Misc

Arsenal and the Passage of Time

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What did Arsenal winning the Premier League title teach me about applied AI?

Nothing.

Nothing except that 22 years is a long time. That two decades of frustration, near misses, and grim results can dissipate in a giddy moment.

When the Invincibles won the league in 2004, I was about to start my first job. Thierry Henry reigned supreme, and London awaited as I looked to start a new chapter of my life.

Time compresses as I lean into middle age. Some players who will lift the trophy at Crystal Palace on Sunday were not born when Henry, Gilberto Silva, and Sol Campbell lifted the trophy at White Hart Lane last time around.

The years since have seen the curtain coming down on the Wenger era, and the painful launch of the Arteta era. They have seen the world consumed by mobile phones, social media, and now AI. They have seen the nature of being a sports fan transform into mean-spirited online banter (a marginal improvement over mean-spirited real-world violence I suppose), the sport itself plastered with gambling and crypto ads.

And yet, the nature of fandom remains the same.

Frustration as the team grinds out another 1-0 result.
Superstition when a friend calls Arsenal champions 3 minutes before the end of the Man City game. Only for Haaland to score. Because of course he would.
Relief and a stream of emojis sent out to relatives and friends in different time zones at the final whistle.

I don’t have much to say. Sometimes it’s good to let the frivolity of being a sports fan consume you. Sometimes it’s good to be reminded that some things take 22 years.

I’ll leave you all with a panel from one of my favorite Arsenal-related cartoons from David Squires at The Guardian – “The North Bank Redemption” (link in comments). This strip was published when Wenger left Arsenal.

“hope is a good thing, maybe the best of things.”

COYG!

Image accompanying the original post about Arsenal, time, and applied AI.

Links mentioned

The Spigot

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I keep thinking about how we ended up here.

My kids are three and five. I have been in this industry for twenty two years. You would think that somewhere along the way I would have put together a coherent mental model of how the technology industry works, and make sense of what is happening. Something that I could wrap up as wisdom, or advice, or maybe just a pointer as my kids think about their future in a few years time.

Instead, I have been reacting. Lurching from one cycle to another. Mobile, Cloud, Crypto, AI and whatever comes next. As I navigate each cycle, like a befuddled tourist, the scale gets bigger. Each time, it seems that fewer people are thinking and the explanations for what is happening and why are more confused.

So I have been reading. Postman. Karen Hao’s Empire of AI on Sam Altman and OpenAI. Sebastian Mallaby’s The Infinity Machine on Demis Hassabis and DeepMind. Kyla Scanlon on the strange economics of the moment. And I have been trying to put together a through-line. The outline of a semi-coherent narrative that gestures towards why we end up here – arguing about data centers, fretting about AI, as the world literally burns.

I think it starts with a banner ad.

October 27, 1994

Bill Clinton was President, and Marc Andreessen had just released the Mosaic web browser just a year earlier. On that date, Wired Magazine’s digital spinoff, HotWired, launched its website with about a dozen paid advertisements. One of them was for AT&T. It was 476 by 56 pixels. It asked: “Have you ever clicked your mouse right HERE? You will.” Clicking it took you on a virtual tour of the world’s great museums.

The world’s first banner ad

By today’s standards, it was quaint. I suspect it was quaint even by the standards of 1994. But it was the beginning of something that would birth an enormous range of products and services: from social media to AI slop. But at the time, nobody involved thought they were the catalyst to the creation of a giant industry.

Within a year of HotWired’s launch, it was followed by Lycos, Excite, InfoSeek, and others – each company created with advertising as their primary business model. By the time Google created AdWords in 2000, the model was established. By 2014, Google alone was generating $60 billion a year in ad revenue. In 2025, Google brought in nearly $295 billion.

Nobody planned this. Andrew Anker, the former investment banker who wrote HotWired’s business plan, settled on advertising because it was, as an oral history of the era describes the only logical revenue stream he could envision. So, the humble beginnings of the business model that today underpins Meta,Google, and one that upstarts like OpenAI are staking their future on. An incidental decision, that became possibly the most profitable business model ever invented.

The same decision was also the inflection point from where technology went from being the domain of a handful of nerds and dreamers in a sleepy suburb of San Jose to the dominating social and economic force it is today. It is also where the trouble starts.

The Spigot Opens

Digital advertising became the canonical business model as the world went online. Technology meant zero marginal costs, global reach, and rapid dissemination. It generated staggering amounts of surplus capital concentrated in a handful of companies. Google, Facebook, Yahoo before them and many others to follow. The venture capitalists who were early investors in these companies generated mind boggling returns. Serving targeted ads at scale became a money printer.

All that surplus capital needed somewhere to go.

Capital chases returns. When you have that much money looking for a home, the bar for “this might work” drops dramatically. The entire Venture Capital business model is predicated on finding that one unicorn that would generate massive returns. You can see multi-billion dollar bets on businesses leasing office space for a loss while wearing the accoutrements of a technology company were justified.

For a VC, sitting on uninvested funds means your fund underperforms, your investors look to greener pastures. So you fund whatever has the ceremonial language of disruption. You fund NFTs, you fund fancy juice makers, because the alternative would be irrelevance.

The Juicero (RIP) – disrupting the fruit squeezing industry

The post-pandemic crypto boom seems like a fever dream now. Billions raised on the promise that pixelated pictures of digital apes would one day sublimate into collectible and unique art. Investors poured money into automated money-making machines based on complicated DeFi (Decentralized Finance) protocols that required a PhD in applied mathematics and the willingness to suspend disbelief.

