RITA GUNTHER MCGRATH: A strategic inflection point, as I define it, is something in the external environment that changes the assumptions upon which a business is based. It's often technology but it doesn't have to be. It could be a change in social norms. It could be a change in a company's reputation. But the reason it's so important to see these things is that we all develop a view of the world and our view of the world is really based on the assumptions that may have been true at one point. But those assumptions guide what we think is going to drive outcomes in our business. When something happens that changes those assumptions all of a sudden it's as though our radar was off, you know. We don't really see what's going on in truth. And the longer an inflection point goes the more wide the gap is between our current understanding of what's happening and what's actually happening.
Now the reason for spotting them early is, firstly, obviously if they're going to change your assumptions you don't want to let that gap persist for too long. But secondly, if you get it right it can take your business to new heights. If you get it wrong it can be very destabilizing. And we've seen so many examples of companies that everybody thought were at the top of their game and within a couple of short years were just out of business or irrelevant or had to dramatically change what they were doing. Blackberry comes to mind in the handset business. They're still there. I mean they're still somewhat relevant but as the driving force in that sector they are no longer really in charge of their own fate. And that's what happens when you get an inflection point wrong.
You can make super smart, very intelligent decisions and still have a bad outcome. You can make horrific, idiotic, stupid decisions and still have a good outcome because that's how unpredictable things are. So I think there is a big distinction between forecasting and what I'm talking about here which is picking up weak signals, opening your mind to different possibilities and having the foresight to say, hey, maybe that's worth putting a small bet or a small investment or maybe it's worth going to that meeting or doing that experiment. I think it's more positioning yourself to be better able to take advantage of the unexpected than it is predicting what's going to happen. I think a lot of people who've made predictions have come to regret that because it's so uncertain. How do you absolutely know for sure what's going to happen?
So a lot of really interesting competitive opportunities are opened up when someone sees a change in the environment and moves to capitalize on it as an opportunity. A great one that is by now pretty well known but I think still illustrates the point nicely is the transition from movies that were sold on cassette tapes to movies that were able to be sold on DVDs. And if you think about it a cassette tape—now you're going to have to go back in history but a cassette tape movie could cost $50 or $60. We've forgotten that by now. We're so used to very inexpensive digital offerings we fail to remember that at the time that Blockbuster, for example, got going their selling point was that for much less than that you could rent the movie for a night or two and enjoy watching it and then return it and then they would re-rent it to other people. And the catch phrase at the time was 'Be kind, rewind.' —rewinding these cassette tapes.
So the beginning of the inflection point that eventually gave rise to Netflix was two things. The first was a technological shift which allowed an entire movie to be published on a DVD. And a DVD was a completely different form factor. It was light, it could fit in an envelope. And even though it was still pretty expensive the costs were already starting to come down relative to what it took to create a product. The second thing that led to Netflix being an inflection point is actually an evolution of digital business models. So the content creation people always had a very rigid system for say movies where the movie was first shown in theaters where people had the highest willingness to pay. Then it kind of went the rental route. Then it kind of went into movies you could have of your own. Then it was residuals. And in the early stages, production companies really didn't have a lot of ways of making money off residuals. Netflix said, hey, you sell us the rights to your residuals and we will pay you. And it became an addictive additional revenue stream for a lot of the content producers.
So that gave Netflix the ability to have all this content, some of which was pretty popular, some of which was pretty niche oriented, to really fuel the beginning of their subscription model. Now as we have seen plenty of people now observing the production companies have regretted that for a long time. And what they're now doing is they're going into competition with Netflix developing their own streaming services. So I think we're yet again seeing this inflection point coming around to possibly undermine Netflix's model where Netflix is now in the business of having to create hugely expensive content in order to keep people on their service, to keep people subscribing.
- Why are companies like Apple on top of the world while others like Blackberry have been relegated to a minor market share? Why is Netflix king and Blockbuster extinct? Netflix spotted a strategic inflection point and capitalized on it, says Rita Gunther McGrath.
- A strategic inflection point is a shift in the external environment that changes the assumptions upon which a business is based—it could be technology, social norms, or a company's reputation.
- People and organizations who see inflection points early and respond to them with a small investment or an experiment have an advantage. They will swim while their competitors may sink.
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Robert Erdmann, a senior scientist working for the Rijksmuseum, cannot help but smile when I ask him to explain — in as much detail as possible — how exactly he used artificial intelligence to recreate long-lost portions of Rembrandt van Rijn's most famous painting, The Night Watch (1642). "Most people just want the elevator pitch," he tells me over Zoom.
The Night Watch is a mammoth of a painting, and it used to be even bigger. In 1715, it came into the possession of the bureaucrats in charge of Amsterdam's Town Hall. In order to fit it on their wall, they sliced off all four outer edges of Rembrandt's priceless masterpiece, inadvertently creating the compromised version we know today.
Rembrandt's "The Night Watch," with the missing edges shown in black.
