If you’ve used Google at any point in the last 28 years the experience you know is gone.

They celebrated this like a moon landing. And honestly? I think that’s fair since it genuinely might be one of the biggest product pivots in Silicon Valley history.
A company that built the modern internet’s front door just decided to brick it up and install a chatbot instead. That takes a particular kind of nerve… or desperation.

But once the confetti settles, some uncomfortable arithmetic starts doing the rounds in your head.

Advertising is how Google makes its money. Roughly 75% of everything Alphabet earns comes from ads placed alongside those search results. The ones they just retired. The model that has funded everything from Gmail to self-driving cars traces back to someone clicking a sponsored link about mattresses.

And here’s the problem with AI Mode: every time a user gets a direct answer from the AI, there’s no webpage to click through to. No click means no ad impression. No ad impression means no revenue. Google has 2.5 billion monthly users on its AI products right now - and the more useful those products become, the less money Google makes per interaction under the old model.

That is, to use a technical term, a bit of a predicament.

Now, to be fair - and I am occasionally fair, when it suits me - I don’t think Google had much of a choice here. The alternative was standing still while OpenAI and Anthropic ate their lunch, their dinner, and eventually their entire kitchen.
Better to cannibalize yourself than get cannibalized by someone who doesn’t even have a search index. At least this way you control the transition.

I think the bet Google is making is that AI agents will open up entirely new advertising surfaces. Maybe your AI assistant books a restaurant and the restaurant pays to be recommended. Maybe sponsored answers replace sponsored results. Maybe the whole thing looks nothing like advertising as we know it, and Google figures it out on the fly because they have the distribution, the data, and the infrastructure that nobody else can match.

That’s a reasonable bet. I wouldn’t take it off the table.

But here’s where I start to get uncomfortable…

With traditional search, the manipulation was at least visible. You could see “Sponsored” at the top of the results. You could make a judgement call. You could scroll past the ads and get to the organic results, imperfect as they were. The line between paid influence and genuine recommendation was thin and often blurry - but it existed.

With AI answers, that line disappears. A recommendation inside a conversational response doesn’t look like an ad. It looks like advice. It sounds like a knowledgeable friend who has read everything and wants to help.
The influence, if it’s there, is invisible… baked into the answer itself rather than labelled in a smaller font at the top.

That’s a fundamentally different relationship between the user and the information. And it’s worth pausing on, because we’re about to sign up for it en masse without really discussing it.

There’s a version of this that goes fine: Google builds clear disclosure standards.
AI-influenced answers are flagged. The model distinguishes between commercial and informational intent in a meaningful way. The whole thing is more useful than what came before, and the advertising ends up less obnoxious than a banner ad from 2009.

There’s also a version where the most powerful information intermediary in history becomes an ad platform so sophisticated you can’t tell it’s an ad platform - and the “honest answer” you’re getting has been weighted by something other than accuracy.

I know… Black Mirror stuff…

The uncomfortable truth is that Google needs both outcomes simultaneously.
They need you to trust the AI completely, and they need the AI to generate revenue. Those two things exist in tension, and only one of them is going to pay the bills.

So I’ll leave you with the question I can’t shake:

Would you trust an AI that’s also an ad platform to give you an honest answer?

I don’t know you guys, but everyday feels like exploring a new world at this point…

The AI takeover of software development

They did try giving AI the keys to the critical stuff. Automatic transactions, live infrastructure, the systems where a wrong decision costs someone their savings or their medical history. Turns out “production-ready” is doing a lot of heavy lifting when the model occasionally just... confidently gets it wrong.
So the industry landed where it probably should have started: AI as a powerful ally to the engineer, not a replacement. It ships the tedious work faster, it’s genuinely useful, and yes - some jobs disappeared. The repetitive ones, mostly. But the dramatic layoffs you’ve been reading about are mainly from companies that over-hired in 2021 and needed a cleaner story for the correction. AI was a convenient headline.
The math was always the math.

What collapsed completely is the boring work.

