Noah Davis writing in Web Designer Depot, says aloud what I’d thought—but never wrote down—before AI, templates started to kill creativity in web design.

If you’re wondering why the web feels dead, lifeless, or like you’re stuck in a scrolling Groundhog Day of “hero image, tagline, three icons, CTA,” it’s not because AI hallucinated its way into the design department.

It’s because we templatified creativity into submission!

We used to design websites like we were crafting digital homes—custom woodwork, strange hallways, surprise color choices, even weird sound effects if you dared. Each one had quirks. A personality. A soul.

When I was coming up as a designer in the late 1990s and early 2000s, one of my favorite projects was designing Pixar.com. The animation studio’s soul—and by extension the soul I’d imbue into the website—was story. The way this manifest was a linear approach to the site, similar to a slideshow, to tell the story of each of their films.

And as the web design industry grew, and everyone needed and wanted a website, from Fortune 500s to the local barber shop, access to well-designed websites was made possible via templates.

Let’s be real: clients aren’t asking for design anymore. They’re asking for “a site like this.” You know the one. It looks clean. It has animations. It scrolls smoothly. It’s “modern.” Which, in 2025, is just a euphemism for “I want what everyone else has so I don’t have to think.”

Templates didn’t just streamline web development. They rewired what people expect a website to be.

Why hire a designer when you can drop your brand colors into a no-code template, plug in some Lottie files, and call it a day? The end result isn’t bad. It’s worse than bad. It’s forgettable.

Davis ends his rant with a call to action: “If you want design to live, stop feeding the template machine. Build weird stuff. Ugly stuff. Confusing stuff. Human stuff.”

Designer Ben Holliday writes a wonderful deep dive into how caring is good design. In it, he references the conversation that Jony Ive had with Patrick Collison a few months ago. (It’s worth watching in its entirety if you haven’t already.)

Watching the interview back, I was struck by how he spoke about applying care to design, describing how:

“…everyone has the ability to sense the care in designed things because we can all recognise carelessness.”

Talking about the history of industrial design at Apple, Ive speaks about the care that went into the design of every product. That included the care that went into packaging – specifically things that might seem as inconsequential as how a cable was wrapped and then unpackaged. In reality, the type of small interactions that millions of people experienced when unboxing the latest iPhone. These are details that people wouldn’t see as such, but Ive and team believed that they would sense care when they had been carefully considered and designed.

This approach has always been a part of Jony Ive’s design philosophy, or the principles applied by his creative teams at Apple. I looked back and found an earlier 2015 interview and notes I’d made where he says how he believes that the majority of our manufactured environment is characterised by carelessness. But then, how, at Apple, they wanted people to sense care in their products.

The attention to detail and the focus and attention we can all bring to design is care. It’s important.

Holliday’s career has been focused in government, public sector, and non-profit environments. In other words, he thinks a lot about how design can impact people’s lives at massive scale.

In the past few months, I’ve been drawn to the word ‘careless’ when thinking about the challenges faced by our public services and society. This is especially the case with the framing around the impact of technology in our lives, and increasingly the big bets being made around AI to drive efficiency and productivity.

The word careless can be defined as the failure to give sufficient attention to avoiding harm or errors. Put simply, carelessness can be described as ‘negligence’.

Later, he cites Facebook/Meta’s carelessness when they “used data to target young people when at their most vulnerable,” specifically, body confidence.

Ian Dean, writing for Creative Bloq, revisits the impact the original TRON movie had on visual effects and the design industry. The film was not nominated for an Oscar for visual effects as the Academy’s members claimed that “using computers was ‘cheating.’” Little did they know it was only the beginning of a revolution.

More than four decades later, TRON still feels like a moment the film industry stopped and changed direction, just as it had done years earlier when Oz was colourised and Mary Poppins danced with animated animals.

Dean asks, now what about AI-powered visual effects? Runway and Sora are only the beginning.

It’s always interesting to hear how others think about the design process from the outside. Eli Woolery and Aaron Walter interview creativity researcher and author Keith Sawyer to learn about what he’s found to be true after interviewing hundreds of art and design professors and students over a decade for his new book:

The creativity doesn’t come at the beginning. You don’t start by having a brilliant insight. You just dive into the process. And then as you’re engaging in the process, the ideas emerge.