I wasn’t immune. I spent way too much time trying to understand how Bitcoin works (and wrote a terrible science fiction story trying). I spent my hard-earned money on staking out some space on the blockchain. Money that could have gone to my kids’ 529 plans. Sorry girls. I wasn’t the outlier though. People way smarter than me poured millions into something that was, when you stripped away the jargon and the vibes, the world’s slowest and most expensive database.

How did this happen? How did so much capital get allocated (and is still allocated!) on something that is so obviously broken?

It is downstream of the spigot. There is so much money in the system, generated by that initial accident of online advertising, that it has to go somewhere. And the job of any ambitious entrepreneur is to provide it a plausible channel. Once the money starts flowing, it becomes self-reinforcing. More money validates the narrative. The narrative attracts more believers. More believers attract more capital. FOMO reigns supreme. The cycle runs until something breaks the spell.

Technopoly

Neil Postman wrote Technopoly in 1992, before the Internet was really a thing. I finished it a few weeks ago and it’s been rattling around in my head ever since.

Postman, a famously skeptical and uncompromising media critic, defined a technopoly as a society that has surrendered its decision-making to technology. Not in a “I welcome our robot overlords” way, but through a blind submission to metrics and statistics of dubious value. His examples – IQ tests, a man “drowning in a river that is, on average, four feet deep”. A society where technology becomes dominant through a self-perpetuating loop where it drives investment in itself, regardless of the impact on human-well being. Does this sound familiar?

Postman didn’t believe resistance was futile

I am also reading Sebastian Mallaby’s The Infinity machine. What comes through in his account of Demis Hassabis, Elon Musk, Larry Page and other industry titans is just how each of them operates with a profoundly different view on Artificial Intelligence – set to be the dominant technology of the 2020s and beyond. Hassabis comes across almost monk-like – viewing AI as the means to understand the nature of existence. Page as a transhumanist who sees the body as a shell to be discarded and the machine as the vessel for eternal life. Musk as a self-appointed guardian of a very particular vision of humanity – presumably with him as some sort of God-Emperor. These are the people who are deciding how and where the most consequential technology in a generation is deployed.

Consider the data center buildout. Trillions of dollars of private capital are being deployed to construct AI infrastructure at a pace that makes the railroad boom look modest. Even adjusted for inflation.

The rationale behind the investment seems to follow a circular logic. AI requires compute for training and inference, so you build data centers and fill them with compute. You fill them with compute which makes rapid deployment of AI possible and sparks massive competition between foundation model companies and hyperscalers to build, fundamentally, the same things. That in turn drives further demand for compute, and so on.

And AI is not just another speculative bet sitting alongside the economy. In 2026, it is the economy. Kyla Scanlon put it plainly: as AI swallows more and more capital, it has become the stock market and the economy simultaneously. The same companies – Microsoft, Google, Amazon, NVIDIA, etc. bankrolling the AI infrastructure buildout are also its biggest customers. Data center construction and investment in AI is driving GDP growth without growing jobs. Scanlon calls it a “jobless expansion“.

The prosperity exists in balance sheets and in the giant data centers sprouting up along state highways in places like Northern Virginia and Tennessee. It does not exist in communities that have been hollowed out as manufacturing evaporated and a career meant becoming the meat interface of a faceless algorithm directing you to the next gig.

Postman would have recognized this instantly. Nobody is in the driver’s seat. No single actor decided this was a good idea for society. No democratic process approved it. The technology cycle itself is driving societal change without any significant discussion. We have abdicated to the technology itself.

I wrote this in my notes on the book: “FOMO-driven investment in AI data centers with little to no prospect of broad societal benefits. We could have spent this money on climate change remediation or education or universal healthcare but here we are.”

Here we are.

The Arbitrage Trap

So there is a clear sense of a backlash brewing against technology. Try posting a pro-AI take on Threads or Bluesky and see the reaction. It’s not just the keyboard warriors who are ready to fight. A young man threw a molotov cocktail at OpenAI CEO Sam Altman’s house last weekend. Politicians who oppose moratoriums on data center constructions are threatened. There is a sense of rage as a new generation emerges into the post-ChatGPT landscape of disappearing knowledge work and the deep sense of careers and meaning being stolen by algorithms and trillion-parameter models.

But there is a key point that is missing in the public discourse. When Bernie Sanders “debates” Claude, it isn’t a meeting of equals. Bernie is an influential Senator and can call for moratoriums, hearings, and regulation. But Anthropic just raised billions of dollars in private capital. Claude runs not on the public dime, but on VC dollars. If Sanders pushed for a ban on data centers in Vermont, they will just build them in North Dakota. It doesn’t really matter where the data center sits. If the capacity is built out, compute will be deployed, and that compute will reshape the day-to-day work of people whether they want it or not.

Hope Bernie has a Claude Max plan

Jasmine Sun, who writes one of the sharpest Substacks on AI and Silicon Valley culture, spent time in DC and San Francisco earlier this year tracking what she calls “AI populism.” Her observation is stark: there is a widening chasm between the people who are building and funding this technology and the people who will live with its consequences. Politicians gearing up for the 2026 midterms are scrambling to design their AI agendas. Labor unions, environmentalists, social conservatives are all rushing to come up with a position.