Internal dashboards. Admin panels. The data visualisation tool three people in finance needed and nobody wanted to build. Scripts that existed purely to move a CSV from one place to another. All that stuff that used to sit on a developer’s backlog for two months because everyone had better things to do - gone.
AI eats that for breakfast now… and trust me, everyone’s celebrating.

And the developers? They’re fine. The ones I know are annoyed about the unnecessary meetings about “how to adopt AI better”. That’s the whole story.
Not an apocalypse - just a lot of suddenly-important meetings.

Musicians don’t have to deal with the corporate nonsense.
I mean… most of them, I’m sure Beyoncé has some corporate nonsense to deal with but you know what I mean…

Music is taking a walk on the wild side

There’s clearly a kind of music that purely exists not because anyone needed to express something, but because something needed fancy noise under it: corporate training video beds, podcast intros, app onboarding loops, YouTube background tracks, games that couldn’t afford a composer, the “upbeat and energetic” track on Shutterstock with 4,000 near-identical neighbours, etc. etc.

That entire industry is about to be automated.
Quickly… and by “quickly” I mean… it’s gone.

The uncomfortable truth - and I say this as someone who makes music and can easily start a fight about these topics - is that a lot of that work probably shouldn’t have cost what it cost.
Cause it’s is, will be and always had been a derivative copy/paste already.
The musician spending three days on a corporate training video bed wasn’t doing meaningful work. They were doing the dashboard. Competently, professionally, and for money they needed, sure. But it was always the dashboard.

AI does the dashboard now.
Nobody cried when that happened in software to be honest… if anything, I remember a few of them singing Ch-ch-ch-changes.

What AI actually does inside a DAW

Here’s where it gets interesting for people who actually make music.

The stuff that used to require either expensive studio time or years of ear training - AI is quietly eating through all of it. Quantisation that doesn’t make a performance sound robotic. Pitch correction that follows the natural movement of a voice instead of hammering it into a grid. Stem separation that would have taken a forensic audio engineer a full day now runs in two minutes on your laptop. You hum a melody into a microphone and the DAW tells you what key it is, suggests the next one, offers three variations in a different key and all that..

Compression and EQ - the dark arts that took engineers decades to develop intuition for - are now handled by tools that listen to your track and make decisions that would have cost you an expensive mixing session. Not perfectly. Not always. But good enough that the gap between “bedroom producer” and “sounds like a real record” has collapsed in a way that would have seemed insane to anyone working in music ten years ago.

And that’s before you get to the stuff that’s coming… AI that can clone the sonic signature of a specific vintage compressor, a specific room, a specific era of recording. The warmth of an analogue desk. The particular way a Neve 1073 sits on a vocal.
All of it becoming accessible to a 16 year-old with a laptop and something to say.

The kid with the idea but not the budget

For most of music’s recorded history, the barrier wasn’t creativity. It was money. Studio time. Engineers. Equipment. The infrastructure that the industry controlled and priced accordingly.

The result was a brutal filter - not for talent, not for ideas, but for access.
The kid with the genuinely strange vision and no connections either got lucky, got signed to something that would sand down the edges, or made music in their bedroom that nobody heard.

AI is dismantling that filter in real time by making the technical floor low enough that creativity is the only variable that actually matters.

The 16 year-old who hears music differently, who has something genuinely new to say, who couldn’t afford a mixing engineer and didn’t know what a sidechain was - that kid now has tools that handle the technical weight while they focus on the thing that was always the point.

And I don’t see how hat’s not a threat to music… that’s the best thing that’s happened to it in decades.

And the music industry can honestly fuck off

The music industry spent years telling us that Napster was killing music…
Before Napster there was MP3s. Before MP3s there was the CD burner, then it was MTV before discovering that music videos were the most powerful promotional tool ever. Then Spotify, which the industry spent years treating as barely legal before it became their primary revenue engine.
Basically everything but their poor judgment.

The same industry that signed artists to contracts designed to extract maximum value while paying minimum royalties. The one that was never particularly interested in art, but was extremely interested in catalogue, sync deals, and streaming numbers. The one that confused its own infrastructure with the thing the infrastructure was supposed to serve.