Sawyer emphasizes that art and design schools are not just teaching students how to create, but how to “see.” He found that many professors believe students already possess creativity, but the role of art and design school is to help them realize and develop that potential by teaching them to observe, critique, and reflect more deeply on their own work.

When I interviewed these artists and designers, I would say, how are you teaching students how to create? And everyone was quite uncomfortable with that question. A lot of them would say, we’re not teaching students how to create. Or they’ll say something like, the students are already creative. We’re teaching them how to realize the potential they have as creatives.

Sawyer notes that the hardest thing for students to learn is how to see their own work—that is, to understand what they have actually made rather than sticking rigidly to their original idea.

When we talk about learning to see, you’re talking about learning to see yourself. The hardest thing to teach a student is how to see their own work, to see something that they’ve just generated. Because these studio classes, students have opportunities to share their work in interim stages along the way. You don’t go off and work for two weeks or four weeks and then bring back in the finished product. You bring in your interim and you get a lot of feedback and comments on it.

And what the professors tell me is these 18, 19, and 20-year-olds, they don’t realize what they put on the canvas. Or if they’re a graphic designer, they don’t realize what it is that they’ve generated. A lot of times, they’ll think they’ve done a certain thing. So they have this kind of linear approach—model of the creative process where I’m going to have an idea and I’m going to execute it so they’ll start with their idea and they’ll execute it. They’ll think that what they put on the canvas is their original idea, but in a lot of cases, it’s not. They can’t see what they’ve done themselves, so that’s kind of powerful how do you teach someone that what you put on the canvas isn’t what you say you’re doing.

You can’t just tell them, “Hey, you’re wrong. Let me tell you what you’ve done.” You have to lead someone through that. You have to walk them through it.

One way you do it is you put students in the classroom together and then have them comment on other students’ work so they will be on the other side. And they’ll see another student. talking about what they’ve done and not really describing what’s really on the canvas.

So I think that’s the hardest thing about learning to see is learning to see yourself, learning to see your own work.

I think that’s the power of art and design school, this studio learning environment. I’m biased, of course, because that’s how I learned. Those who are self-taught or have gone through bootcamps miss out on a lot of this experience. The other thing the design school environment teaches is how to give and take critiques. It’s about the work, not you.

In the scenario “AI 2027,” the authors argue that by October 2027—exactly two years from now—we will be at an inflection point. Race to build the superintelligence, or slow down the pace to fix misalignment issues first.

In a piece by Derek Thompson in The Argument, he takes a different predicted AI doomsday date—18 months—and argues:

The problem of the next 18 months isn’t AI disemploying all workers, or students losing competition after competition to nonhuman agents. The problem is whether we will degrade our own capabilities in the presence of new machines. We are so fixated on how technology will outskill us that we miss the many ways that we can deskill ourselves.

Degrading our own capabilities includes writing:

The demise of writing matters because writing is not a second thing that happens after thinking. The act of writing is an act of thinking. This is as true for professionals as it is for students. In “Writing is thinking,” an editorial in Nature, the authors argued that “outsourcing the entire writing process to LLMs” deprives scientists of the important work of understanding what they’ve discovered and why it matters.

…

The decline of writing and reading matters because writing and reading are the twin pillars of deep thinking, according to Cal Newport, a computer science professor and the author of several bestselling books, including Deep Work. The modern economy prizes the sort of symbolic logic and systems thinking for which deep reading and writing are the best practice.

More depressing trends to add to the list.

Our profession is changing rapidly. I’ve been covering that here for nearly a year now. Lots of posts come across my desk that say similar things. Tom Scott repeats a lot of what’s been said, but I’ll pull out a couple nuggets that caught my eye.

He declares that “Hands-on is the new default.” Quoting Vitor Amaral, a designer at Intercom:

Being craft-focused means staying hands-on, regardless of specialty or seniority. This won’t be a niche role, it will be an expectation for everyone, from individual contributors to VPs. The value lies in deeply understanding how things actually work, and that comes from direct involvement in the work.

As AI speeds up execution, the craft itself will become easier, but what will matter most is the critical judgment to craft the right thing, move fast, and push the boundaries of quality.