But the backlash lacks a mechanism to break the capital flow. Moratoriums work only if they are coordinated and enforced. Otherwise you just get regulatory arbitrage. Capital exits to friendlier jurisdictions, and the places that resisted end up with neither the investment nor the jobs. That is the trap.

And if you want to see the arbitrage logic taken to its absurd conclusion, look no further than Elon Musk’s push for building data centers in space. As they say, in space, no one can hear your strident demands for a datacenter moratorium.

Spending a trillion dollars to deploy millions of GPUs in space seems insane, but it is also internally consistent with the incentive structure.

The Ratchet

The AI investment cycle can be best described as a ratchet. It moves only in one direction before locking in place. We seem to be committed to seeing where this cycle plays out – even if it ends in tears.

Sun points out the worst case scenario in a recent post – “One nightmare is a future where we get AI that’s good enough to wreak social and economic havoc, but not yet good enough to cure cancer / solve climate change / deliver 10% GDP growth. In that world… who pays?”

I work with AI every day. I run teams that build with it. I can see the utility. I have watched it compress weeks of work into hours and deliver real value for my clients. I am no old man shaking my fist at the clouds. But I also know that when the backlash arrives in full force, when the torches are lit and the pitchforks come out, the distinction between “I used AI thoughtfully” and “I profited from AI” will not matter. I worked in investment banking in 2008, I know what it means to be a social pariah.

People like me, who have bet careers on this technology being useful, will be caught in the same sweep as the people who bet billions on it being transformative. Everyone on the ratchet moves in the same direction. And yet, apart from the odd paper, there are little to no concrete suggestions from the same billionaires about how to make technology work for everyone apart from some vague gesturing to super-intelligence and to abundance.

And what feels like willful delusion rather than mere miscalculation is that the ratchet keeps clicking forward even as the world around it deteriorates. The United States, Israel, and Iran are in an active military conflict with direct strikes and counter-strikes. The Strait of Hormuz, through which a quarter of the world’s traded oil passes, is under threat. Russia’s invasion of Ukraine grinds on. These are the kinds of events that should be sending capital fleeing to safety. Instead, the markets shrug and carry just .. carry on?

Trying and Failing to Understand the World

I started this year realizing that I did not have a coherent mental model to explain what was happening. I read Postman, Hao, Sun and Scanlon to try and see how others made sense of a world so utterly dominated by technology and the eccentric billionaires who control it.

I ended up with a series of explanations that seem to involve some sort of hardware. Spigots, ratchets, and data centers in space. But I do not think I have a mental model. I do not think I can predict what comes next except a vague feeling that we will continue to spin faster until the whole edifice comes crashing down or we ascend to the singularity.

Postman’s views on the subservience of humanity to technology appeal to me because they seem to be manifest everywhere I look. From people scrolling aimlessly on their phones to pouring out their darkest secrets and deepest fears into the maw of a trillion-parameter language model. It is grim stuff.

But Postman also offers a solution. He calls it a “thoughtful rebellion.” Maybe the movement to touch grass, the surging sales of physical books and vinyl are signs that there is a genuine desire to disengage from digital technology. But how much of that movement is itself driven by mimetic desires pushed by algorithms, through BookTok and the like?

Perhaps technology provides its own means of meaningful disengagement. And maybe that is the only, if unsatisfactory, answer.

My kids will inherit a world shaped by decisions nobody consciously made, funded by a torrent of money nobody voted to spend, run on infrastructure nobody asked for. The best I can do is to show them the machinery that drives the world. And hope that they can find a way.


Who Gets to Decide?

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The Anthropic vs. Pentagon standoff can be seen as both a contractual dispute and another skirmish in the never-ending culture war that has consumed public discourse in the United States. It is neither, and it is both.

Another way to think about “Claude-gate”, I guess, is as two separate questions that are being collapsed into one. These questions are:

  • Is the Department of Defense (recently rebranded as the Department of War) acting legitimately here?
  • Do Dario Amodei and the folks at Anthropic have the legitimate authority to decide how a civilization-scale technology gets used?

To answer these questions, we need to explore what AI is, how it is evolving, and why this particular dispute may be the first of many difficult questions we, as a society, will have to deal with in the coming days.

The answer to the first question is straightforward. The DoD did not act legitimately.

The Pentagon signed a contract with Anthropic in July 2025, under the Trump administration, with agreed usage terms. Those already included the two restrictions – on using models for domestic surveillance and in autonomous weapons. Anthropic was already a willing partner, and its models were deployed for both offensive and defensive purposes.

Then, in January 2026, the DoD demanded renegotiation. They wanted to use the models for “all lawful purposes” – effectively removing the carve-outs. When Anthropic refused, the DoD threatened to call Anthropic a “supply chain risk to national security.” This last designation, until last Friday, had been reserved for foreign adversaries. Huawei and ZTE, both Chinese companies, saw their US businesses destroyed after being labeled as supply chain risks. The DoD was given six months to transition away from Anthropic, and other vendors were asked to comply “immediately.” They also threatened an invocation of the “Defense Production Act” – effectively nationalizing Anthropic.

To quote Amodei:

“those two threats are inherently contradictory: one labels us a security risk; the other labels Claude as essential to national security.”

The second question is a lot harder to answer, and I think, more interesting.

The Truman Principle

Ben Thompson, who writes Stratechery, and Gregory Allen of CSIS (the Center for Strategic and International Studies) explored this in a recent interview. They talked about the parallels between this moment in AI and the Manhattan Project. Thompson’s take was that if a private company had stumbled onto nuclear fission in 1944, the State would have nationalized it without debate.

