Author: Philippe Beaudoin

Anybody who predicts AI will replace artists, that it will direct all our movies and compose all our music should also predict that robots will take over football, basketball, hockey…

Sounds ridiculous, right? It’s because for physical activities we have two words: work and sport. In physical work, only the outcome matters. We are indifferent to the way in which this outcome is achieved. A delivery by drone brings exactly the same value as a delivery by a human driver. It’s not the same when we watch a hockey game. We very much care about the humans pushing the puck around.

For art we don’t have different words. We call relaxation music music and we say that Radiohead plays music. Everyday language doesn’t distinguish between those two things, but they are different. We typically don’t care about the creation process behind relaxation music — as long as it helps us relax. But almost everyone at a Radiohead concert very much cares that the music stems from the intentions, the emotions, and the suffering of real human beings.

This amalgamation of two different things behind the single word art has real consequences. A lot of people who aspire to be artists — to create something intentionally, by distilling their emotions, by tapping into their creativity — have jobs where they make functional art. It scratches the itch a little. An illustrator can put some of their emotions and creativity in their illustration. They can do it even if most people clicking on the Add to Cart button will do so without sparing a thought for the human who made the webpage illustration.

To use the sports analogy, imagine a world where package delivery was done by soccer players getting paid to kick spherical packages around. When robots get invented, all these functional soccer players will lose their jobs. The world would soon have much fewer peopler spending their day playing soccer. Many would be forced to accept jobs they enjoy a lot less.

Where am I going with this argument?

I think what the current world is showing us is that a lot more people aspire to be artists than what the market is willing to pay for art. This mismatch is being partly hidden by the fact that the market asks for a lot of functional art and, today, people dreaming of being artists can scratch their itch by being employed doing functional art.

Unfortunately, more and more aspects of functional art will be automated. It will create pain for countless people who will no longer be able to get paid to do what they love doing. Contrary to previous waves of automation, where manual jobs got automated and people, through education, eventually accessed jobs that were more fulfilling to them, I can’t imagine arguing that people getting paid to do functional art will eventually access jobs that are more meaningful to them.

So what do we do? Should we accept this relentless march of progress even if it causes more and more people to have less fulfilling jobs?

Or do we remember that it’s our world, our society, and that we can decide how we shape it?

My friend Sherif Elsayed-Ali wrote:

What should a city built in 2050 look like? They should live in harmony with the natural environment – meshed into it rather than replacing it.

This is close to one of my core belief. That the real value of AI is to enable automation that can scale while respecting the organic and messy nature of our environment.

The industrial era gave us the ability to scale. That is, the ability to build systems (Ie. collections of machines and humans) that can increase their output by a factor X while increasing the number of humans involved by a smaller factor Y.

Humanity gained an amazing power, but it came at a cost: the machines were dumb and needed a formatted environment to do their jobs efficiently. Building a factory is a two step process: first format the environment by building a huge metal box, then pack it with dumb machines that can work their scaling magic away from the organic messiness of the world.

Agriculture is the same: clear up a huge plot of land and structure it cleanly by planting stuff in neat rows that can be tended to by our dumb machines.

Another example. Prior to cars our cities were a lot more organic, with curved streets wide and narrow meandering around. Modern ones have been formatted to maximize our ability to automate them with dumb machines — cars.

AI changes all that.

Suddenly we no longer need the formatting phase. We can achieve scaling by training an intelligent machine to operate within our natural, messy, and organic environment.

We can build factories and cities that keep their efficiencies while meshing with the environment. For agriculture it’s even better: we can embrace the efficiency of nature (the way a messy collection of crops can complement each other to increase yield) instead of fighting against it.

It’s hard to think in this way. The idea that efficiency requires a formated environment is deeply ingrained in our mentalities. Therefore, today we’re thinking of using AI to automate the messy parts of our processes — the formatting phase, or the steps where our formatted environment has to interact with the non-formatted world around it.

Soon, however, we’ll take a step back, take a holistic look at our systems, and realize we can skip more and more of the formatting phase without losing the scaling efficiency.

We’ll realize that we can often do that today, with our current level of AI.

