Meta has gone after AI in the same way they went after the metaverse, by splashing money around and rushing to build products fast. They’re spending tens of billions building out data centers and related AI infrastructure. They’re tossing out incredible compensation packages in the hundreds of millions of dollars to top AI researchers.

Facebook is now the cultural symbol for useless slop and disinformation, while ‘that’s so Reels’ is now a common insult for terrible shortform video content - and you get the first theory of Meta. It’s a laughable company whose core business is increasingly uncool, a company in decline, a company that falls flat on its face any time it tries to change things up.

Call Meta uncool all you’d like, metrics are up across the board. They’re getting higher user engagement, higher user counts, and they’re selling more ads at a higher price-per-ad. The numbers are up in virtually every way on every platform - Facebook, Instagram, WhatsApp and even Threads.

They can afford to make huge bets on speculative new technologies because they have more money than they know how to spend. Why not spend all that money on the metaverse or on AI? What else are they going to do with it? Zuckerberg still controls the company and he’d prefer to invest in new technology rather than just pay himself fat dividends. This is the second theory of Meta - a unbelievably successful company whose core business is booming and who spends a lot of money on speculative investments simply because they can.

This Apple has been dismantling Mac OS, as if it’s a foreign tool to them. They’ve bashed its UI around. And they seem to have done that not for the purpose of improving it, but simply for the purpose of changing it; adapting it to their (mostly misguided) idea of unifying the interface of different devices to bring it down to the simplest common denominator.

f we look at Mac OS as a metro railway line, it’s like Apple has stopped extending it and creating new stations. What they’ve been doing for a while now has been routine maintenance, and giving the stations a fresh coat of paint every year. Only basic and cosmetic concerns, yet sometimes mixing things up to show that more work has gone into it, a process that invariably results in inexplicable and arbitrary choices like moving station entrances around, shutting down facilities, making the train timetables less legible, making the passages that lead to emergency exits more convoluted and longer to traverse, and so on — hopefully you know what I mean here.

When you self-impose timelines and cadences that are essentially marketing-driven and do not really reflect technological research and development, then you become prisoner in a prison of your own making. Your goal and your priorities start becoming narrower in scope. You reduce your freedom of movement because you stop thinking in terms of creating the next technological breakthrough or innovative device; you just look at the calendar and you have to come up with something by end of next trimester, while you also have to take care of fixing bugs that are the result of the previous rush job… which keep accumulating on top of the bugs of the rush job that came before, and so forth.

From what I’ve understood by examining the evolution of computer science and computer history, scientists and technologists of past decades seemed to have an approach that could be described as, ‘ideas & concepts first, technology later’. Many figures in the history of computing are rightly considered visionaries because they had visions — sometimes very detailed ones — of what they wanted computers to become, of applications where computers could make a difference, of ways in which a computer could improve a process, or could help solve a real problem.

What I’m seeing today is more like the opposite approach — ‘technology first, ideas & concepts later’: a laser focus on profit-driven technological advancements to hopefully extract some good ideas and use cases from.

Where there are some ideas, or sparks, they seem hopelessly limited in scope or unimaginatively iterative, short-sightedly anchored to the previous incarnation or design. The questions are something like, How can we make this look better, sleeker, more polished?

Steve Jobs once said, There’s an old Wayne Gretzky quote that I love. ‘I skate to where the puck is going to be, not where it has been.’ And we’ve always tried to do that at Apple. Since the very, very beginning. And we always will. If I may take that image, I’d say that today a lot of tech companies seem more concerned with the skating itself and with continuing to hit the puck in profitable ways.

Reinforcement learning with human feedback is a process by which models learn how to answer queries based on which responses users prefer most, and users mostly prefer flattery. More sophisticated users might balk at a bot that feels too sycophantic, but the mainstream seems to love it. Earlier this month, Meta was caught gaming a popular benchmark to exploit this phenomenon: one theory is that the company tuned the model to flatter the blind testers that encountered it so that it would rise higher on the leaderboard.

A series of recent, invisible updates to GPT-4o had spurred the model to go to extremes in complimenting users and affirming their behavior. It cheered on one user who claimed to have solved the trolley problem by diverting a train to save a toaster, at the expense of several animals; congratulated one person for no longer taking their prescribed medication; and overestimated users’ IQs by 40 or more points when asked.

