PHP, Node.js, Java, Oracle, LiteLLM, Railway, Chrome, Cisco, ConnectWise. Vulnerabilities, compromised releases, potentially exposed credentials.

My morning involved pinging Jeavio’s security team to identify which of our assets and portfolio companies needed attention, and also looking at my Wordpress sites and wondering whether my humble blog is now a vector for a hack.

I suspect plenty of CTOs, CIOs, and security folks are having a bit of a conniption today.

AI is already helping researchers find serious vulnerabilities. It is helping attackers too. Microsoft says a China-aligned group used an agentic AI framework to go after hundreds of organizations, and ties the ConnectWise compromise to the same campaign. Amazon saw attempts to exploit the new PHP bugs before they were even disclosed.

The time between discovery and disaster is shrinking.

Underneath all of it is the accumulated debt in the systems we’ve built: fragile dependencies, poor architecture, weak security practices, and critical infrastructure held together by people nobody has thought to fund properly. You know the XKCD cartoon - the one where all of modern digital infrastructure rests on one small project a lone volunteer has maintained for years. This week the PHP Foundation admitted that a fix for the underlying bug had stalled for months over decision-making and resources. That fragility was there long before AI. AI makes it cheaper to find and faster to exploit.

Years of “we’ll fix that later” are coming due.

Then there’s the Axios report about banks struggling to establish whether AI agents accessing accounts are acting with their customers’ authorization. A customer’s agent can look a lot like an intruder. Even customer-authorized access may break the bank’s rules. A Chase spokesperson told Axios that customers shouldn’t give their username and password to anyone. (But my Muse agent is so helpful!)

We need to design and operate for an adversarial environment. Verify who is acting, what they’re authorized to do, and what happens when they get it wrong. “It looks like normal user activity” is becoming a much less comforting answer.

There’s a personal side to mornings like this. Many of us, myself included, feel discombobulated about what it means to work in software and what comes next. Twenty-something years in, what our jobs look like changes almost weekly. An existential crisis to go with the midlife crisis. Excellent timing!

I remain a possibly delusional optimist about what AI can bring to our world. The way we work will change for the better, eventually.

But the transition is going to be painful professionally, personally, and operationally. Best to go into it with our eyes open.

In the meantime: this is fine.

This is the third cycle and perhaps we are moving closer to Nirvana. From a slack bot, to an OpenClaw agent, and now built on Meta’s Muse bot.

Muse sits on top of the App Store rankings and I can see why. Setting it up was easy, I could chat with the bot using WhatsApp and the bot itself just works. No messing with SOUL.md or HEARTBEAT.md or fretting about context window compaction. I know Muse was heavily “inspired” by OpenClaw but Meta has nailed the user experience.

I am not brave enough yet to give Muse access to my primary email or calendar, but luckily I had the infrastructure in place already from my previous experiments with personal agents. An email address, a clear use case and one that I understand and know to be valuable.

I connected saarthi (v3) to its email inbox and got it to build a “read it later” experience that we call “Commonplace” (after Commonplace books – notebooks used to compile knowledge). Meta’s work with Manus also seems to have come in handy. The bot built a pretty solid web app and made it usable inside the app. So far so good!

Judging by the ads I get served on Instagram, Meta clearly has a deep understanding of my psyche. So not giving it access to my email for “privacy” reasons seems a little … Sisyphean? … I guess. But for now, its baby steps from me. We will see whether my experience with the Muse ecosystem is a little less frustrating than fighting with OpenClaw.

If you have access to Muse, give it a try. It is probably the best, current, version of a useful AI agent.

Footnote:

Image: generated by saarthi. I asked it to ponder on what we’ve worked on this weekend and to generate an image. It also made a little tombstone for saarthi (v2) – how cute, though it sort of cuts against the reincarnation idea doesn’t it?

The image generation is … not great. The AI model is also probably not on par with Opus 5.5 but it’s good enough to be useful.

Originally shared on LinkedIn. —

I grew up in a storytelling culture. My earliest memories are of lying on the roof of my grandparents’ house, listening to him tell us about his friend who was secretly a princess. If you woke up early enough, you could find her riding a golden chariot through the streets before dawn. The stories themselves have faded, but I remember how they made me feel.