Those people can get comfortable with being uncomfortable, because what’s coming isn’t a Napster. It’s something they genuinely can’t absorb, acquire, or lobby against.

You can’t buy the creative explosion of a generation of kids who grew up with these tools and have nothing to prove to anyone.

I walked away from that industry’s logic a long time ago.
Watching AI hand the keys to the kids who were always locked out is so relaxing.
So yeah the industry can fuck off.

And for what it’s worth: everything I’ve said about music applies to every creative field. Writing, visual art, film, design. AI isn’t the thing that replaces the artist. It’s the thing that finally makes the artist the only thing that matters.

The kids are going to be fine. It’s the gatekeepers I’d be worried about.
So yeah… turn and face the strange fuckers.

Every few months the tech industry picks a new thing to declare dead.
Last year it was designers. Before that, developers. Before that, copywriters.
The pattern never changes - a tool drops, someone writes a breathless LinkedIn post, seventeen VCs share it without reading it, and suddenly an entire profession is obsolete according to people who’ve never actually done that job.

I’ve been talking to a lot of companies recently about their design and tech spend. And I keep hearing the same anxiety. So let me just tell you what’s actually happening out there. With numbers, because words are cheap.

The Poisoned Narrative

Claude designs your brand. Claude Code builds your product.
Twenty dollars a month does what used to cost fifty grand.
Game over, pack it up, learn prompt engineering or die.

Great story… if you sell that twenty dollars subscription.

What happened instead

The bottom of the market collapsed. That much is true. If you were grinding out five-hour logo jobs on Upwork - no strategy, just execution, whoever bids lowest wins - that’s gone. Down 17% in under a year.
Entry-level project availability on the platform fell from 15% to under 9% by 2025.

Honestly? Good. That was always a bad deal.
Those clients weren’t going anywhere.
You were just cheap labour with a Behance portfolio.

But here’s the bit that didn’t make it into the LinkedIn posts. AI-specialized designers - the ones who actually learned to use the tools - are now billing 25 to 60 percent more than generalists. The market sorted itself out. The execution layer got cheap. The thinking layer got expensive.

The part where the brands who went cheap start sweating

So a lot of companies looked at all this and thought: brilliant, we’ll just do everything with AI and pocket the difference.

How’s that going? Not great, as it turns out.

One in three consumers say AI branding actively damages their trust in a company. Not “doesn’t really move the needle.” Damages it. Actively. You spent three weeks prompting Midjourney and the customer who lands on your site trusts you less than they would have with no branding at all. Rough.

58% of people now associate AI-heavy design with startups that are running low on cash. So the cheap rebrand that was supposed to signal modernity is actually signalling to your customers that you might not make payroll.
That’s a pretty bad trade.

And the word for all this content - “slop” - got enough traction that Merriam-Webster made it word of the year in 2025. Mentions grew 200% over the year. 82% of the sentiment around it is negative.
The audience figured it out before the consultants did… HA!

Follow the money, as always

So who actually needed the “AI replaces designers” story?

Take a guess.

The people selling you the twenty-dollar subscriptions. That story was written for their pitch decks, not your business. “We eliminate the need for creative teams entirely” is a much bigger total addressable market than “we help your designers work faster.” One of those sentences raises a Series B. The other one doesn’t.

They weren’t talking to you.
They were talking to their investors.
This is not new information.

What to actually do with this

Use AI. Use it a lot. The people winning right now are the ones who picked up the tools early and figured out how to actually use them - not the ones paralysed by the discourse and not the ones who outsourced their entire creative brain to a chat interface.

Slash doesn’t credit the Les Paul as co-writer. The guitar amplifies what’s already there. If there’s nothing there, you just get louder noise. And right now the internet has more loud noise dressed up as branding than at any point in history.

The commodity layer is gone. Execution got cheaper. Thinking got more expensive.

The only question worth asking is which one you’re actually selling.

