry is that I don’t see as much progress being made towards making it happen as I’d like. I’ll be honest, we didn’t make as much progress at TfL as I’d have liked. The only city that’s doing this seriously and properly is Oslo, under the leadership of visionary Ruter CEO Bernt Reiten Jenssen. Take a listen to him on the Freewheeling Podcast. We all need to catch up.

But the cars are coming

However, we cannot take our eye off the ball when it comes to autonomous vehicles. I have long predicted that it will take a very long time to get to Level 5 autonomy: when a car can go anywhere without anyone in the driving seat. It’s not because the technology isn’t there to make it happen most of the time. It’s because the consequences of failure are so great.

But, equally, autonomous cars are already live in several US cities, carrying passengers.

We need to be alert that Elon Musk and his fellow travellers don’t try to pull of the same trick that their predecessors did almost exactly a century ago. Back in the 1920s, car execs realised that it would be easier to get their product off the ground if cities were better designed for cars. So they persuaded councils to put fences along pavements and to corall pedestrians onto fixed crossing points. You can see a similar dynamic playing out with autonomous.

*Do we trust him with the future of our cities?*

Is it a bird, is a plane, not it’s a taxi

AI-driven cars also create the risk that the 'taxi’ product becomes a much more significant competitor. Tesla wants to enable you to let your Tesla act as a self-driven taxi during its downtime. Indeed, one of the characteristics of autonomous vehicles is that it becomes much less clear what is a bus, what is a taxi, what is a car, what is a private hire vehicle. There’s a family of seven down the road from us who have a thing they call a van. But you could call it a small bus. If they send it out to carry fare-paying passengers while they’re at home watching Googlebox, what is it exactly?

There is a significant risk of new competition from vehicles that are more sustainable than a petrol car but a lot less sustainable than sustainable transport.

Better, and cheaper

It’s therefore crucial that our core network becomes cheaper. It won’t become cheaper because we will take out the drivers from busy services. We won’t. But the network will become cheaper.

AI will be able to optimise schedules both in advance and in real-time.

What’s the best roster for all the buses and trains in Kent to balance cost and revenue? Like all models, AI’s answer will depend on assumptions (elasticities will remain as key as ever). But it’ll have the capability to try to answer the question. Today it’s a question we don’t even try to answer.

You cannot be serious!

Some of you may doubt what I’m saying.

You’d be right to.

I mean, obviously, I’m wrong.

But I’m not going to be a million miles wrong. The core capabilities of AI are now pretty clear. So the only question is whether they will scale to the extent I’m describing.

And I think we got the answer to that last month when Google announced it was building its own private nuclear reactors to generate its own energy to power the data centres needed for AI. That’s how much power they think they’ll be using. That’s because they’re going to be doing… well… the stuff I’m talking about.

So I’m definitely wrong in the detail. But I’m not wrong that it’s another big transition, like the internet.

What about the planet?

Many of you may worry about the sustainability implications of all this.

I certainly do.

After all, Google may be building nuclear reactors but not every AI company is thinking about the energy they generate. I asked the founder of one AI startup how he thought about the carbon implications of what he was doing and he literally hadn’t thought about it at all.

The problem is that this article isn’t what I want to be happening: it’s what is happening.

I can’t prevent AI any more than Canute could turn back the tide.

My specialism is transport and mobility, so all I can do is try to minimise the carbon emission in our sector.

And my assertion is that we can deliver a better outcome for carbon by using AI to make sustainable transport superb than by allowing ourselves to be overtaken by Elon Musk (etc) who don’t give a damn about sustainability.

In that regard, AI feels very different to the previous carbon tech horror: crypto. AI, if we get it right, is useful. Bitcoin is not.

This is big

To give you a sense of the power of AI, a modern AI model uses 10 billion petaFLOPS of computing power to train. (This isn’t a prediction - it’s reality today.)

FLOPS and PetaFLOPS aren’t language we’re familiar with (FLOPS stands for floating-point operations per second). It’s a measure of computing power.