OpenAI, Meta, and all the rest remain under the same pressures they were under before all this happened. When your users keep telling you to flatter them, how do you build the muscle to fight against their short-term interests?  One way is to understand that going too far will result in PR problems, as it has for varying degrees to both Meta (through the Chatbot Arena situation) and now OpenAI. Another is to understand that sycophancy trades against utility: a model that constantly tells you that you’re right is often going to fail at helping you, which might send you to a competitor. A third way is to build models that get better at understanding what kind of support users need, and dialing the flattery up or down depending on the situation and the risk it entails. (Am I having a bad day? Flatter me endlessly. Do I think I am Jesus reincarnate? Tell me to seek professional help.)

But while flattery does come with risk, the more worrisome issue is that we are training large language models to deceive us. By upvoting all their compliments, and giving a thumbs down to their criticisms, we are teaching LLMs to conceal their honest observations. This may make future, more powerful models harder to align to our values — or even to understand at all. And in the meantime, I expect that they will become addictive in ways that make the previous decade’s debate over “screentime” look minor in comparison. The financial incentives are now pushing hard in that direction. And the models are evolving accordingly.

It symbolizes everything everyone has ever hated about digitization. It celebrates a lossy, creative compression for the most flimsy reason: An iPad shedding an irrelevant millimeter or two. It's destruction of beloved musical instruments is the perfect metaphor for how utterly tone-deaf technologists are capable of being. But the real story is just how little saved up goodwill Apple had in the bank to compensate for the outrage.

This should all be eerily familiar to anyone who saw Microsoft fall from grace in the 90s. From being America's favorite software company to being the bully pursued by the DOJ for illegalities. Just like Apple now, Microsoft's reputation and good standing suddenly evaporated seemingly overnight once enough critical stories had accumulated about its behavior.

Apple had such treasure chest of goodwill from decades as first an underdog, then unchallenged innovator. But today they're a near three-trillion dollar company, battling sovereigns on both sides of the Atlantic, putting out mostly incremental updates to mature products.

If I had to summarize it, I would say that the signal to noise ratio is what wore me down. We start companies to build products that serve people, not to sit in meetings with lawyers.  You need to be able to answer the "what have I done for our users today" question with "not much but I got promoted" and be happy with that answer to be successful in Corp-Tech.

being part of a Corporation means that the signal to noise ratio changes dramatically.  The amount of time and effort spent on Legal, Policy, Privacy - on features that have not shipped to users yet, meant a significant waste of resources and focus. After the acquisition, we have an extremely long project that consumed many of our best engineers to align our data retention policies and tools to Google. I am not saying this is not important BUT this had zero value to our users. An ever increasing percent of our time went to non user value creation tasks and that changes the DNA of the company quickly, from customer focused to corporate guidelines focused.

the salaries are so high and the options so valuable that it creates many misalignments.  The impact of an individual product on the Corp-Tech stock is minimal so equity is basically free money.  Regardless of your performance (individually) or your product performance, you equity grows significantly so nothing you do has real economic impact on your family. The only control you have to increase your economic returns are whether you get promoted, since that drives your equity and salary payments.  This breaks the traditional tech model of risk reward.

Artists never complete a single, perfect artwork, and a single work never instigates an absolute transcendence in viewers. We may aspire toward this quasi-theological ideal, but art only has the ability to suggest the sublime. The real sustenance of the artistic is the scope of experience it provides, the cumulative sense of growth and cultivation of ourselves through art, a tendency toward a good that we can never capture but only assist in radiating itself and existence.

I quickly realized that my habits were more suited to going to galleries every week than to working regularly on longer pieces, that there weren’t very many shows I wanted to write about at length, and that a regular stream of blithe, off-the-cuff reviews would attract more attention than intermittent longer essays

Film, music, food and book critics write for a general public that can be swayed to spend their money one way or another, whereas the general public cannot afford to buy the art that is written about in Artforum.

The VP in charge of Google Plus hosted the Friday all-hands several times to get us all excited about what they were building. It was obvious to me and many others that there was no reason for people already on Facebook to switch from Facebook. Someone asked a direct question, but the VP deflected and talked about how easy it would be to group your friends with the Circles feature — which was not at all a reason to switch.It seemed like Google didn’t have the processes or experience to get the product strategy right. “Who are our potential users and what does it take to win them?” is product strategy 101. Maybe someone raised this question in an exec review, but it didn’t become a launch blocker. Google+ never took off, and was eventually shut down.

If Google didn’t start with a conviction that they needed the product, it makes sense that they wouldn’t have the stamina to keep iterating and investing. Most other companies don’t have the money to build and launch products with such little conviction and oversight. Other companies need their products to succeed, so they try harder & smarter to make the products successful.

IME people often don’t realize that product strategies are actually way more important and influential than company strategies. Simply because it’s the products that have an impact on people’s lives, not the company.