Sources:

- We and the Color Entry-level project availability on Upwork fell from 15% to under 9%
- Tapflare AI-specialized designers billing 25–60% more than generalists
- Clutch - 1 in 3 consumers say AI branding damages brand perception
- Salted Stone - 58% associate AI-heavy design with financially constrained startups
- Brandwatch - 82% of slop-related sentiment is negative

First, what Zero actually is

Zero is basically Rust, but simpler. Same explicit memory management. Same systems-level thinking. A borrow checker… but a weaker one. No LLVM backend, which makes the compiler faster but the output less optimised.
As someone who has written Rust (and C) in the past, the syntax immediately felt familiar to me.

So when Vercel says “AI programming language,” they’re not really saying “AI will write better Zero because, for some reason, it’s a language that AI seems to understand exceptionally well”. It’s not a language written in token IDs or something the models understand natively (even though at one point, reading discussions online, it almost sounded like that was the case).
What they’re actually saying is: we built a language where the toolchain was designed for machines to consume, not bolted on for humans as an afterthought.

That’s a different claim. And it’s actually more interesting.

What’s genuinely real… for real.

Three things. These are real.

One: The compiler speaks JSON natively. When Zero errors, it doesn’t print a message for you to read and interpret. It emits a structured JSON object with a stable error code. NAM003 means “unknown identifier.” Today, tomorrow, next version.
Most of programming languages added JSON output as an afterthought.
Zero made it the primary interface.
Honestly, that’s anyway something I would expect from a modern programming language with or without AI

Two: zero fix --plan --json. This is the one that caught my attention.
Zero tells the agent what to change and where - a machine-readable repair plan.
That’s pretty cool and matches with the previous point, the result is no guessing from an error description. The compiler hands the agent a fix plan.
That’s a genuinely different design decision… nice one!

Three: zero skills - version-matched guidance baked into the CLI. Run it, and you get a workflow guide matched to the exact compiler version you have installed. Meaning: the agent isn’t scraping documentation that might be three versions out of date. The guidance ships with the toolchain. That’s small and smart…

None of these are impossible to approximate elsewhere. Rust’s compiler has --error-format=json. You could build wrappers. But Zero made it structural.
First-class. The whole language is designed around that assumption.

Now, the part Vercel won’t say out loud

This is v0.1.2. No package registry. Limited cross-compilation.
Vercel themselves call it experimental and suggest you don’t use it in production.

And the “AI programming language” framing? That’s doing too much work. The language doesn’t make AI better at writing code. It makes the feedback loop better when AI is writing code.
That’s a very-nice toolchain improvement… but it’s dressed up as a paradigm shift.

I’ve seen Vercel do this before.
Let’s be real… Next.js didn’t invent server-side rendering - it packaged it beautifully and called it the future of the web. v0 didn’t invent AI code generation - it built the slickest interface for it and got the press cycle going.
Zero doesn’t invent structured compiler output. But it builds a whole language around that idea, ships it open-source, and calls it the programming language for agents.

Is that dishonest? Not really. Is it marketing? Abso-effing-lutely.

Here’s the thing: the core ideas in Zero… honestly are worth stealing.
I think they will influence how the next generation of toolchain designers thinks about agent-first developer experience. That’s real. Whether it’s Zero that becomes the standard or whether Rust and others absorb these ideas into their own tooling - that’s the more interesting question.

Should you adopt Zero?

No. Not yet, and Vercel knows it - they say so themselves in the README. If you put this in a production codebase today, that’s a you problem.

But if you’re building agent infrastructure, or you spend any time thinking about why agentic coding loops keep breaking down… honestly spend an afternoon with it. The Apache 2.0 license means no strings. The zero fix --plan --json command alone is worth understanding as a design pattern, even if you never ship a line of Zero. And the zero skills subcommand is a genuinely interesting idea for anyone building tools that agents will use.

The real question Zero is asking isn’t “should you use this language?”
It’s: is this gonna be the standard moving forward?

That’s a good question. I don’t care whether Zero is the answer.