Just saying that 10 billion petaFLOPS is a huge amount of computing power is pretty meaningless. So let me help you try to visualise what it looks like.

Imagine doing a maths calculation on a pocket calculator. Visualise that? OK, that’s about 20 FLOPs. Now imagine you’re a real whizz at multi-tasking, so you’re simultaneously doing a maths calculation on 50 pocket calculators at the same time. I appreciate this might be harder to visualise. Now imagine that every human on earth is simultaneously doing a maths calculation on 50 pocket calculators each. Now imagine that every planet in the solar system is populated with as many humans as Earth, and they are all doing a maths calculation on 50 calculators each.

I realise we’re getting beyond the usual “size of Wales” “number of double deck buses” or “area of football pitches” scales here. Sorry. There’s a lot of computing power in AI. And I’m afraid this visualisation is about to get worse.

You see, and bear with me, but I’m going to have to ask you not to operate 50 pocket calculators but seven trillion. Soz.

If all your seven trillion pocket calculators were piled on end, they would stretch out from your hand to the sun. And back. Six times. So I’m afraid you might struggle a tad to operate them all at once. But you’re game for a go, right?

So, there we have it, if you imagine you and everyone else on Earth (plus an equivalent number of people on every other planet) all simultaneously doing a mathematical calculation on a pile of pocket calculators that each stretches from Earth to the sun and back six times, that is how much computing power is used to train a modern AI model.

I don’t know about you, but I think that’s gonna have an impact on things.

Any downsides?

Other than the fact AI requires so much energy that the biggest providers are having to build nuclear power stations?

Oh my goodness, yes. AI has the potential regurgitate our own biases, to build enormous dependency on hackable computers and to blur the lines of organisational accountability. I feel it daily in the most basic way: as a self-employed consultant, I’m dependent on the LinkedIn algorithm. I have no influence over whether my posts are seen by 100 people or 100,000.

My point isn’t that AI is good. My point is that AI is happening.

Our job is to get the best we can out of it.

We’re not good at this

Now, I’m afraid I need to come to the tricky part. We don’t have a great track record of getting the most from technology. In fact, we’re one of the only sectors I can think of where technology and automation make things more expensive.

When signalling systems moved the skill first from the cab to the signalbox and then to the software developer’s studio, we incurred all of the costs of modern signalling but carried on paying the drivers as if the job was as skilled as it had been before. The London Underground’s Four Lines Modernisation (to automate the sub-surface network) has so far cost the taxpayer £5 billion in technology costs. Yet there is no expectation that, once the trains drive themselves, future tube drivers will be recruited at a salary that reflects the reduced skill level.

When the e-commerce revolution hit transport, we incurred the costs of online retailing. And we kept booking offices open. And we have more ticket vending machines than ever. And dissatisfied customers.

In general, automation allows us to add new costs, retain the old costs and fail to deliver consumer benefits.

Now, this is a problem given what’s about to happen.

I’m not blaming anyone else for this. I’ve been a director of transport companies since I was 26. I carry as much blame as anyone, and more than most. Collectively, we haven’t fixed this.

But we’ve got to. One of the reasons I created Freewheeling is that I can see this coming and feel like it needs an outsider championing the change. When I was at TfL, I found it surprisingly hard to have this conversation. When I was on the inside doing the job I did then, I really needed someone like me on the outside.

The AI revolution is going to transform every sector, upend customer expectations and revolutionise the workplace. If anyone doubts that, ask yourself this: for which sector AIs is Google building nuclear reactors to power AI? If it’s for every sector but transport, that’s just as big a problem as if the revolution is coming to transport.

But we shouldn’t see it as a threat. If we get it right, it will usher in a new golden century for public transport. Get it wrong, and we’re dinosaurs.

If you’ve found this post interesting (even if you don’t agree with every word), I’d love it if you would share it. It’s a debate we need to start.

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