And Vijay Says…

It’s not a post I thought I’d be writing in 2026—but this week was one of those weeks where I felt a short refresher might be useful for everyone’s sanity.

Core Principle: Respect the time and cognitive load of the recipient.

When Not to Use Email

 When you are angry or sad: Don’t even risk typing a draft. Just close the app.

 When you need a nuanced or complex discussion: Pick up the phone or hop on a call. Talk it through.

 When it’s a quick, casual check-in: If it can be handled in a single sentence, move it to a messaging platform.

What a Useful Email Actually Looks Like

 A clear subject line: Make it easy to search for and crystal clear on intent (e.g., Action Required, FYI, URGENT). If you need my sign-off, a subject line like ⁠”Need approval for travel to London client meeting”⁠ will get my attention 10x faster than ⁠”Quick question.”⁠

 BLUF (Bottom Line Up Front): Always. Don’t make someone read a three-paragraph thesis before they figure out why you’re writing to them. State the point immediately, offer the explanation context below it, and invite them to chat if they have questions.

 Bold the key takeaways: Let’s face it, very few people read every word of an email. If your message is longer than a couple of sentences, use bold text to guide the reader’s eye to the most critical information.

 Be explicit with the “Ask”: Don’t make the reader guess what their homework is. If you need a response by a certain deadline, say it plainly: ⁠”Need your review of the attached deck by 3 PM ET on 3/7/26.”⁠

 Less is always more: Most people read emails on their phones between meetings. Two paragraphs are usually plenty. Don’t loop in random people just for “visibility,” and please, use Reply All sparingly.

Hot tip: Write the email body before you add the recipients. It is the single easiest way to prevent accidental half-written sends and catastrophic mistakes you will immediately regret.

Three of the most consequential IPO filings in stock market history just happened within two weeks of each other. SpaceX filed its public S-1 on May 20. OpenAI filed confidentially around May 22, targeting a debut above $1 trillion. Anthropic filed its own S-1 on June 1 at a private valuation approaching $1 trillion, with some estimates putting its IPO target as high as $1.75 trillion. If realized, it would be the largest public listing ever attempted.  

The coverage has focused almost entirely on valuation, competition, and whether the hype is justified. Those are reasonable questions. But they are not the most important one. The most important question is not whether these companies succeed. It is what claim the rest of us have on that success, given that the foundations they were built on were not entirely private.

I have pushed back publicly on the doom framing around AI and employment, and I will say it again here. AI is extraordinarily good at specific, well-defined tasks. It is not good at navigating enterprise complexity, managing client relationships under pressure, or making judgment calls in ambiguous situations. Enterprise inertia alone : the sheer difficulty of changing how large organizations actually operate, will keep humans central to most workflows for longer than the headlines suggest. The historical pattern of technology creating more work than it destroys is not broken yet.

But here is what that reassurance misses: You do not need mass unemployment for something to go badly wrong. You only need the normal operation of capital markets.

The Problem the IPO Filings Won’t Mention

By most estimates, NVIDIA’s market cap grew by somewhere in the range of $2 trillion between early 2023 and the end of 2025. Median wages in knowledge work barely moved over the same period. That gap tells the real story. AI is making a small number of companies enormously productive, and the gains are flowing almost entirely to whoever owns the capita : not to the workers, communities, or governments whose infrastructure, data, and publicly funded research made those gains possible.

Consider what AI actually stands on. The transformer architecture underlying most modern AI came out of publicly funded academic research. The internet it runs on was a government project. The data it trained on was generated by billions of ordinary people going about their lives. The power grids it consumes were built with public money and are regulated as public utilities.

This is not an argument for arbitrary wealth redistribution. It is an argument for a return on public investment. The value AI generates from these massive public inputs currently compounds almost entirely in private hands.

Our tools for capturing that value were not built for this. Corporate tax works when companies have local payrolls and physical assets that are hard to move. Large tech companies have spent two decades perfecting the art of moving intellectual property to low-tax jurisdictions. Income tax works when wages grow with productivity. If AI holds wages flat while productivity rises, the tax base stagnates at exactly the moment demand for public services goes up.

The jobs debate gets all the oxygen in the room. The ownership question barely gets asked. That asymmetry is where the real policy failure lives.

What the US Can Actually Do

America built the AI industry. That is a genuine advantage. While government equity stakes in private companies are politically impossible in the US context, the right model already exists closer to home. Since 1982, Alaska has taxed oil companies for using public land and paid every resident an annual dividend. The parallel to AI is direct.

The right mechanism to capture this is a federal levy on AI compute revenue billed within the US. It must target top-line revenue, not profits, which can be re-routed through standard transfer pricing to any low-tax jurisdiction overnight.

A state-by-state approach invites a dangerous race to the bottom, where cloud providers route their next large scale data center expansions to whichever state blinks first. To prevent internal capital flight, the mechanism must be federal, structured as a sovereign wealth contribution to fund a national AI dividend over time.

Predictably, Silicon Valley will argue that taxing compute amounts to unilateral economic disarmament in a critical national security race against state-subsidized adversaries like China. But this presents a false choice. A top-line compute levy does not choke innovation : it funds the very public infrastructure, grid capacity, and research grants required to sustain a long-term technology race. True national security cannot coexist with hollowed-out domestic tax bases.

What India Should Be Doing Differently

I work in this industry, so let me say this plainly: The apocalyptic predictions for India’s technology and services sector, whole industries gone in five years, do not survive scrutiny. Enterprise buying models change slowly. Compliance requirements and hallucination risks keep humans firmly in the loop. Client relationships built over decades carry switching costs that no AI agent can instantly replicate.