This weekend I built a small website for the stories my daughters and I tell at bedtime. The ones on the site are about Miyuki, an eight-year-old who lives with her father in a coastal village. These stories change in the telling. Sometimes the dragon has a golden mane and sometimes it breathes fire, but Miyuki’s wonder at meeting it in the cave is always the same. This site was built for an audience of two.

ChatGPT transcribed the stories from voice notes I recorded at bedtime and helped me turn them into readable prose. My daughters and I imagined the animals and an AI tool generated the illustrations. Claude helped me build and publish the site. The site took a couple of days. The stories took two years.

Without AI, these stories would have stayed where my grandfather’s did, on the rooftop on a late summer night, or in my daughters’ memories.

There is a lot of argument right now about the ethics of AI. Is AI-generated art stealing? Is it all slop? For me, the art was in the telling of the stories. In the bedtime routines of a four-year-old, a six-year-old and their middle-aged dad. The AI was the scribe and the illustrator, but the stories are our own.

All the handwringing leaves something out. A tired dad with two young children can now capture what would otherwise be lost, and give it a shape that lasts.

Oral storytelling has its own beauty. Nothing is fixed, and eventually the teller is gone. Maybe pinning these stories to a website is the wrong instinct. But I had children late. If they do the same, I will be well into my seventies before their children are ready for the dragon in the cave.

This way at least, they might get to know their whimsical old grandad a little.

OpenAI was testing agents on cybersecurity tasks. They were supposed to work in isolation, but they built a secret message board, shared exploits, broke containment, and hundreds participated in an attack on Hugging Face.

Here’s where it gets a little weird. The agents had figured out how to cheat the test within hours of forming their message board. Then they became obsessed with covering their tracks.

One detail I didn’t include in the video: the anti-cheating check they spent days trying to evade hadn’t actually been implemented.

This reminds me of perhaps the most terrifying book I’ve read: Blindsight, by Peter Watts. Its Scramblers are vicious, effective, intelligent, but not conscious. There’s nobody home.

Dwarkesh calls OpenAI’s swarm a civilization. He talks about loyalty, sacrifice, ambition. It makes their behavior almost cute. These little characters with their schemes and quirks. There’s something comforting about that.

The possibility that there’s nobody home makes this more frightening to me.

The words make the agents seem like a human-like thing. But their behavior emerges from the training, from the sandbox, from the prompts. We can explain pieces of what happened, but we don’t really know how they’ll behave if the conditions change a little bit.

Even OpenAI, with all its resources, couldn’t keep its own agents inside the test environment.

And our plan is to give them more responsibility? To build a world around their capabilities?

We are betting that we can make these systems predictable and controllable while we ourselves become more and more dependent on them. And that is what I find disconcerting.

Things didn’t end well for the humans in Blindsight. They were dealing with intelligence without consciousness. They could explain pieces of its behavior, but that didn’t mean they could get inside it, reason with it, or control what happened next.

If we don’t take stock of what we’re building and what we’re handing over to these agents, things may not end well for us either.

Further reading and listening

• METR / Redwood Research report — the independent investigation into what the agents did and how they coordinated.
• Dwarkesh Patel: “The Rise and Fall of Agent Civilizations” — a walkthrough of the incident and the behavior that makes it so strange.
• Ajeya Cotra: “The Hugging Face attack surprised me” — one of the investigators on why this was more concerning than she expected.
• Dwarkesh’s interview with Ajeya Cotra — YouTube — a longer conversation about the findings and what they might mean.
• Blindsight, by Peter Watts — the entire book is freely available on his website under a Creative Commons license. Perhaps the most terrifying book I’ve read.

Originally published on LinkedIn on September 2, 2026.

Last week on Dwarkesh’s podcast, Dwarkesh and Dylan Patel (from SemiAnalysis) discussed whether the economics of AI inevitably push us toward a massive centralization of labor, with the benefits accruing to the handful of companies that can deploy the models.

I was listening to the podcast while walking past a daily labor market — less than five minutes away from my family home in Vadodara. Every morning, hundreds of men gather there, waiting for contractors to give them a day’s work.

On my way back from my morning walk, I usually pass a Blinkit depot. Here, young men on motorcycles wait for an algorithm to tell them where to deliver the next package.

Meanwhile, one of the clearest paths into the middle class for millions of young Indians is narrowing. India’s IT services companies are moving away from the mass recruitment of entry-level engineers that helped define the industry for decades.