The question is whether AI warrants the same treatment.

Thompson makes two arguments:

  1. The role of politicians: Politicians are best positioned to make decisions about transformative or disruptive technology because they represent the will of the people. Their job is to integrate across domains. Experts, in contrast, see through a single lens. What makes sense in one dimension might be actively harmful in the light of others. Someone has to weigh these dimensions against each other. Experts can’t, because their expertise is precisely what prevents them from doing so.
  2. Democratic legitimacy: Truman decided to use nuclear weapons on Hiroshima and Nagasaki. That decision wasn’t morally uncontested then, and isn’t now. But, as Commander-in-Chief, it was his to make. He had input from scientists (such as Oppenheimer), military planners, and moral advisors. He also had to face an electorate that was tiring of war. He made a decision that would eventually be judged by the ballot box.

Applied to the current dispute: Amodei is not elected. He is a scientist whose company has built the most capable AI today. Anthropic’s worldview and its safety philosophy, however sincere, is that of a small group of extremely privileged (and AI-pilled) people in San Francisco. The argument that they get to set the terms of how a sovereign government uses this technology is hard to sustain on democratic grounds. And as bombastic as Pete Hegseth is, he is ultimately the representative of an elected government.

Thompson’s arguments are persuasive. But they rely on an equivalence between AI and nuclear technology that I don’t think holds.

Where the Analogy Breaks

The Manhattan Project comparison is a useful starting point. Amodei’s take is that AI (or more specifically AGI – Artificial General Intelligence) will be a technology that has world-changing implications. He takes the nuclear parallel seriously himself. His favorite book, according to Kevin Roose at the New York Times, is “The Making of the Atomic Bomb.”

But this analogy breaks in a couple of different ways.

A secret government project vs. a widely dispersed and publicly available technology

The Manhattan Project was a secret that could be maintained until its explosive revelation to the world. AI capability hasn’t been a secret. Large Language Models are based on a research paper published by Google back in 2017. LLM capabilities rapidly diffuse through academic papers, experimentation, and other creative approaches. Publicly available open source models are only a few months behind the cutting edge.

Anthropic is on an annual run rate of $20bn as of March 2026. The DoD contract was for $200m. These are significantly different numbers. The use of Anthropic’s models in enterprise and by “regular people” dwarfs the potential national security use cases.

This means the “nationalization window”, if it was ever open, may have closed before anyone noticed. The genie is out of the bottle, and anyone can now have their own AI assistant.

There is a counterargument worth acknowledging: access isn’t the same as control.

You can use Claude on your desktop, but running a frontier model is beyond the capabilities of consumer hardware. Training and running frontier models like Claude Opus requires datacenter-scale infrastructure that only a handful of entities can acquire. This means the real nationalization question is about access to compute and electricity, which is a narrower problem, but isn’t really being discussed apart from local opposition to datacenter construction.

Same capabilities in different dimensions, or “you can take Claude from my cold, dead hands.”

As a subscriber to the “Claude Max” plan from Anthropic, I have access to a model with capabilities (reasoning, planning, synthesis, persuasion) that are categorically similar to those used by the DoD.

Similar capabilities, different magnitudes, and used for different use cases. After all, a long-running safety concern from the likes of Amodei is terrorists using commercially available LLMs to build weapons of mass destruction.

Nuclear technology has civilian applications too: power plants! But nuclear weapons are not the same as nuclear power. These are different capabilities. With AI, the differentiation happens at the application layer. The same model that helps me think through this blog post could be used by the NSA to find security vulnerabilities. If you decide to restrict the use of one, you must restrict the use of the other.

To put it another way, any serious attempt to wall off military-grade AI capabilities necessarily implicates consumer access. That is not going to be a popular position given how quickly this technology has dispersed and become part of mainstream knowledge work. Now, the argument could shift considerably if we were to face a crisis caused by an AI-enabled attack.

In any case, the nationalization frame, however intellectually coherent, is not a realistic scenario.

The Breaking of the Accountability Loop

There is a deeper thread running underneath the contractual dispute.

Democratic governance rests on a transaction: citizens pay taxes, serve on battlefields, and sustain the economy. Elected officials represent their citizens. Rights, franchise, the welfare state, the GI Bill are what the state offers its citizens.

This loop has been fraying for years. Contractor armies, drone warfare, and now AI: each reduces the state’s dependence on broad citizen participation for force projection. AI is already being used for offensive operations. Gregory Allen describes a scenario in which AI agents could multiply the NSA’s offensive cyber capabilities, from thousands of human hackers to millions of AI hackers, enabling a debilitating first strike.

The trajectory of AI-enabled military capabilities points towards radically reduced dependence on human labor. Drones don’t unionize. Autonomous weapons systems don’t get tired or mutiny. When the state can project force without relying on citizen soldiers, the mechanism that historically forced political accountability from capital to labor begins to erode.

The Anthropic dispute is a preview of the challenges to come. The government is attempting to control AI capabilities while the people building the technology object to specific applications, and the people affected by those applications (mass surveillance, autonomous weapons) have no visibility and little understanding of the decisions being made.