The first zero-day ever written by AI just showed up in the wild.

Not in a lab. Not in a research paper. In an actual exploitation campaign, targeting real systems. Google’s Threat Intelligence Group confirmed it on Monday: an unknown threat actor used a language model to discover and write a Python exploit that bypasses two-factor authentication on a popular open-source web administration tool. The code had all the hallmarks of LLM output - clean structure, educational comments, and, brilliantly, an hallucinated CVSS score the AI invented for itself… nice, isn’t it?
The flaw it found was a semantic logic error.
A hard-coded trust assumption buried in the code.
The kind of subtle mistake that a tired human reviewer skips over and an LLM, having ingested millions of code examples, spots immediately.

This is the milestone people (including myself) have been predicting and quietly dreading. AI as an active participant in attack operations.

You’ve probably already been on the receiving end of something like this.

That LinkedIn job offer last month, written in suspiciously good prose, using your actual job title and your company’s real product names. The phishing email that passed every spam filter because, for once, it was genuinely well-written.
These aren’t flukes. IBM’s 2026 X-Force Threat Index confirmed AI-driven attacks are escalating across the board.
We just haven’t built the vocabulary to describe them yet.
We call them “sophisticated phishing” when what we mean is “AI-authored scams… at scale.”

The general sentiment among developers and security practitioners right now isn’t panic. It’s a very specific kind of fatigue. The r/cybersecurity community spent the first half of 2026 relitigating the same point: the old vulnerabilities never got patched. AI didn’t break anything that wasn’t already broken. It just lowered the bar for exploiting what was already there. Same shit. Less effort.

That’s the uncomfortable truth behind the Google disclosure. The attack worked not because AI is magic, but because the target had a logic flaw that nobody had bothered to fix. The mean time from CVE publication to working exploit is now roughly 10 hours in 2026, down from 56 days in 2024. AI didn’t create that problem. It accelerated it to the point where human response times are structurally incompatible with the threat.

But here’s where I want to push back on the “nothing new” framing, because something genuinely is new.

Up until recently, AI was the weapon pointed at you. What’s shifted is that AI has also become the target — and that’s a categorically different problem.

Think about what your AI tools actually have access to. Your codebase. Your credentials. Internal conversations. Customer data. Agent frameworks sitting on top of your entire stack with read-write access to half your infrastructure. That’s not a productivity tool. That’s the most valuable thing in your environment to compromise.

This week, a fake model on Hugging Face impersonating OpenAI’s newly released Privacy Filter model hit number one trending on the platform — 244,000 downloads and 667 likes in 18 hours before it was pulled. It was infostealer malware: screenshots, Discord tokens, crypto wallets, browser credentials, the lot. Dressed in OpenAI branding, copying the real model card verbatim. HiddenLayer’s research linked it to Silver Fox, a Chinese threat actor previously distributing ValleyRAT. Six additional malicious repos using the same loader were found. The attack hit the AI supply chain — the model marketplace itself — because that’s where engineers now go with their guard down.

And it’s not just model repos. Ollama — the local LLM framework with 171,000 GitHub stars — disclosed a critical CVSS 9.1 vulnerability this week (”Bleeding Llama”, CVE-2026-7482) that allows a remote, unauthenticated attacker to read the entire server process memory. API keys. System prompts. Conversation data. Affecting over 300,000 exposed servers globally.

I’ve been saying this for two years and I’ll say it again.

We built all of this fast, at scale, as productivity tooling. Nobody treated it like infrastructure. No threat modelling. No security review gates. The assumption was: this is internal, this is ours, this is fine.

Attackers have noticed that the AI layer is now the most exposed thing in the stack. Of course they have. And every vendor currently selling you AI tooling will, conveniently, also want to sell you the security layer on top of it.

So here’s the question worth putting to your team in tomorrow’s standup: do you actually know what AI tools are running in your infrastructure right now, and whether any of them are vulnerable?

Because if the answer is “not entirely sure” — that’s the answer attackers are counting on.