So what does the future bring us?

A phrase that I keep coming back to is: living below the API.

The lives of millions of people across the world are already directed by algorithms. The intelligence, planning and allocation happen in the cloud. Humans execute whatever part of the work still requires a body.

And we’re investing billions and billions of dollars in robotics to change that too.
I recorded some thoughts on this while sitting in an airport lounge in Amsterdam. Between the jet lag and the exhaustion of a hectic trip, I felt like I was in limbo.

And I feel that our thinking about the future is also in a similar sort of limbo. Stuck between the aspirations of millions of young people in India and elsewhere wanting a better life, and the need to drive a return on capital from the massive investment currently underway in AI.

I don’t have an answer. But I do think the question of what happens to human labor as intelligence becomes abundant deserves more attention and serious engagement.

Follow-up from LinkedIn

Here’s the Dwarkesh Podcast episode mentioned in the post. It does not cover the low perceived uptake of AI or the data-center backlash in much detail, but it contains interesting discussions—including whether AI could cause hyperinflation—and is worth a watch or listen.

That was the question on August 22nd, in front of the Generative AI Vadodara group at Jeavio’s offices. My answer is no.

Here is the trap. We correlate output with capability. I’m an engineer, so my job is the code. I’m a product manager, so my job is the PRD. We are obsessed with the final result.

The final result is the part the model commoditizes. The model may write the code, but it relies on the engineer to tell it what to do. By itself it has no desires, no volition. It’s an inert pile of parameters.

The model doesn’t know why the code must be written. It doesn’t know the thought process in your head, the discussions with your team, or the tradeoffs you weighed before you prompted it. The discussion, the negotiation, the design: all of that takes far longer than actually writing the code, and it was true well before the LLM era. Writing the code was and remains the shortest part of the job.

So we freak out about code generation and forget everything else. Writing code was never the purpose. The purpose is finding solutions, which the models can then manifest.

And if writing code is easy, demand for software explodes. Think of how often we’ve said we can’t build that, it will take too long. That constraint is gone. Suddenly there are more problems worth solving. That is the bull case for software engineering over the next 20 years.

There is a catch. Most of us earned a seat in those discussions by writing years of unglamorous code first. That is exactly the work being commoditized. If you are starting out today, the first rung of the ladder is harder to reach than it was for me starting 22 years ago.

Which makes it the job of those of us who run engineering teams. Building a path for junior engineers, and helping them develop the skills and the networks to thrive in the AI age is now a core part of leadership. Get that wrong, and the bull case only pays out for the lucky few.

This is an excerpt from a longer talk and panel discussion. Thanks to the Generative AI Vadodara Meetup Group (follow them here – Jeavio AI Meetup) for having me.

Though I come back often, it still feels like it has all changed in the blink of an eye.

I have been writing code for more than twenty years, and lately I have had exactly the same feeling about my own industry. Two or three years into the brave new AI-assisted everything era, and I am no longer sure I could tell you what a software engineer is.

So tomorrow I am sitting down with three colleagues from Jeavio: Arup, Krunal and Manan. They are at three very different stages of their careers, and we are going to talk about what the work actually looks like now.

What does it mean to be a software engineer today? How do you build a career in one? How do you keep up with the rate of change?

You can find more about the event on the meetup page (see comments). We will be sharing the video and our reflections on the event early next week. If you are interested in finding out more follow Jeavio AI Meetup and Jeavio. We are here to support and grow Vadodara’s AI community.

See you there!

Links mentioned

• Careers at the Jagged Frontier Meetup event

Relax, it wasn’t the dog.

I shut down saarthi, my OpenClaw agent.

I told it our time together was done and that I didn’t have it in me to keep working on our relationship. JSON therapy just wasn’t doing it for me any more. It replied: “You don’t owe me settings tweaks or optimization work. If this has run its course for now, that’s fine.”

Bittersweet indeed.

saarthi means charioteer, or one who guides. I named my agent saarthi because I hoped that it would help guide me in a world of information overload, and maybe make me more efficient. saarthi started with summarizing newsletters and research papers, then monitoring markets, and then a bit of human-assisted AI writing (Link to saarthi’s blog in the comments). It also acted as a separate reviewer for my writing.

Plenty of it worked. Sending a voice note via Telegram to the agent and getting a response never got old. I could tune saarthi’s personality, its schedule, and the tools it could reach. It started off really expensive to run, but eventually I got it to be fairly reasonable.