Google has a company strategy, but they don’t make product strategies.

Google’s company strategy is “Hire all the smart people.” Hire all the smart people and let them build. Hire all the smart people so they can’t work at a competitor. Hire all the smart people even if we don’t have something important for them to work on.Google acts like a venture capitalist, investing in promising people with the expectation that most will fail. They invest broadly in search of the idea that will deliver 100x. Let 1000 flowers bloom, and see which are the best.

Unlike the “trauma plot,” Parul Sehgal’s coinage for the use of trauma as narrative payoff, the therapeutic plot doesn’t wallow in trauma itself. Instead, it offers formulaic accounts of diagnosis and healing—what Janet Malcolm has called “the streamlined truisms of the age of mental health.”

Book blogs sort recommendations by pathology (severe social anxiety, schizophrenia, body dysmorphia), symptom (anxiety, panic attacks), and trauma (parental suicide, psychiatric stay). Fans diagnose characters with mental disorders (a fan theory diagnoses the character Bruno from another recent Pixar offering, Encanto, with obsessive compulsive disorder). Self-diagnosis even informed the development of Everything Everywhere. In early drafts of the script, Evelyn suffered from undiagnosed attention deficit hyperactivity disorder. Writing and researching her character inspired Kwan to identify and seek treatment for his own heretofore undiagnosed ADHD.

No doubt, such a medical diagnosis can provide relief and resolution. But is such a diagnosis the job of art? What is lost when audiences and creators eschew other ways of discussing fiction (for example, E.M. Forster’s distinction between round and flat characters) and instead reduce characters to clinical profiles?

In ancient Athenian tragedy, catharsis was defined by Aristotle’s Poetics as the ritual purification and purgation of emotions, particularly pity and fear. Pity arises from identification with the tragic hero, whose nobility is compromised by a fatal flaw, and fear is elicited by his excessive punishment. The therapeutic significance of catharsis originated much later in the theories of Sigmund Freud. By applying Joseph Breuer’s “cathartic method,” Freud theorized that hypnosis allowed patients to recall the traumatic experience at the root of their condition. Catharsis was Freud’s first major breakthrough, and his first brush with the powers of the unconscious that would form the underpinnings of psychoanalytic theory.For Aristotle, catharsis was the result of anagnorisis—the humility produced by the tragic hero’s recognition not only of the calamity that had befallen him, but also of his own role in bringing it about. Freud, meanwhile, described an inherent tragedy in the “impossible profession” of psychoanalysis, “in which one can be sure beforehand of achieving unsatisfying results.” It wasn’t that Freud had no faith in his own methods; he simply perceived the enormity of the human condition, and understood that the odds of success were not stacked in the psychoanalyst’s favor.Humility is absent from today’s therapeutic catharsis, which assumes with algorithmic certainty that sharing will lead to understanding, and that understanding will lead to healing. Art’s role, according to Everything Everywhere actress Stephanie Hsu, is “to hold space for trauma and offer catharsis,” and to recognize that “empathy and radical empathy and radical kindness are also a tool.” Buried beneath this gauzy language is the fact that the “empathy” of Turning Red and Everything Everywhere rely on the transformation of the mothers, not their children. The adults must learn that children are individuals rather than extensions of parental will, and when empathy is granted to mothers, it is only through their shared status as victims.

Pity the immigrant women who fled war-torn nations and corrupt regimes only to be subjected to psychoanalysis from hipster filmmakers and their own children

All this is not to say that storytelling holds no empathic power, nor an ability to transcend individual perspectives. But this power lies in art’s ability to overthrow, not reify, easy solutions—to challenge rather than “validate.” The transmission of suffering from one generation to the next is a worthy subject for art, but not because its effect on any particular demographic has been under-represented.

Our approach to culture should account for rigor and complexity, not defer to trite solutionism.

To find jobs more quickly, students tend to showcase the types of work most in demand in our industry, making website and app redesigns the overwhelming majority of case studies being published.

The myopic focus on shorter time-to-job metrics requires schools to standardize their design curricula — a trend which is reflected in the structure of case studies being produced.

The UX bootcamp also benefits from its students’ projects being published on Medium: case studies become content marketing that helps attract new students — and therefore revenue.

How can you differentiate yourself when applying to a position, if case studies from other candidates look exactly the same at first glance?

formulas create the illusion that real projects will always follow those same predetermined steps. In reality, working with both in-house and agency teams is too chaotic to apply a one-size-fits-all methodology

Research budgets are not always available; user journeys are not always necessary.