If I were building software today, I wouldn’t be building a product.
I’d be building an operating system.

I know how it sounds and no, I’m not talking about Windows or Linux but something closer to what Anthropic has been quietly assembling… lt me explain exactly what I mean.

Follow the Whales

To understand where the industry is going, ignore the headlines and watch what the big players are actually building.

Claude didn’t stay a chatbot. It’s now an extensible desktop application with agents, skills, third-party integrations, MCP support and so on...

Perplexity recently introduced the concept of “computers”, a system that “acts” on information.

OpenAI made the same move (of course, louder): ChatGPT launched Operator in early 2025, an agent that browses the web, fills out forms, and completes tasks end to end.

Google instead executed one quietly. In March 2026, Gemini went live across the entire Google product suite. Google Search added an AI Mode

Four companies. Same structural bet. The future isn’t a model you talk to. It’s a platform that acts… and that’s not breaking news… but it highlights the path IMHO.

The Gap

Here’s the problem with these big players right now: they’re built for everyone.
Which means, in practice, they’re optimised for no one in particular.

A tax accountant doesn’t need a general-purpose assistant. They need something that already knows tax codes, speaks the language of their workflows, and connects directly to the tools sitting in their existing stack. Same goes for an insurance underwriter, an e-commerce operator, a compliance officer and so on and so forth…

A general-purpose agent isn’t going to own those spaces unless you heavily fine-tune it. The depth isn’t there. And depth - in a specific domain - is exactly what enterprises will pay for.

That’s the gap. And it’s wide open.

The Blueprint Already Exists

Here’s what I also find interesting: you don’t need to reinvent the experience: Anthropic already built it.

Look at Claude Desktop. Left sidebar with task history. A persistent chat interface in the centre. A dynamic right sidebar for tools, context, and outputs.
That three-panel structure is essentially an operating system shell - and it’s the right model if you ask me.

The play is to take that architecture and make it vertical.

What “Vertical” Actually Means

Three things separate a generic AI platform from a vertical AI OS.
Get these right and you have a defensible product.
Get them wrong and you have a chatbot with a logo.

1. A custom knowledge layer

It goes without saying… you need a fine-tuned RAG system bolted on top.
A knowledge layer tailored on the actual documents that matter to that profession. Tax codes. Insurance policies. Product catalogues. Legal precedents… etc.
The agent needs to speak the language of that industry natively.

This is the foundation. Without it, you’re just reskinning ChatGPT.

2. Profession-specific tooling

Not generic integrations like Google Drive or Notion. The exact tools that specific professional uses every day, already there… already connected, already contextualised. An accountant’s stack looks nothing like an underwriter’s stack.
The tools need to reflect that.

The more tightly you match the tooling to the profession, the less onboarding friction you have. The product should feel like it was built for them… because it was.

3. Generative UI

This is the most underrated component IMHO.
You give the agent the ability to build its own interface for the tools it’s working with. It doesn’t just use the tools, it shapes how they’re presented to the user based on the task at hand.

Instead of the interface being fixed, the agent decides in real time what to render based on what the user is trying to do. Google Research described it as “an AI model generating not only content but an entire user experience” - and they’ve already shipped it inside Gemini 3. The UI isn’t loaded from a template. It’s composed on demand.

For a vertical AI OS, this matters more than it does for a generic platform. An accountant reconciling invoices needs a different interface than the same accountant filing a quarterly return. You don’t want to build both screens manually and maintain them forever. You want the agent to figure out what components need to be used.

That’s a fundamentally different product architecture - and it’s what separates a smart assistant from something that actually feels like an operating system.

Where I’d Start

The vertical you pick matters more than the technology you use. Pick something with a clear document-heavy workflow, a professional audience that pays for software, and enough complexity that a generic tool will always fall short.

Accounting. Legal. Insurance. Healthcare administration. These aren’t sexy. That’s the point. The horizontal AI platforms will spend years trying to reach them. A focused vertical product gets there in months.

The architecture is replicable. The domain expertise isn’t.