But more runway is not the same as no urgency. The FTE-based headcount model is under genuine structural pressure. Companies that treat current stability as permanent will find themselves in a much harder position than those moving now. The direction is clear: from executing processes to redesigning them with AI, from headcount-based billing to outcome-based delivery. It is harder to run, but far more defensible.

On the policy side, India holds an asset the US would struggle to replicate. Aadhaar, UPI, and the Direct Benefit Transfer (DBT) stack form the world’s most capable digital distribution infrastructure. No other country could run a large-scale basic income pilot for workers navigating sectoral transition as cheaply or as quickly. The infrastructure exists. What is missing is the political will to treat this as a serious economic experiment rather than a welfare line item.

India is also leaving its primary negotiating leverage on the table. Global AI models actively seek high-velocity, diverse behavioral and economic data to train localized agents, and currently receive it from Indian citizens entirely for free.

India must stop approaching these negotiations with a 20th-century “manufacturing plant” mindset, content with mere construction jobs and local real estate investment. The goal should be aggressive data sovereignty and revenue-sharing arrangements. If global AI companies want to build data centers here and integrate with our digital public infrastructure, India should be negotiating for sovereign equity stakes, not just tax receipts.

The Honest Summary

The job destruction story is overblown. The wealth concentration story is underreported. Conflating them produces the wrong policy conversation.

Preparing for AI does not mean panicking about unemployment. It means building the ownership structures and revenue-sharing mechanisms before the value fully concentrates, because redistributing wealth after it has concentrated is an order of magnitude harder than claiming a fair share of it upfront.

The wealth from AI is real and it is compounding now. The question is whether we build the pipes to share it before the ownership patterns harden into something permanent, or spend the next decade having an argument about jobs while the more consequential decisions get made without us.

The enterprise AI landscape is hitting a quiet but definitive turning point. Over the last two years, organizations rushed to move their generative AI proofs-of-concept into production, driven by the sheer awe of what frontier LLMs could accomplish. We built multi-agent frameworks, dense RAG pipelines, and autonomous workflows capable of orchestrating complex enterprise tasks.

But as these systems scaled to production instances, a cold, hard reality set in. The issue isn’t that the models aren’t smart enough, it’s that they are incredibly expensive to feed!

Enterprise technology leaders are waking up to a profound realization: building context-aware, deterministic applications with non-deterministic models is an economic battlefield. The era of “token-maxing” – throwing boundless token budgets and massive test-time compute loops at every problem is hitting a financial and operational wall. Winning the next phase of enterprise AI requires an aggressive shift toward Architectural Minimalism.

So, how did we get here?

In the race for absolute accuracy, frontier model labs introduced a paradigm shift: test-time compute. Instead of generating a knee-jerk next token, modern reasoning models use internal monologues, multi-turn self-correction loops, and extensive chain-of-thought processing before outputting a final answer.

This is “token-maxing” in its purest form. For complex coding, scientific discovery, or deep strategic evaluations, this approach is revolutionary.

But when applied carelessly to enterprise workflows, it creates what can only be described as structural bloat !

Your “simple” question might only be 50 cheap input tokens – and your answer might be 100 more expensive tokens. What you don’t see is the part where an additional expensive 1000 tokens were the AI talked to itself. Think about the overhead of this cost across millions of transactions – you changed a 2 cent efficient transaction to a 50 cents unit economics liability !

In multi-agent architectures, teams frequently pass the entire chat and execution history back and forth between specialized agents to maintain context. If Agent A, Agent B, and Agent C all receive the full payload at every turn, the input tokens grow quadratically, not linearly. You quickly end up paying a massive “historical baggage tax” on a turn that only required a simple validation.

High token costs rarely stem from rank incompetence. Instead, they happen because teams are trying to force non-deterministic models to behave reliably within rigid enterprise constraints. Without mature guardrails, models naturally wander, hallucinate, or demand massive context injections to maintain accuracy.

High token spend is a sign of an architectural mismatch. It happens when a team treats a top-tier, frontier LLM like a universal database, a basic keyword router, and a heavy-duty processor all at the same time. Using a frontier model to parse a date string or extract an account number is the enterprise equivalent of using a Ferrari to haul gravel. It works, but the cost per mile will ruin you.

So, what does Architectural minimalism mean in this narrow context?

It is about answering this one question : what is the absolute minimum compute required to execute this step with 99.9% accuracy?

Transitioning to a minimalist architecture requires decoupling your systems into a tiered, intent-driven framework.

1. Have a “cheap” gate keeper : Route the incoming questions to the appropriate component to answer. “What is my account balance” doesn’t need even an LLM – it can be answered by an API call or a DB lookup. Only route complex reasoning tasks to frontier models. Another elegant solution that is often missed is semantic caching – where a recently answered similar question can help reduce the cost of answering the new question to nearly zero.
2. Surgical context management : Don’t let your RAG system feed multiple PDF pages when 5 lines of well crafted sentences will do the job. Another underutilized hack is prompt caching – you can save 80% plus costs while also returning results faster – which helps UX. Why only please the CFO when you can also keep your users happy with under two second responses?
3. State Truncation in Multi-Agent Loops: Stop passing the entire historical baggage of a conversation. Instead, compress past agent actions into concise, structured metadata packets so that agents only receive the immediate payload required for their specific micro-task.

The winning architectures of the coming years will not be the ones that burn the most tokens; they will be the ones that exhibit the highest intelligence efficiency. By embracing architectural minimalism, optimizing context, and deploying specialized, tiered models, the enterprise can finally bridge the chasm between raw technical capability and genuine, sustainable business value.