Yesterday, officials in the Bay Area issued an order for those of us who live here to “shelter in place.” Meaning, we’re to stay inside our homes and only go out for essential reasons, such as buying groceries. (Most businesses are closed anyway.) This order is in place for three weeks.
I went to Costco to buy some staples (coffee!) before the order took effect. I found shelves stocked with a mix of some goods (there was plenty of coffee) and not much of others (no toilet paper, very little bread.) Hoarding behavior + supply chain disruptions = empty shelves. As I perused the gaps in the store’s inventory, one word kept coming to mind: resiliency. We’re learning the degree to which our systems can keep us fed, clothed, connected, etc.
Markets are great mechanisms for reducing costs. But in times of crisis, cost is only one variable among many. There may come a time when people are willing to pay more for a roll of toilet paper. But if there are no machines turning out more rolls, or trucks to transport them, or fuel to power them, or raw materials to produce them, or stores to sell them, then cost won’t matter much. A month ago, this observation would’ve been hypothetical. Now, it feels very real.
A generative question for the world we create after this crisis: how might markets better balance efficiency and redundancy?
A keynote presentation I delivered at World IA Day San Francisco 2020.
Information architecture isn’t about nav bars and search engines and site maps; it’s about order in service to understanding. To effectively design order, we must look beneath the surface, to the elements that make IA distinct from other disciplines. These elements are language, distinctions, relationships, and rules. Information architects use them to create structures that help others understand.
In a world that is increasingly mediated through environments made of language, it’s essential that designers master these elements. This presentation illustrates how they work by examining a masterwork of information architecture, Dmitri Mendeleev’s periodic table of the elements.
Larry Fink, CEO of BlackRock (the world’s largest fund manager) writing in his annual letter to CEOs:
Climate change has become a defining factor in companies’ long-term prospects. Last September, when millions of people took to the streets to demand action on climate change, many of them emphasized the significant and lasting impact that it will have on economic growth and prosperity – a risk that markets to date have been slower to reflect. But awareness is rapidly changing, and I believe we are on the edge of a fundamental reshaping of finance.
As reported in The Financial Times, BlackRock is backing up this position by changing its investment strategies towards more sustainable opportunities. The company will consider environmental, social, and governance factors along with financial factors when analyzing risk. (A report in Ars Technica explains in more detail the changes BlackRock is implementing.)
The long-term viability of our civilization rests on the sustainability of our ecosystems. For too long our organizations have operated using business models that don’t account for the full impact of their decisions. Finance underlies those decisions, so it gives me hope to see powerful financial actors adopting a more systemic accounting for their investments.
Larry Fink CEO Letter | BlackRock
By this time twenty years ago, many of us were feeling relieved. We’d been hearing for months about the near-certain fallout from the “Y2K bug”: widespread computer system failures caused by the practice of shortening years to two digits instead of four (e.g., 99 rather than 1999.) But by mid-January, 2000, it was clear that all would be ok. Or so it seemed.
Some context, in case you weren’t around then. By the mid-1990s, computer systems were already essential parts of our infrastructure. Nobody knew how many of these computers had the bug or what would happen after 11:59 pm on December 31, 1999, when these systems would assume it was now January 1 of year zero. Would there be blackouts? Urban transport cancellations? Airplane collisions? The complexity of such infrastructure-level systems made the consequences impossible to predict. Governments and companies undertook massive and expensive projects to “fix” the problem. FORTRAN programmers suddenly found their skills in demand.
Then nothing happened. By the end of the first week of January 2000, it was clear that either the fixes had been successful or the potential downsides overblown. Those of us who’d been stressing out about the Y2K bug felt relieved and quickly forgot about it.
I took Christmas Day off: no client work, no podcast editing, no writing. Instead, I spent the day playing with my kids. Mostly, we built LEGO sets.
Although I am not an AFOL, LEGO is an important part of my life. I use it in my systems class and have written about some lessons it holds for systems thinkers. More importantly, I love playing with LEGO. It’s my favorite toy — and has been since I was a child.