The image below was taken by Spaceship Voyager 1 in 1990. It was 6.4 billion kilometers from us. It’s called the pale blue dot in honor of that little blue pixel you see in the center of a ray of light. It inspired astrophysicist Carl Sagan, who wrote the following:

Our posturings, our imagined self-importance, the delusion that we have some privileged position in the Universe, are challenged by this point of pale light. Our planet is a lonely speck in the great enveloping cosmic dark. In our obscurity, in all this vastness, there is no hint that help will come from elsewhere to save us from ourselves.

Let me use cruder words.

The pale blue dot doesn’t give a shit.

We’re facing an array of global challenges and we have to fix this ourselves, we have to fix this together, and we have to fix this now. We wont fix anything if we wait for the singularity to emerge and save us all. We wont fix anything if we sit back and hope for someone else to fix it for us.

We’ll fix this if we roll up our sleeves and find a way to work together. To understand together what is broken. To go back to the basics, to the tools we have for probing reality and understanding what’s broken. To science.

We need to use science to write stories of hope and wonder. Beautiful stories that get people to regroup, no matter where they come from, no matter what they look like, no matter what they believe in.
And we can do this. We have the tools to do this. Not in a hundred years, but today.

We’ve got the tools, today, to embrace the complexity in which the world thrives.

Here’s to a New Year where we do this, together, a bit more.

(Adapted from a keynote I gave at Element AI a few years ago.)

This story was originally published on Product Hunt.

In 2016, I co-founded Element AI. In 2017 we raised $100M. It went weird.

Peak Zeitgeist

We hit peak zeitgeist. AI was super hot. Many investors believed that early AI would be a talent game. Montreal — where Element AI was headquartered — was seen as one of the largest untapped beds of AI talent.

In the months between December 2016 (our seed) and June 2017 (our Series A), we had executed super well on the talent game. We had signed a lot of big logos who wanted to undergo an AI transformation. We had built a world-class network of academics and managed to actively engage them on consulting engagement with these big customers. More importantly, we did it with enough collegiality that researchers enjoyed the time they spent discussing with us — and amongst themselves — around our customer’s problems.

Talent Tsunami

We managed to translate researcher engagement into hiring momentum. Very talented AI researchers and AI engineers heard of the exciting discussions that were going on between top researchers in our meeting rooms. Some of them were stuck in data science jobs, some were at Google or Facebook but dreamt of having more impact.

We brought them in.

At that point, any C-level of any large corporation who walked into Element AI went into jaw-drop mode. They saw equations doodled on the windows in the kitchen, they heard researchers discuss the latest deep learning result. They saw the kind of energy they dreamt of having in-house but that they just couldn’t get.

We closed them.

We became the fastest growing startup in Canada.

Customers Everywhere

We had so many marquee customers. I gave talks all around the world. I found myself on a stage next to Andrew Ng, only him and I, addressing a room of hundreds of Hyundai executives. That’s where I learned that jokes don’t survive live translation.

We had an exceptional team of consultants. Ex-McKinsey. Ex-Deloitte. They looked top-notch in meetings. They could wow our customers by bringing in the brightest AI minds at a moment’s notice. We delivered.

We opened offices in Toronto, in London, in Singapore, in Seoul.

Too Much of a Good Thing

Then there were the products. We had a lot of great initiatives. We built infra to help our researchers develop and train their models more efficiently. We built a solid information platform using top-notch NLP, a top-of-the-line visual text processing tool, a product using AI to help manage user permissions in large organizations… Lots of potentially very useful products.

But the problem was that none of these products had the traction our consulting group had. We tried to align our consulting outreach with the products we were building, but there was always this new customer that was too big to pass and that we just had to accept.

We had incredibly talented and driven people. People who wanted to bring value to the organization. Only, that value came mostly from consulting. This led to internal tensions. On one side you had those who believed success would come from products and asked for more freedom to build them. On the other, you had those who wanted to bring in revenue and asked for engineers and researchers to work on consulting engagements.

The Exit

That tension became really hard to manage. After 4 years, it’s the kind of thing you could feel in the air, just walking through our gorgeous headquarters in the center of Montreal’s Quartier de l’IA.

On March 13th 2020, the day the world shut-down, I caught the last flight out of London Heathrow. This was my last business trip with Element AI.