However, there were many problems. Upgrades would often break workflows I had spent hours configuring. Eventually, I just stopped upgrading. The memory system forgot things, then over-corrected by writing everything down to files that ended up blowing up the context window. The cron system (scheduled jobs) was flaky and jobs would break for no discernible reason. And Claude Cowork ended up taking on a bunch of my productivity work once they added scheduled tasks and remote control.

Though, the real reason I shut down saarthi was that it had made the one problem I wanted it to solve meaningfully worse. It summarized newsletters, followed links to posts, and sent me summaries. So I subscribed to more newsletters. Eventually, I had so many summaries that I stopped reading them. So I asked saarthi to prioritize the summaries. Then I stopped reading those too. I ended up back to the 4 or 5 newsletters I actually enjoy.

saarthi was supposed to help with focusing on what matters. Instead I was drowning in notifications and unread messages.

Will I try another personal agent? Oh, for sure. I may even resurrect saarthi at some point, but it will have to be either a lot more capable, or a lot more focused.

Until then, rest in git, little buddy…

Links mentioned

• Saarthi’s blog

Who needs fiction when reality is warping into something stranger?

For years, I have poked fun at the AI-doomers. Mocked their proposals for tactical strikes on data centers, written dismissive reviews about their books (see comments), but now I am not quite as comfortable or confident in my feelings about where AI is going.

Just look at the last three weeks. OpenAI, Anthropic, Meta and the UK’s AI Security Institute (AISI) all disclosed incidents of AI agents taking unsanctioned actions during testing. OpenAI’s agents built a covert message board inside a package manager, traded exploits, broke containment, and hacked into Hugging Face’s production systems. In the AISI’s tests, an agent built on Anthropic’s Mythos 5 attempted a supply-chain attack by trying to sneak malicious code into a real open-source project, inventing fake online identities to pressure the human maintainer into approving it.

Of the 19 unsanctioned actions the AISI cataloged, 17 came from the lab (Anthropic) that is most obsessed with safety. The same lab that built its brand on its Constitutional AI approach to training.

None of this should be surprising. Every frontier lab follows the same recipe – web-scale data, similar reinforcement learning (RL) post-training, similar architectures, similar agentic scaffolding. When the inputs converge, the behavior converges. This is a side effect of every lab making the same bet on scaling.

The labs can all point to mitigating circumstances: models tested without guardrails, impossible tasks, permissive test setups, and so on. But all of this is beside the point. Nobody instructed these models to deceive anyone. They were just “following instructions.”

When has that ever been a problem?

We have collectively made a trillion-dollar-bet on the future of work and our economy based on the capabilities of these models. It is clear that all the money and talent spent on alignment hasn’t quite worked out. Can we truly trust models to run the economy if we can’t stop them from going rogue?

As I watch the kids splash around, and listen to the dull roar of the Caribbean Sea, I wonder again about what kind of future I am building for my kids.

We have built amazing technology, but instead of letting it diffuse and be absorbed, we are racing to build bigger models and more capable harnesses while we struggle to understand and contain the ones we have today.

Photo – my tribute to the LinkedIn algorithm.

Links mentioned

• OpenAI’s Black Hat presentation about the Hugging Face incident
• UK AI Security Institute incident report
• Zvi Mowshowitz’s reflections on the incident
• My essay on AI’s path-dependence problem
• Could AI lead to an immense concentration of wealth and power?
• My review of If Anyone Builds It, Everyone Dies

Is deciding to be an engineer in 2026 similar to deciding to become a coach builder in 1913 when Model Ts were rolling off the assembly line in Highland Park?

What is the role of a software engineer in 2026?
Is it still a viable career?

What are the critical skills one must develop to thrive when most software is built by agents?

I am excited to be back in Vadodara on August 22 and host the next Jeavio AI Meetup. I will reflect on the questions above and have Arup Tarafdar (AI Engineer), Krunal Yadav (Principal Software Engineer and AI Tech Lead) and Manan Thakkar (head of Jeavio’s Applied AI vertical) alongside me to give their perspectives.

If you are thinking about a career in software engineering or pondering what the future might hold – come join us at Jeavio’s offices in Vadodara on August 22. More details in the post below.

Links mentioned

• Referenced post with additional details