For those looking for work, Scott says, “You NEED to change how you find a job.” Quoting Felix Haas, investor and designer at Lovable:

Start building a real product and get a feeling for it what it means pushing something out in the market

Learn to use AI to prototype interactively → even at a basic level

Get comfortable with AI tools early → they’ll be your co-designer / sparring partner

Focus on solving real problems, not just making things look good (Which was a problem for very long in the design space)

Scott also says that “Design roles are merging,” and Ridd from Dive Club illustrates the point:

We are seeing a collapse of design’s monopoly on ideation where designers no longer “own” the early idea stage. PMs, engineers, and others are now prototyping directly with new tools.

If designers move too slow, others will fill the gap. The line between PM, engineer, and designer is thinner than ever. Anyone tool-savvy can spin up prototypes — which raises the bar for designers.

Impact comes from working prototypes, not just facilitation. Leading brainstorms or “owning process” isn’t enough. Real influence comes from putting tangible prototypes in front of the team and aligning everyone around them.

Design is still best positioned — but not guaranteed

Designers could lead this shift, but only if they step up. Ownership of ideation is earned, not assumed.

Is the AI bubble about to burst? Apparently, AI prompt-to-code tools like Lovable and v0 have peaked and are on their way down.

Alistair Barr writing for Business Insider:

The drop-off raises tough questions for startups that flaunted exponential annual recurring revenue growth just months ago. Analysts wrote that much of that revenue comes from month-to-month subscribers who may churn as quickly as they signed up, putting the durability of those flashy numbers in doubt.

Barr interviewed Eric Simons, CEO of Bolt who said:

“This is the problem across all these companies right now. The churn rate for everyone is really high,” Simons said. “You have to build a retentive business.”

I love this framing by Patrizia Bertini:

Let me offer a different provocation: AI is not coming for your job. It is coming for your tasks. And if you cannot distinguish between the two, then yes — you should be worried. Going further, she distinguishes between output and outcome: Output is what a process produces. Code. Copy. Designs. Legal briefs. Medical recommendations. Outputs are the tangible results of a system executing its programmed or prescribed function — the direct product of following steps, rules, or algorithms. The term emerged in the industrial era, literally describing the quantity of coal or iron a mine could extract in a given period. Output depends entirely on the efficiency and capability of the process that generates it.

Outcome is what happens when that output meets reality. An outcome requires context, interpretation, application, and crucially — intentionality. Outcomes demand understanding not just what was produced, but why it matters, who it affects, and what consequences ripple from it. Where outputs measure productivity, outcomes measure impact. They are the ultimate change or consequence that results from applying an output with purpose and judgment.

She argues that, “AI can generate outputs. It cannot, however, create outcomes.”

This reminds me of a recent thread by engineer Marc Love:

It’s insane just how much how I work has changed in the last 18 months.

I almost never hand write code anymore except when giving examples during planning conversations with LLMs.

I build multiple full features per day , each of which would’ve taken me a week or more to hand write. Building full drafts and discarding them is basically free.

Well over half of my day is spent ideating, doing systems design, and deciding what and what not to build.

It’s still conceptually the same job, but if i list out the specific things i do in a day versus 18 months ago, it’s almost completely different.

Care about the outcome, not the output.

In an announcement to users this morning, Visual Electric said they were being acquired by Perplexity—or more accurately, the team that makes Visual Electric will be hired by Perplexity. The service will shut down in the next 90 days.

Today we’re sharing the next step in Visual Electric’s journey: we’ve been acquired by Perplexity. This is a milestone that marks both an exciting opportunity for our team and some big changes for our product.

Over the next 90 days we’ll be sunsetting Visual Electric, and our team will be forming a new Agent Experiences group at Perplexity.

While we’ve seen acquihires and shutdowns in either the AI infrastructure space (e.g., Scale AI) or coding space (e.g., Windsurf), I don’t believe we’ve seen one in the image or video gen AI space have an exit event like this yet. Obviously, The Browser Company announced their acquisition by Atlassian last month.

I believe building gen AI tools at this moment is incredibly competitive. I think it takes an even stronger stomached entrepreneur than in the pre-ChatGPT moment. So kudos for the folks at Visual Electric for having a good outcome and getting to continue to do their work at Perplexity. But I do think this is not the last that we’ll see consolidation in this space.

Tim Berners-Lee, the father of the web who gave away the technology for free, says that we are at an inflection point with data privacy and AI. But before he makes that point, he reminds us that we are the product:

Today, I look at my invention and I am forced to ask: is the web still free today? No, not all of it. We see a handful of large platforms harvesting users’ private data to share with commercial brokers or even repressive governments. We see ubiquitous algorithms that are addictive by design and damaging to our teenagers’ mental health. Trading personal data for use certainly does not fit with my vision for a free web.