The Trump administration’s approach of maximum velocity has gotten inside the OODA Loop (Observe, Orient, Decide, Act) of how government is supposed to function. Anthropic will sue the DoD over the supply chain risk designation and probably win the case eventually. Hegseth and Trump both used social media to issue directives before Anthropic was notified or before there was the chance of any legal due process. The calculation in this, and in many other instances, was to control the narrative and rely on the institutional processes being so slow as to become irrelevant.

(”We have always been at war with Eastasia”)

Thompson’s framing captures the forward-looking dimension of this. The economic asymmetry between the government and AI companies is only going to grow. The government’s economic leverage (carrots) is shrinking relative to the industry’s revenue and capital base. What remains are sticks: regulatory power, procurement threats, and national security designations. The worse the asymmetry gets, the more tempted the state is to use those sticks preemptively, before the companies are powerful enough to resist.

In a more stable political environment, this tension would play out through public debate and legislative action. Instead, it’s playing out through broadsides on X and Truth Social.

The Visibility Problem

Most people have no idea any of this is happening. Their exposure to AI is watching a video of babies doing crude standup on Instagram. The people watching AI move in real time are a small group: researchers, developers, and a small subset of AI-pilled knowledge workers paying significant money for access to frontier models.

This gap between an informed vanguard (”riding their models into personal singularities” to paraphrase technology writer Venkatesh Rao) and the general public itself is a democratic failure. Thompson’s assertion that politicians are held accountable for their decisions holds only if the general public has sufficient visibility and the mental models to do so. How many people know the capabilities of frontier models? To them, this whole dispute seems like a storm in an extremely nerdy teacup.

But the frontier models like Claude Opus keep getting better. And now, we are starting to see significant repercussions in the “real world” – from volatility in stock markets to job losses that are attributed to AI capabilities. Companies like Anthropic, OpenAI, Google, and Meta (along with hyperscalers such as Amazon and Microsoft) are locked in an escalating, expensive race to build more capable models and infrastructure. The rate of investment in AI now dwarfs ($36bn vs $700bn) that made in the Manhattan Project – even accounting for inflation.

So on one hand, we have the government and an AI company locked in a contractual dispute, and on the other, we have rapidly improving technology that could have a significant impact on the economy and on society.

The problem is that the people who get to decide whether they want this future don’t seem to have visibility or much of a say in this collision between a government that sees AI as both a weapon and a means of control and corporations that are gambling on an “AI or bust” future.

Heavy Lies the Crown

I agree with Thompson. Political accountability under uncertainty is what democracy is for. The crown should lie heavy on whoever wears it.

But who is wearing it?

The current administration isn’t integrating expertise. They rely on vibes, culture wars, and performative displays of dominance on social media to govern. Most of the electorate doesn’t have visibility to hold anyone accountable. And the guardrails – Congress, the judiciary, and the media seem unable to function at the speed that this technology and administration demands.

We are being asked to deal with the consequences of decisions made without the political and social infrastructure these times demand.

What would it take to build that infrastructure?

At minimum: a broad consensus-building effort between government and the technology industry, regulatory frameworks that address military AI procurement and civilian protections simultaneously, and international cooperation that acknowledges the global diffusion of these capabilities. This would be difficult under any circumstances. Given the “winner takes all” dynamics that dominate the AGI discourse, both among nations and among companies, I see very little sign of it happening.

And the cost of not building it is severe. Without institutional infrastructure, there are two default paths, and neither is democratic. The first is the trajectory the current dispute previews: the state asserts control through coercion rather than consensus, using national security designations and emergency powers to bring the technology to heel. Taken to its logical endpoint, that’s a future where frontier AI capabilities are captured by the state and deployed to entrench power, at home and abroad. The second is a future where the technology simply outruns all governance, where capabilities improve faster than any institution can adapt, and the question of who decides becomes moot because nobody decided anything. It just happened.

I hope for the best, but I am not optimistic.

Perhaps the next round of elections will bring some more measured approaches to AI regulations. Perhaps the “market” will decide that incinerating public goodwill in a rapid race to the singularity doesn’t make sense.

Perhaps we have already decided that the best course is to do nothing. To wait and hope for the best and enjoy our AI-generated entertainment while we can.

A Letter to 2025

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Dear Rushi,

This letter comes to you from January, 2026. I know 2024 was rough, and you are wondering about what comes next. I can tell you. Some of it will help. Some won’t. But you should know. 

On Parenting

Being a father will continue to be the hardest and the most rewarding project you take on. The highs will be high: the 5-year-old starting kindergarten, working on her reading, her delight in tales from the Indian Epics – again and again. The 3-year-old starting pre-k and developing an absolutely delightful (and maybe slightly unhinged?) sense of humor. 

The lows will involve too many sleepless nights, epic meltdowns, and becoming adept at catching puke with your bare hands so you don’t have to change the sheets for the fourth time that night. 

You will complain about how they insist on climbing into bed with you in the early hours. 

But, when they don’t, you will wake with a start and tiptoe into their room to make sure they’re okay.

There will be dancing. K-POP. So much K-POP that you will find yourself humming it while walking the dog or waiting to join a Zoom call. Sometimes you will find yourself watching YouTube videos analysing songs from K-POP Demon Hunter and wondering if they will make a sequel. I don’t know yet.

(Image: Petite gare by Isaac Levitan. Wikimedia Commons)

On Grief

Another year will pass with your father gone. The pain will sometimes recede to a background hum and sometimes it will come roaring back. 

Walking into your parents’ house will still be difficult. You’ll wait to hear him call out from his study to come look at something. Wait for the monosyllabic text asking if you’re coming for dinner that night.