I had decided to follow my dream and found Waverly. A product-centric startup. Yes, yes, AI. Yes, deep tech. Researchers, all of that. But first: a product. First, value that can scale. Real traction. Something small that could grow, grow, and grow.

A year later Element AI was sold to Service Now.

My Takeaway

I loved my journey at Element AI. I loved my coworkers. This was the best team I ever worked with. They had the brains of my Google coworkers and the grit of entrepreneurs.

As founders, I think we took a reasonable bet. I still think that talent was key. I believe we executed it well. It was bold, and being bold was not our mistake.

Our mistake was not seeing the tensions that could come from having too much money. Money gave us the ability to bring in the best customers, but it did not pressure us into building products. Building products and despairing because they’re not finding traction. Building products and testing them, pivoting them, validating them…

That’s why I’m posting this on ProductHunt. Because you understand what it means to relentlessly push and pivot a product.

That’s what I want to do now, with Waverly, and I know you can help me.

Update

The original publication of this article has generated a lot of discussions, I encourage you to read the very thoughtful response of Jerome Pasquero, former PM at Element AI.

Picture the scene. You walk into your small neighborhood coffee shop in the morning. The barista smiles at you from above his espresso machine and mouths “Flat white?” You answer with a smile and moments later you’re sipping your favorite drink.

It makes you feel at home. It makes you feel you’re amongst friends. That’s the kind of experiences we love.

Now picture another scene. Your car started making weird noises so you drive it to the garage, a place you’ve never been to. The mechanic asks you a series of technical questions. You stumble through the answers in a way that makes it pretty clear you don’t know much about cars. A few hours later the mechanic calls you back with a long list of things that need to be fixed and asks you to approve an expensive bill.

Not such a cool experience, now, is it? Makes you wonder if the mechanic somehow figured out that cars were not your forte and is trying to slip you a couple of unnecessary fixes.

These two experiences illustrate the difference between someone who knows you, versus someone who’s trying to profile you.

The concepts of knowing you or profiling you are similar in many ways but they differ in one key aspect. Someone trying to know you better engages in candid conversations. They happily let you know what they’ve learned about you. Their behavior makes it obvious that they’re not trying to extract information without you knowing. They allow you to set boundaries and keep some things for yourself.

Someone who tries to profile you does all the opposite. They watch you without you knowing. They infer things about you from your behavior but they won’t let you know what they’ve learned. Profiling happens in the shadows. It doesn’t let you choose what you’re willing to share or not.

There’s a word that captures that key difference between knowing someone and profiling someone: transparency.

The reason transparency is so important is that it allows the emergence of a trusted relationship. The candid conversations you have with your barista install a level of confidence. They allow you to trust that they want to know you in order to serve you better rather than to serve their own interests. Sure, it’s all about conducting a business transaction, but it’s one where you can be more confident that the goal is not to take advantage of you.

Now let’s transpose the scene to the online world. When you shop on the web, the site you visit tries to present you with a personalized item selection. Do they achieve that personalization by knowing you or by profiling you?

With traditional recommender systems there’s very little transparency as to what the online vendor knows or doesn’t know about you. Their knowledge is built by observing the log of your interactions, not on a history of candid conversations. This makes it hard for trust to emerge.

The personalized online shopping experience looks like a barista who knows your favorite coffee, but it feels like a mechanic trying to take advantage of you.

The feeling of being profiled also exists on our social networks. The order in which the reels are presented on TikTok clearly reflects some knowledge of our preferences, but we’re never offered an opportunity to learn what the system knows about us. We don’t have ways to set boundaries on that knowledge. We don’t have any mechanism through which we could build a trustworthy relationship with our social platforms.

These platforms are not trying to trick us with an inflated garage bill, but they are trying to steal our attention. They are trying to get us to keep watching their content… And when we regret spending too much time on their feeds they give us a poor excuse: “You can shut down the app at any time.”

It doesn’t have to be this way. At Waverly, we’ve built a new type of recommender system. One that is all about transparency. A platform that aims to know you. Slowly, though candid conversations, by letting you set boundaries whenever you want, by forgetting anything you want it to forget. A platform that work for you, with your best interests at heart.