On many platforms, we are no longer the customers, but instead have become the product. Our data, even if anonymised, is sold on to actors we never intended it to reach, who can then target us with content and advertising. This includes deliberately harmful content that leads to real-world violence, spreads misinformation, wreaks havoc on our psychological wellbeing and seeks to undermine social cohesion.

And about that fork in the road with AI:

In 2017, I wrote a thought experiment about an AI that works for you. I called it Charlie. Charlie works for you like your doctor or your lawyer, bound by law, regulation and codes of conduct. Why can’t the same frameworks be adopted for AI? We have learned from social media that power rests with the monopolies who control and harvest personal data. We can’t let the same thing happen with AI.

I’m happy that the conversation around the design talent crisis continues. Carly Ayres, writing for It’s Nice That picks up the torch and speaks to designers and educators about this topic. What struck me—and I think what adds to the dialogue—is the notion of the belief gap. Ayres spoke with Naheel Jawaid, founder of Silicon Valley School of Design, about it:

“A big part of what I do is just being a coach, helping someone see their potential when they don’t see it yet,” Naheel says. “I’ve had people tell me later that a single conversation changed how they saw themselves.”

In the past, belief capital came from senior designers taking juniors under their wing. Today, those same seniors are managing instability of their own. “It’s a bit of a ‘dog eat dog world’-type vibe,” Naheel says. “It’s really hard to get mentorship right now.”

The whole piece is great. Tighter than my sprawling three-parter. I do think there’s a piece missing though. While Ayres highlights the issue and offers suggestions from designer leaders, businesses need to step up and do something about the issue—i.e., hire more juniors. Us recognizing it is the first step.

Jason Spielman put up a case study on his site for his work on Google’s NotebookLM:

The mental model of NotebookLM was built around the creation journey: starting with inputs, moving through conversation, and ending with outputs. Users bring in their sources (documents, notes, references), then interact with them through chat by asking questions, clarifying, and synthesizing before transforming those insights into structured outputs like notes, study guides, and Audio Overviews.

And yes, he includes a sketch he did on the back of a napkin.

I’ve always wondered about the UX of NotebookLM. It’s not typical and, if I’m being honest, not exactly super intuitive. But after a while, it does make sense. Maybe I’m the outlier though, because Spielman’s grandmother found it easy. In an interview last year on Sequoia Capital’s Training Data, he recalls:

I actually do think part of the explosion of audio overviews was the fact it was a simple one click experience. I was on the phone with my grandma trying to explain her how to use it and it actually didn’t take any explanation. I’m like, “Drop in a source.” And she’s like, “Oh! I see. I click this button to generate it.” And I think that the ease of creation is really actually what catalyzed so much explosion. So I think when we think about adding these knobs [for customization] I think we want to do it in a way that’s very intentional.

Chatboxes have become the uber box for all things AI. The criticism of this blank box has been the cold start issue. New users don’t know what to type. Designers shipping these product mostly got around this problem by offering suggested prompts to teach users about the possibilities.

The issue on the other end is that expert users end up creating their own library of prompts to copy and paste into the chatbox for repetitive tasks.

Sharang Sharma writing in UX Collective illustrates how these UIs can be smarter by being predictive of intent:

Contrary, Predictive UX points to an alternate approach. Instead of waiting for users to articulate every step, systems can anticipate intent based on behavior or common patterns as the user types. Apple Reminders suggests likely tasks as you type. Grammarly predicts errors and offers corrections inline. Gmail’s Smart Compose even predicts full phrases, reducing the friction of drafting entirely.

Sharma says that the goal of predictive UX is to “reduce time-to-value and reframe AI as an adaptive partner that anticipates user’s intent as you type.”

Imagine a little widget that appears within the chatbox as you type. Kind of a cool idea.

Thinking about this morning’s link about web forms, if you abstract why it’s so powerful, you get to the point of human-computer interaction: the computer should do what the user intends, not the buttons they push.

Matt Webb reminds us about the DWIM, or Do What I Mean philosophy in computing that was coined by Warren Teitelman in 1966. Webb quotes computer scientist Larry Masinter:

DWIM is an embodiment of the idea that the user is interacting with an agent who attempts to interpret the user’s request from contextual information. Since we want the user to feel that he is conversing with the system, he should not be stopped and forced to correct himself or give additional information in situations where the correction or information is obvious.