Every time you look in the mirror you will see more of him reflected back. The tilt of the head. The slight pout. His impatience will show up when working with the 5-year-old on writing practice. His whimsy when making silly jokes with the 3-year-old.

The grief will settle like sediment at the bottom of a pond. Sometimes it will be disturbed: a photo that surfaces on your phone, a handwritten card found while tidying your office. 

Let it stir and let it settle. It’s a part of you now.

On Love

2025 marks fifteen years of marriage. Adventure, adversity, and now domesticity.

Things will be difficult. Having a 3 and 5-year-old makes time together reliant on the kindness and generosity of loved ones. You will make the most of those moments: a short weekend away for a wedding, a few days spent eating out and making inappropriate jokes, the regrettably few nights watching TV under the covers.

One night up in the hills of West Virginia. Looking at the fire. Drinking bourbon. Feeling like twenty-somethings again. It was only one night, but enough to remind you of what it was and what it will be. One day.

There will be arguments, clenched jaws, silent shrugs. There will be reconciliation, often commiseration over what your little tyrants have put you through. Work stress, travel, adjusting schedules, waving at each other’s planes as you cross paths at Dulles.

You will finish the year strong if tired. Your wife more beautiful than ever. You a little more haggard.

On Meaning

You will spend many evenings in a dark room waiting for your children to fall asleep. Answering for the fifth time that morning time will be in ten hours. Fighting the sentimental inner parent who wants to hold his daughter’s hand every night until she drifts off.

Those dark hours are also when you think and wonder.

You will spend hours thinking about whether you will have a job in the new year and what that job will look like. You will fret about your responsibilities to your peers, your teams, your clients. Wrestle with the importance of process versus the value of output.

You will use the same technology that threatens to upend everything. The foundation models. You’ll use them to build tools and bring ideas to life. 

Oscillating between dread, wonder, and the defiance of knowing that if you can think it, you can build it.

And underneath it all, the questions that don’t resolve: What does it mean to work when twenty years of hard-won knowledge can be summoned with a prompt? What world will your children inherit? Post-scarcity utopia or hyper-capitalist hellscape where all power and wealth accrues to capital and to compute?

You will wonder about your decision to raise your daughters in a country turning from kindness to cruelty, from embracing diversity to strident ethno-nationalism. You will wonder if they will embrace their Indian heritage or find it more expedient to lean into their ethnic ambivalence.

Those won’t be fun thoughts.

On Stillness

After all the hours spent working and reading and fretting, it will be the quiet moments that matter.

Long walks with your not-very-smart dog. Waiting for the coffee maker to stop bubbling before the first 6am call. Moments when the house is quiet, the mind is still and the body is calm.

Treat each moment like a piece of masonry, building a wall against the coming chaos.

Lean into those moments. Shelter against the storm.

You will need it.

Roadtrippin’

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Fifteen hours alone in a minivan will take your mind to strange places.
Last weekend, as I drove from the Gulf Shore back home, mine wandered from gas station hot dogs to the future of AI.

📎 It feels like we’re already in a “paperclip maximization” loop.
Each new model is just good enough to justify ongoing jaw-dropping investments in data, compute, and talent. Data center construction now seems to be propping up a flagging US economy. But the benefits of AI don’t yet show up in the numbers.
Is the point of AI simply… to build more AI?

🙏🏾 AI research has often been overtly religious undertones. Kurzweil imagined post-singularity AI as an omnipotent God — the Old Testament kind: awesome, inscrutable, alien.
But maybe we don’t get that.
With so many teams building frontier models, maybe we get something closer to the Hindu pantheon — a whole cast of deities, each with their own agendas. Some awe-inspiring. Others… a little kooky.

🎭 Calling a startup a “ChatGPT wrapper” used to be an insult.
Now I think we’ve all become AI wrappers — sometimes just the meat-interface for LLMs.

🐉 On vacation, my kids and I made up stories:
Pink glitter dragons.
Mean unicorns.
Friendly witches.
Fearsome fairies.
I tried asking ChatGPT for stories, but even the most expensive model couldn’t match my three-year-old’s chaotic creativity. That made me hopeful — because what are humans, if not storytellers?

💀 There’s probably a billion-dollar business in a “dead man’s switch” for chatbots.
An app that erases your entire chat history when you die.
Because I’d rather not be remembered as the guy who once asked ChatGPT why the minivan’s doors wouldn’t close.
(It was a switch. Of course it was.)

A most pleasant parking lot

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I have spent the last week in a small cabin that is slowly being devoured by carpenter bees. It has a small porch that looks out over big open space. Away to the right is a play area with a small zip line.

Kids zip back and forth at all times of the day. At night I see bright fluorescent ankle bracelets levitating across the fields to the sound of squeals of delight and the thunk of another journey completed.

This is my summer vacation. A week in a camp site in the Appalachian mountains a couple of hours west of Washington, DC.

Just beyond the zip line is the water park. Three pools, a couple of slides and lots of sunburnt campers clutching giant Stanley cups filled with ice. Between the zip line and the water park is a road where the site’s mascot – Yogi Bear, trapped in perpetual 1960s optimism, circles every couple of hours in a golf cart, waving at campers who may or may not care for classic American entertainment.

Camp sites are also not spared the excess of American suburbia. The most common sight is a comically large trailer hitched to a shiny pickup truck. I guess they call the trailers RVs here. The RVs always have impressive decals – “Marauder”, “Wayfarer”, and so on.