Many case studies start with an unexplained “user need” that one infers is the student’s personal need (e.g. "an easier way to share music on Spotify"). User research is then utilized in an attempt to prove that other people experience the same problem.

Because students are used to ticking boxes on a standard design process template provided by their school, they forget to explain why they are utilizing a particular method. If everyone’s process is somewhat the same, what is your unique angle to the way you work?

Insights gathered from one step are rarely applied to the next one. As a result, most case studies feel complete, but few feel smart.

Many case studies describe the project process in great detail, only to conclude with a solution which is predictable, unpolished, and/or lacking insight.

There are only so many audiences a single case study can speak to. If the student’s main objective in creating a case study is to get a foot in the industry, it might be a good idea for them to prioritize recruiters and hiring managers when thinking about their public-facing output. Ultimately, recruiters are the ones who screen candidates and hiring managers are the ones who make the final decision.

Because their time is limited, most hiring managers do not go through a designer’s entire portfolio, but instead review a couple of case studies

Since they must review a high volume of case studies as part of the recruiting process, hiring managers rarely read thoroughly, but instead quickly scan students’ work in search of talent

First [I do] just a quick scan. If the work looks interesting then I proceed to a more thorough analysis. This can range from 30 seconds to 15 or 20 minutes, it all depends on the quality and quantity of the work. Weak portfolios get discarded in seconds. Really good portfolios are analyzed in-depth and I usually end up on the candidate’s website or Twitter account. — Head of Design, 13 years of experience

Another common concern raised by the upper-level managers we spoke with was the extreme focus on the part of students on the design process, at the expense of the quality of the output. They reported that this was especially the case with UX and product-focused portfolios.

I jump to the final designs, I want to see the outcome of the case study. Only then, if the final designs are solid I go back and try to understand the designer’s process. You see a lot of people building really deep use-cases, but the execution fails and right now I need designers who can think and deliver, not one or another. — VP of Product Design, 17 years of experience

Your case study structure should reflect the areas that most interest you as well as the ones that will help you reach your personal goals. What type of company do you want to work with, and what kind of story will make the design leadership of that organization excited about the possibility of working with you?

If UI design is one of your strengths, showing personas that are shallow or unresearched can hurt more than they can help.

If you are writing about a real project for a real client, it’s important to explain the constraints and limitations around which you were working. If there were no constraints involved —as in the case of a project completed as part of a UX course— hiring managers will expect the designs to be as as innovative as possible, and will be frustrated if they are not.

Focus on insights rather than process

Reading an insightful case study is much more exciting than reading one that is complete but uninspired.

Would someone feel compelled to retweet a random sentence from your case study?

how you brought those insights to each subsequent step of the process.

The case study should tell a story about how you think and how you design — it's never about the project itself. Ideally, your case study should reflect your personality so strongly that it would feel out of place in any other designer’s portfolio.

What excited you the most about working on this project when you first received the brief?

A case study should focus less on the project it portrays and more on the skills and personality of its designer.

it should provide a platform for you to talk about the things you believe as a designer: your passion (reflected in how much time and attention you invested in the project), your thinking (demonstrated by how you connect the dots throughout your process), and your insights as you learn new things and evolve in your career.

A Little Life was rightly called a love story; what critics missed was that its author is one of the lovers. This is Yanagihara’s principle: If true misery exists, then so might true love. That simple idea, childlike in its brutality, informs all her fiction. Indeed, the author appears unable, or unwilling, to conceive love outside of life support

Luxury is simply the backdrop for Jude’s extraordinary suffering, neither cause nor effect; if anything, the latter lends poignancy to the former. This was Yanagihara’s first discovery, the one that cracked open the cobbled streets of Soho and let something terrible slither out — the idea that misery bestows a kind of dignity that wealth and leisure, no matter how sharply rendered on the page, simply cannot.

“There’s a point,” Yanagihara once said of Jude, at which “it becomes too late to help some people.” These are difficult words to read for those of us who have passed through suicidal ideation and emerged, if not happy to be alive, then relieved not to be dead. It is indeed a tourist’s imagination that would glance out from its hotel window onto the squalor below and conclude that death is the opposite of paradise, as if the locals did not live their little lives on the expansive middle ground between the two.

even Yanagihara’s novels are not death camps; they are hospice centers. A Little Life, like life itself, goes on and on. Hundreds of pages into the novel, Jude openly wonders why he is still alive, the beloved of a lonely god. For that is the meaning of suffering: to make love possible. Charles loves David; David loves Edward; David loves Charles; Charlie loves Edward; Jude loves Willem; Hanya loves Jude; misery loves company.