Webb goes on to say:

Squint and you can see ChatGPT as a DWIM UI: it never, never, never says “syntax error.”

Now, arguably it should come back and ask for clarifications more often, and in particular DWIM (and AI) interfaces are more successful the more they have access to the user’s context (current situation, history, environment, etc).

But it’s a starting point. The algo is: design for capturing intent and then DWIM; iterate until that works. AI unlocks that.

Forms is one of the fundamental things we make users do in software. Whether it’s the login screen, billing address form, or a mortgage application, forms are the main method for getting data from users and into computer-accessible databases. The human is deciding what piece of information to put into which column in the database. With AI, form filling should be much simpler.

Luke Wroblewski makes the argument:

With Web forms, the burden is on people to adapt to databases. Today’s AI models, however, can flip this requirement. That is, they allow people to provide information in whatever form they like and use AI do the work necessary to put that information into the right structure for a database.

How can it work?

With AgentDB connected to an AI model (via an MCP server), a person can simply say “add this” and provide an image, PDF, audio, video, you name it. The model will use AgentDB’s template to decide what information to extract from this unstructured input and how to format it for the database. In the case where something is missing or incomplete, the model can ask for clarification or use tools (like search) to find possible answers.

I believe purity tests of any sort are problematic. And it’s much too easy to throw around the “This is AI slop!” claim. AI was used in the main title sequence for the Marvel TV show Secret Invasion. But it was on purpose and aligned with the show’s themes of shapeshifters.

Anyway, Daniel John, writing in the Creative Bloq:

[Lady] Gaga just dropped the music video for The Dead Dance, a song debuted in Season 2 of Netflix’s Wednesday. Directed by Tim Burton, it’s a suitably nightmarish black-and-white cacophony of monsters and dolls. But some are already claiming that parts of it were made using AI.

John shows a tweet from @graveyardquy as an example:

i didn’t think we’d ever be in a timeline where a tim burton x lady gaga collab would turn out to be AI slop… but here we are

We need to separate quality critiques from tool usage. If it looks good and is appropriate, I’m fine with CG, AI, and whatever comes next that helps tell the story. Same goes for what we do as designers, ’natch.

Gaga’s song is great. It’s a bop, as the kids say, with a neat music video to boot.

Here’s a fun visual essay about a artist Yufeng Zhao’s piece “Alt Text in NYC.” It’s a essentially a visual search engine that searches all the text (words) on the streets of New York City. The dataset comprises of over eight million photos from Google Street View! Matt Daniels, writing for The Pudding:

The result is a search engine of much of what’s written in NYC’s streets. It’s limited to what a Google Street View car can capture, so it excludes text in areas such as alleyways and parks, or any writing too small to be read by a moving vehicle.

The scale of the data is immense: over 8 million Google Street View images (from the past 18 years) and 138 million identified snippets of text.

Just over halfway down the article, there is a list of the top 1,000 words in the data. Most are expected words from traffic signs like “stop.” But number twenty-five is “Fedders,” the logo of an air-conditioner brand popular in the 1950s to the 1990s. They’re all over the exteriors of the city’s buildings.

Best viewed on your computer, IMHO.

Josh Miller, CEO, and Hursh Agrawal, CTO, of The Browser Company:

Today, The Browser Company of New York is entering into an agreement to be acquired by Atlassian in an all-cash transaction. We will operate independently, with Dia as our focus. Our objective is to bring Dia to the masses.

Super interesting acquisition here. There is zero overlap as far as I can tell. Atlassian’s move is out of left-field. Dia’s early users were college students. The Browser Company more recently opened it up to former Arc users. Is this bet for Atlassian—the company that makes tech-company-focused products like Jira and Confluence—around the future of work and collaboration? Is this their first move against Salesforce? 🤔

DOC is a publication from Fabricio Teixeira and Caio Braga that I’ve linked to before. Their latest reflection is on interfaces.

A good user interface is a good conversation.

Interfaces thrive on clarity, responsiveness, and mutual understanding. In a productive dialogue, each party clearly articulates their intentions and receives timely, understandable responses. Just as a good conversationalist anticipates the next question or need, a good interface guides you smoothly through your task. At their core, interfaces translate intent into action. They’re a bridge between what’s in your head and what the product can do.