The RV sites have hook ups for water, power and sewage lines. Deploy the retractable sunshade, fire up the portable gas grill and the pop up tent and relax out in what is effectively a parking lot – with a view, if you paid enough to get a premium spot.

Americans love parking lots.

We are in a valley close to the famous Skyline drive. Around us are rolling hills covered in dense forest. The drive here involved some gloriously twisty roads and made me miss my motorcycles. We are content spending the days going to the pool, the playground and the odd adventure a little further out. Yesterday it was a small hike and then a visit to a brewery a couple of miles away.

When they have had enough of the pool, and when the relentless Virginia sun makes the playground a non-viable option, the girls demand stories. 

I tell them versions of Greek mythology – stories of Medusa, Centaurs, Cyclops and the Sirens. I try and intersperse these stories with those from Indian mythology. Krishna dancing on the snake, the avatar of Vishnu covering the earth and heaven in three steps. 

We also make up stories, of dragons and unicorns, and princesses and witches.

The five year old listens and asks questions and the three year old is mainly bemused. Though she wants to make sure that the dragon is pink. They demand spooky stories but are satisfied with ancient tales of somewhat dubious morality.

All these stories – ones I devoured as a kid now make me wonder. They are about creatures doing their own thing, guarding caves, singing, swimming in rivers – minding their own business before humans show up to disturb the peace and assert their dominance. 

Odysseus playing tricks on the Cyclops to steal his treasure, Krishna annoying the snake by dancing on its head and forcing it to leave the river.

The stories may be thousands of years old but they endure, just like human nature. Here we are – clearing out an ancient forest to build a glorified car park.

The girls couldn’t care less for moral ambivalence. They are enjoying themselves – turning a deep shade of almond as their Indian genes give them a helping hand with the Virginia summer. The dog is chasing lantern flies and I am here on the porch listening to the cicadas and tree frogs and the drone of the air conditioning unit behind me.

Agency and LLMs as Mentors

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Cate Hall, writing on Substack, suggested that a way to do hard things is to ask “What would someone 10x better do?” and then do it. Reading the post made me realize that agency – the belief that we can ‘just do it’ – is what holds most of us back from success.

I saw this play out again and again growing up in India in the 1980s. You were expected to do as you were told, to not question authority, to focus on getting credentials – degrees, membership of networks, family connections – to get ahead in life. I was lucky to have mentors who encouraged me to read and indulged my curiosity. But millions of kids weren’t so lucky. Who didn’t have anyone to talk to about books, art, movies, and… well… dinosaurs.

What’s forgotten in debates about AI replacing human creativity, taking jobs, or generating slop is that these models can act as expert mentors.

Don’t have anyone to help you with a presentation? Ask AI Simon Sinek to give you feedback. Thinking about how to live your life? Get AI Marcus Aurelius to talk to you about Stoicism. Never written a love letter? Maybe AI Jane Austen could help..

This democratization extends beyond learning into creation itself. Those who sneer at people creating Miyazaki-style images are practicing the same gatekeeping that once kept mentorship exclusive.

Yes, those profile images may seem trite, but they’re also gateways – introducing millions to the worlds of ‘My Neighbor Totoro’ and ‘Spirited Away.’ More importantly, they make it possible for anyone to create, explore, express…

People today have access to world-class mentors via AIs on their phones, regardless of whether they live in Boston or Baroda. These mentors may just be the catalyst to inspire a generation of young people to say, “I can do it.” To embrace agency and set themselves up to do impossible things.

Without gatekeepers, without credentials, without someone telling them what they can or cannot do.

PS – This image is an AI rendering of one of my favorite portraits of my daughter and our dog. Photography was also once looked down upon as “not really art”. Today, it remains one of my favorite hobbies and ways of creative expression.

Why I’m Riding The Distinguished Gentleman’s Ride in 2025

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On the 18th of May, I’ll be suiting up and riding my beloved Triumph Bonneville to Leesburg, VA, for the Distinguished Gentleman’s Ride (DGR). The DGR is a global movement to raise awareness and funds for men’s mental health and prostate cancer.

The DGR’s mission is close to my heart.

I’m in my mid-40s and, yes, fully leaning into the midlife-crisis stereotype of riding motorcycles. But the truth is, the last five years have been full of change — and extremely challenging at times. I moved countries, stepped into the most demanding role of my career, and became a father to two beautiful children. I also lost my father suddenly and faced both personal health challenges and the passing of other close family members in a short span of time.

Grief, stress, and the daily pressures of life and work have, at times, taken a real toll on my mental health. Through it all, rediscovering my love of motorcycles has been a gift.

For me, riding is more than just a love of beautiful machines and the joy of the open road. It’s therapy. It’s a sanctuary — a way to clear my head, feel grounded, and process everything life has thrown my way.

Mental health is a tough conversation for many men — especially those of my generation, and particularly those raised in cultures where talking about emotions just wasn’t something men were supposed to do. I never really had (or still have) the tools to talk openly about what it means to struggle, to age, or to ask for help when things feel overwhelming.

That’s why this ride matters. The Distinguished Gentleman’s Ride has partnered with Movember, a charity committed to changing the face of men’s health by bringing people together and creating space for these important conversations.

Support the Cause
If this resonates with you, I’d be grateful for your support. You can donate to my DGR campaign here. Every little bit helps.