Yesterday, as I helped my daughter build set #10260, I reflected on why I love the bricks so much. It boils down to the following:
In his book Where the Action Is, Paul Dourish surfaces a key distinction in software: that of the user interface as an abstraction of the implementation details that underly it:
The essence of abstraction in software is that it hides implementation. The implementation is in some ways the opposite of the abstraction; where the abstraction is the gloss that describes how something can be used and what it will do, the implementation is the part under the covers that describes how it will work. If the gas pedal and the steering wheel are the abstraction, then the engine, power train, and steering assembly are the implementation.
Designers often focus on this abstraction of the system — the stuff users deal with. As a result, we spend a lot of cycles understanding users. But for the interface to be any good, designers must also understand the implementation — the system’s key elements, how they interact with each other, its processes, regulation mechanisms, etc.
Sometimes, as with a new (and perhaps unprecedented) system, this implementation itself is in flux, evolving subject to the system’s goals and the needs of the people who will interact with the system. That is, it’s not all front-end: the implementation is part of the design remit; both the implementation and its abstraction are the object of design.
James Manyika, Jake Silberg, and Brittany Presten writing for the Harvard Business Review:
AI can help identify and reduce the impact of human biases, but it can also make the problem worse by baking in and deploying biases at scale in sensitive application areas.
The phrase “artificial intelligence” is leading us astray. For some folks, it’s become a type of magical incantation that promises to solve all sorts of problems. Much of what goes by AI today isn’t magic — or intelligence, really; it’s dynamic applied statistics. As such, “AI” is highly subject to the data being analyzed and the structure of that data. Garbage in, garbage out.
It’s important for business leaders to learn about how AI works. The HBR post offers a good summary of the issues and practical recommendations for leaders looking to make better decisions when implementing AI-informed systems — which we all should be:
Bias is all of our responsibility. It hurts those discriminated against, of course, and it also hurts everyone by reducing people’s ability to participate in the economy and society. It reduces the potential of AI for business and society by encouraging mistrust and producing distorted results. Business and organizational leaders need to ensure that the AI systems they use improve on human decision-making, and they have a responsibility to encourage progress on research and standards that will reduce bias in AI.
What Do We Do About the Biases in AI?
Organizations never exist on their own; they’re part of an ecosystem, a web of relationships that make it possible for things to get done. Your decisions affect the ecosystem, and the decisions of others affect you.
This has always been so, of course, but the internet has made ecosystems more visible and susceptible to disruption. Transacting has become easier and faster. Changes are often immediate, have more impact, and lead to greater network effects. The balance of power shifts: organizations can leverage connections to go straight to consumers. Alternatively, intermediaries can create new roles for themselves, becoming purveyors of information as much as goods.
There are great opportunities for organizations that can affect system dynamics. But there are also risks — to themselves and to the ecosystem. For example, in a recent interview with economist Tyler Cowen, music critic Ted Gioia talked about the impact internet streaming has had on the music industry:
I’m currently reading Brad Stone’s The Everything Store, a history of Amazon.com. One of the early chapters is about the very early days of the company, which at that point was only selling books. In addition to showing information about products, founder Jeff Bezos wanted the site to include customer reviews of individual books.
Of course, some customer reviews were negative. Mr. Bezos received an angry letter from a book publishing executive, arguing that Amazon was in the business of selling books, not trashing them. But that was not the Amazon way. Per Mr. Bezos,
When I read that letter, I thought, we don’t make money when we sell things. We make money when we help customers make purchase decisions.
These two sentences struck me as a key insight: the particular sale isn’t the ultimate goal of the interaction; building the overall relationship with the customer is.
Long-term thinking is rare in business — especially in a fast-paced environment such as the early web. Nascent Amazon was under a great deal of pressure to prove itself, to grow. Driving more immediate sales would’ve seemed the more prudent approach. And yet, the team chose the long-term relationship. That’s values in action.
In your work, you may sometimes be called to choose between a feature that “drives the needle” in the short term versus one that builds an ongoing relationship. How do you choose? How do you measure the cost either way?
Photo by Steve Jurvetson via Wikimedia