Reflection is the best word I’ve found to describe these pieces. They’re hype-free, urging us to take a step back, and—at least for me—a reminder about our why.

In the end, interfaces are also a space for self-expression.

The ideal of “no interface” promises ultimate efficiency and direct access—but what do we lose in that pursuit? Perhaps the interface is not just a barrier to be minimized, but a space for human expression. It’s a canvas; a place to imbue a product with personality, visual expression, and a unique form of art.

When we strip that away, or make everything look the same, we lose something important. We trade the unique and the delightful for the purely functional. We sacrifice a vital part of what makes technology human: the thoughtful, and sometimes imperfect, ways we present ourselves to the world.

Interesting piece from Vaughn Tan about a critical thinking framework that is disguised as a piece about building better AI UIs for critical thinking. Sorry, that sentence is kind of a tongue-twister. Tan calls out—correctly—that LLMs don’t think, or in his words, can’t make meaning:

Meaningmaking is making inherently subjective decisions about what’s valuable: what’s desirable or undesirable, what’s right or wrong. The machines behind the prompt box are remarkable tools, but they’re not meaningmaking entities.

Therefore when users ask LLMs for their opinions on matters, e.g., as in the therapy use case, the AIs won’t come back with actual thinking. IMHO, it’s semantics, but that’s another post.

Anyhow, Tan shares a pen and paper prototype he’s been testing, which breaks down a major decision into guided steps, or put another way, a framework.

This user experience was designed to simulate a multi-stage process of structured elicitation of various aspects of strongly reasoned arguments. This design explicitly addresses both requirements for good tool use. The structured prompts helped students think critically about what they were actually trying to accomplish with their custom major proposals — the meaningmaking work of determining value, worth, and personal fit. Simultaneously, the framework made clear what kinds of thinking work the students needed to do themselves versus what kinds of information gathering and analysis could potentially be supported by tools like LLMs.

This guided or framework-driven approach was something I attempted wtih Griffin AI. Via a series of AI-guided prompts to the user—or a glorified form, honestly—my tool helped users build brand strategies. To be sure, a lot of the “thinking” was done by the model, but the idea that an AI can guide you to critically think about your business or your client’s business was there.

Designer Tey Bannerman writes that when he hears “human in the loop,” he’s reminded of a story about Lieutenant Colonel Stanislav Petrov, a Soviet Union duty watch officer who monitored for incoming missile strikes from the US.

12:15 AM… the unthinkable. Every alarm in the facility started screaming. The screens showed five US ballistic missiles, 28 minutes from impact. Confidence level: 100%. Petrov had minutes to decide whether to trigger a chain reaction that would start nuclear war and could very well end civilisation as we knew it.

He was the “human in the loop” in the most literal, terrifying sense.

Everything told him to follow protocol. His training. His commanders. The computers.

But something felt wrong. His intuition, built from years of intelligence work, whispered that this didn’t match what he knew about US strategic thinking.

Against every protocol, against the screaming certainty of technology, he pressed the button marked “false alarm”.

Twenty-three minutes of gripping fear passed before ground radar confirmed: no missiles. The system had mistaken a rare alignment of sunlight on high-altitude clouds for incoming warheads.

His decision to break the loop prevented nuclear war.

Then Bannerman shares an awesome framework he developed that allows humans in the loop in AI systems “genuine authority, time to think, and understanding the bigger picture well enough to question” the system’s decision. Click on to get the PDF from his site.

Simon Sherwood, writing in The Register:

Amazon Web Services CEO Matt Garman has suggested firing junior workers because AI can do their jobs is “the dumbest thing I’ve ever heard.”

Garman made that remark in conversation with AI investor Matthew Berman, during which he talked up AWS’s Kiro AI-assisted coding tool and said he’s encountered business leaders who think AI tools “can replace all of our junior people in our company.”

That notion led to the “dumbest thing I’ve ever heard” quote, followed by a justification that junior staff are “probably the least expensive employees you have” and also the most engaged with AI tools.

“How’s that going to work when ten years in the future you have no one that has learned anything,” he asked. “My view is you absolutely want to keep hiring kids out of college and teaching them the right ways to go build software and decompose problems and think about it, just as much as you ever have.”

Yup. I agree.