Until then, ride safe.

And take care of yourself.

Obesity, Second-Order Consequences & ..Molds?

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I came across this series of posts on the root causes of Obesity by Slime Mold Time Mold, a delightfully weird pseudonym for the team behind the posts. The posts are long, well researched, and, despite the weighty content (haha), quite fun to read.

The blog series is not complete, and I am fascinated to see what comes next.

The writers start with the observation that we have seen a startling increase in obesity rates from 1980 to the present day as seen in the animation below from Our World in Data.

As of 1980, around 11% of the population of the Americas was obese. In 2016, this went up to a staggering 28%. What has caused this?

The team at Slime Mold Time Mold (SMTM) reviews research and data spanning the last 150 years. From studies done on the BMI of Civil War veterans to looking at rates of obesity in Macaques – they cover an incredibly wide range of material. Their thesis is that 1980 was an inflection point – after 1980 we see soaring rates of obesity across the industrialized world.

Strangely, this increase does not seem to be driven by a big change in the total number of calories consumed or in the way that we live our lives. Indeed, the diets in the early 20th century were more calorific and unhealthy than present-day diets.

Just think of all the big dinners on Downton Abbey.

Carbs, glorious carbs..

We eat fewer carbs today than we did a hundred years ago. While the research does show an increase in the total number of calories consumed, the data does not support the increase in obesity rates being driven by an increase in calorific consumption.

SMTM’s thesis is that environmental contamination is an important driver of the increase in obesity rates since 1980.

They look at a number of different culprits – Lithium, a group of chemicals called PFAs as well as the presence of antibiotics in livestock.

If indeed environmental contamination is a primary driver of the obesity crisis, we are looking at a public health scandal that will be bigger than smoking or lead in gasoline. The jury is still out, but SMTM’s work seems to point to a strong correlation.

It also makes me think, again, that we are very poor at thinking through the second-order consequences of our actions. I think we are tinkerers by nature and are biased to action. This has helped us make a lot of progress in a short time – but we are also continuously fixing (or abandoning) things that we have broken along the way.

Environmental contamination and obesity are just another of a variety of different examples of “progress” messing things up. Maybe the research will identify a smoking gun and the problem will be regulated away.

We are strikingly poor at figuring out how complex systems operate and how they may trigger feedback loops. Global warming, disinformation on social media, and many other modern ailments can be tracked to us not being able to think through the consequences of our actions.

So what is the solution? One way is a monastic retreat to the wilderness. But when even remote Alaska is contaminated with PFAS chemicals, retreat does not seem to be a viable option. Maybe we need to push regulatory bodies for stricter enforcement of laws and punitive measures for those who do not comply. But the revolving door between big government and big chem does not fill me with confidence that this is an avenue that holds much hope.

The one, teeny-tiny, ray of hope comes from us being able to deploy massive computational power to model and simulate the world a little better. Perhaps we could get better at figuring out how complex systems interact and that might help towards a more thoughtful and considered approach to change?

One can live in hope. Until then, I wait for my next SMTM fix.

Footnote

The work by SMTM is worth reading (and following) in full. It is a great example of just some “random people on the Internet” using open data to think through and attempt to answer difficult questions.

Reading SMTM reminded me of the early, pre-social media engagement-driven, Internet. I remember stumbling upon blogs such Naked Capitalism (Still going strong), The Epicurean Dealmaker (RIP), and Slate Star Codex (also RIP) and enjoying reading about topics that were outside my areas of work or of study.

The death of Google Reader and the rise of Social Media has made the Internet a much less surprising place.

Computer Says No

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Photo by Andre Hunter on Unsplash

Everyone has dealt with a “Computer Says No” situation. You call up a customer services agent hoping for a quick resolution to a perfectly reasonable query. But “computer says no”. You go through a convoluted questionnaire, answer the questions as best you can, but “computer says no”. The customer service agent shrugs saying that they can’t do anything because the system won’t let them.

It is tempting to call the customer service agent a jobsworth, or someone who doesn’t care about their job. To hang up in frustration and curse the status of the customer service industry.

So why is modern customer service so bad? Why do we dread calling up a help line or deal with an online customer support agent (who may or may not be a bot)?

Atul Gawande’s recent New Yorker article on why medical professionals hate their computers may have an answer. Dr. Gawande is a surgeon. In the article, he talks about how the medical informations systems used in his hospital make it difficult to really look after his patients.

Hospital systems in the US (much like the NHS in the UK) have spent billions of dollars to make medical care more efficient. But when we go visit our local general practitioner, we find them struggling with their PC more than talking to us. Instead of empathy, we get distracted clicks and frowns while the doctor tries to figure out how to massage the conversation into a bunch of drop downs and radio buttons. It is a terrible experience for everyone involved.

I am an engineer, I like efficiency. I like measuring the performance of the code I write and love reading articles about how to optimise software. In this pursuit of perfection, I fear that we have optimised ourself into a corner. Our systems are optimised, everything is measured — except the misery that they inflict on those who actually have to use the system every day. Human conversations and problems cannot be modelled so easily into a workflow. Improved throughput and the need to be more efficient drive design decisions more than the need to solve a problem. So we end up systems that their users hate. These systems and workflows lead to dis-engaged employees and ultimately to terrible customer service.

The next time that someone talks about having poor customer service, don’t blame the agent. Blame the analyst who designed the convoluted workflow in the software they use and the engineer who implemented it.