The missing pieces of "The Night Watch" were never recovered, but we know what they looked like thanks to Gerrit Lundens, a contemporary of Rembrandt who copied the painting when it was complete. These missing sections depict the top of the arch, a balustrade at the bottom, and two soldiers of Frans Banninck Cocq's militia company that stood at the far left.
Though the absence of these elements does not make "The Night Watch" any less impressive, their presence greatly alters the painting's look and feel. The balustrade emphasizes the company's movement forward. Together, the four missing pieces shift the principal figures — Cocq and Willem van Ruytenburch — to the right, creating a more compelling composition.
Copy of "The Night Watch" by Gerrit Lundens.
As part of Operation Night Watch, a multimillion-dollar restoration mission, the Rijksmuseum set out to recreate these missing pieces of the painting to show visitors The Night Watch as Rembrandt had originally constructed it. One easy way to do this would be to upload the smaller Lundens copy into Photoshop, blow it up by a factor of five, print it out, and call it a day.
Easy, but far from adequate. As Erdmann puts it: "There's nothing wrong with using an artist like that. However, the final product would still contain traces of that artist's own style." For Erdmann, the only viable solution was to create a series of neural networks — software that mimics the human brain through the use of artificial neurons — to transform the Lundens copy into an "original" Rembrandt.
Humans, unlike computers, aren't able to make perfect copies. Faithful though Lundens' painting is — especially in its visual detail, for example, the number of buttons on a coat, plumes on a feather, or engravings on a halberd — it still contains a myriad of miniscule differences that prevented Erdmann from simply copy-pasting it onto the original.
Perspective was the first and arguably most important item on Erdmann's list. "The geometric correspondence is pretty good at the bottom of the copy," he says. "At the top, that correspondence starts to fall apart; the composition looks stretched out, supposedly because Lundens was unable to reach the top of the painting to get its precise measurements."
Lundens' copy, adjusted for perspective by the AI.
After creating a neural network that could identify corresponding elements in both versions of The Night Watch — from faces and hands to clothing and weapons — Erdmann made a second neural network that could stretch, rotate, foreshorten, compress, and decompress the Lundens copy so that its measurements matched the Rembrandt original as closely as possible.
According to Erdman, this step was "a guide to where we should place the figures on the left, because they need to be consistent with the extrapolation from the original Night Watch." Aside from aligning the two paintings, Erdmann's adjustments also transformed the facial structure of figures like Cocq, bringing them closer to Rembrandt's expert rendering.
Detail of the Lundens copy before perspectival adjustments.
Detail of the Lundens copy after perspectival adjustments.
Just as a painter must tone their canvas before they can work on composition and color, so too did Erdmann have to get the dimensions right before he could move on to the third and final stage of his coding process. Erdmann's next part of the neural network involved — to paraphrase his elevator pitch — sending the artificial intelligence algorithm to art school.
"Not unlike how you might translate a text from Dutch to English, we wanted to see if we could transform Lundens' painterly style and palette into Rembrandt's," he explains, comparing the learning curve to a quiz. To educate it, the AI was given random tiles from the Lundens copy and asked to render the tiles in the style of Rembrandt.
As with any pedagogical situation, Erdmann evaluated the AI's efforts with a corresponding grade. The closer its output matched the contents of the original Night Watch, the higher the grade it received. When grading, Erdmann considered things like color, texture, and representation (i.e., how well does this frowning face resemble a frowning face, or this sword an actual sword?).
"Once you've defined what makes a good copy, you can train the network on thousands and thousands of these tiles," Erdmann goes on. There are 265 gigabytes of memory of thousands of attempts stored, which demonstrates improvement in quality over a very short time. Within less than a day, the error margin between the AI and the real Rembrandt grew so small it became insignificant; the training was complete.
Lundens copy when adjusted for perspective and Rembrandt's style by AI.
Along the way, the AI had developed a thorough understanding of what made Rembrandt Rembrandt. When translating Lundens' copy, it used a less saturated color palette and thicker, sketchier brushstrokes. It even adopted the painter's signature use of chiaroscuro — a technique involving sharp contrasts between light and shadow.
Then it was time for the final exam. Using the knowledge gained from copying Rembrandt, Erdmann ordered the AI to transform the four outer edges of the Lundens copy — removed from the original Night Watch — into Rembrandt's signature style. The result, an unprecedented collaboration between man and machine, is now on display in the Eregalerij of the Rijksmuseum.
Detail of the completed "Night Watch." The two figures on the left were added from the adjusted Lundens copy.
The missing pieces, resuscitated by AI, were printed onto canvas and varnished so that they had a similar gloss to the rest of the painting. The pieces were then attached to metal plates, which were placed in front of the original Night Watch at a distance of less than one centimeter, thus creating an optical illusion for visitors without actually touching Rembrandt's work.
While conservation science is evolving rapidly, the achievements of people like Erdmann are still eclipsed by the artistic genius of the painters whose work they try to preserve, which is a shame because Erdmann's software can be just as inventive as Rembrandt's brushwork. At the very least, Erdmann's problem-solving skills would have made the master proud.
