Rishi Kulkarni

His Majesty The King addresses a Joint Session of the United States Congress

Five months in, I think I've decided that I don't want to vibecode — I want professionally managed software companies to use AI coding assistance to make more/better/cheaper software products that they sell to me for money.

In 2005, India couldn’t meet 12.3% of its own peak demand. By 2007, the shortfall had widened to nearly 16.6%, and close to 18,000 megawatts were unavailable. The early 2000s were years of genuine electricity poverty. Factories ran on diesel backup generators as a matter of routine. Homes in smaller cities and villages received power for a few hours a day. Distribution, which is the final link between the grid and the household, was historically the most neglected and most corrupt part of the chain. Electricity theft was widespread, billing was unreliable, and state electricity boards were financially broken. Reforms here were uneven and politically difficult, but schemes like UDAY, launched in 2015, restructured the debt of state distribution companies and pushed them toward financial viability. The Saubhagya scheme, from 2017, connected the last unelectrified households, around 25 million of them, to the grid by 2019. India’s solar capacity in 2010 was negligible. Today, it is measured in hundreds of gigawatts. The price of solar panels fell globally by over 90% across this period, and India made a strategic bet to capture that cost decline at scale. Rooftop solar programmes brought electricity generation to homes, factories, and commercial buildings. And the International Solar Alliance, co-founded by India in 2015, helped build global momentum. The timing proved critical. India’s peak electricity demand now falls in the afternoon, driven by air conditioning in an increasingly hot country. Solar generates hardest in exactly those hours. On April 25, around 12:30 pm, solar plants and rooftop systems together supplied roughly one-third of all electricity being generated at that moment. Across the full day, solar’s share was around 22%. India today draws 52% of its electricity from non-fossil sources. More than half of every unit generated comes from sun, water, wind, or nuclear. The deficit percentage, which once sat stubbornly above 10%, has now collapsed. Since 2024, it has been effectively zero. Reliable electricity means a small business owner does not budget for a diesel generator as a fixed cost. It means an electric vehicle is practical for someone who cannot afford to be stranded. It means a student in a rural home can study at night without planning around power cuts. It means a hospital runs its equipment on the assumption that the supply will hold. Electricity reliability is, in the end, a quiet form of equity. When the grid is unreliable, those with money buy backup. Those without simply go without. India's closing of its power deficit means that the gap no longer falls along economic lines. The country that once rationed darkness now delivers light on demand, at the moment of highest need, to everyone connected to the grid. That took two decades and thousands of infrastructure decisions. It is not the kind of achievement that fits in a headline. But on an April afternoon, when 256 gigawatts flowed, and nothing broke, it showed.

🚨 BREAKING: Stanford's 423-page AI Index Report 2026 is out! [Bookmark it below]. These are its key takeaways: 1. AI capability is not plateauing. It is accelerating and reaching more people than ever. 2. The U.S.-China AI model performance gap has effectively closed. 3. The U.S. hosts the most AI data centers, with the majority of its chips fabricated by one Taiwanese foundry. 4. AI models can win a gold medal at the International Mathematical Olympiad but cannot reliably tell time, an example of what researchers call the jagged frontier of AI. 5. Robots still fail at most household tasks, even as they excel in controlled environments. 6. Responsible AI is not keeping pace with AI capability, with safety benchmarks lagging and incidents rising sharply. 7. The U.S. leads in AI investment, but its ability to attract global talent is declining. 8. AI adoption is spreading at historic speed, and consumers are deriving substantial value from tools they often access for free. 9. Productivity gains from AI are appearing in many of the same fields where entry-level employment is starting to decline. 10. AI’s environmental footprint is expanding alongside its capabilities. 11. AI models for science can outperform human scientists, though bigger models do not always perform better. 12. AI is transforming clinical care, but rigorous evidence remains limited. 13. Formal education is lagging behind AI, but people are learning AI skills at every stage of life. 14. AI sovereignty is becoming a defining feature of national policy, but capabilities remain uneven, even as open-source development helps to redistribute who participates. 15. AI experts and the public have very different perspectives on the technology’s future, and global trust in institutions to manage AI is fragmented. - 👉 Download the full document below. 👉 To learn more about AI's legal and ethical challenges, join my newsletter's 93,500+ subscribers (link below).

JUST IN: Use of AI in the office is reportedly creating a flood of “workslop” that takes longer to fix than do from scratch.

Every time I see a tweet saying “I can vibe code this in a weekend” - I think of the slack notification system.. It takes time, persistence and effort to get the details right. Sure, a lot of simple workflows will get vibe coded away. And maybe you can put this in Claude Code and get the code right in one shot. But quality, depth and great systems will still have value and take time. You can’t vibe code lessons. Now and forever.

The more enterprises I talk to about AI agent transformation, the more it’s clear that there is going to be a new type of role in most enterprises going forward. The job is to be the agent deployer and manager in teams. Here’s the rough JD: This person will need to figure out what are the highest leverage set of workflows on a team are (either existing or new ones) where agents can actually drive significantly more value for the team and company. In general, it’s going to be in areas where if you threw compute (in the form of agents) at a task you could either execute it 100X faster or do it 100X more times than before. Examples would be processing orders of magnitude more leads to hand them off to reps with extra customer signal, automating a contracting review and intake process, streamlining a client onboarding process to reduce as many straps as possible, setting up knowledge bases than the whole company taps into, and so on. This person’s job is to figure out what the future state workflow needs to look like to drive this new form of automation, and how to connect up the various existing or new systems in such a way that this can be fulfilled. The gnarly part of the work is mapping structured and unstructured data flows, figuring out the ideal workflow, getting the agent the context it needs to do the work properly, figuring out where the human interfaces with the agent and at what steps, manages evals and reviews after any major model or data change, and runs and manages the agents on an ongoing basis tracking KPIs, and so on. The person must be good at mapping the process and understanding where the value could be unlocked and be relatively technical, and has full autonomy to connect up business systems and drive automation. This means they’re comfortable with skills, MCP, CLIs, and so on, and the company believes it’s safe for them to do so. But also great operationally and at business. It may be an existing person repositioned, or a totally net new person in the company. There will likely need to be one or more of these people on every team, so it’s not a centralized role per se. It may rile up into IT or an AI team, or live in the function and just have checkpoints with a central function. This would also be a fantastic job for next gen hires who are leaning into AI, and are technical, to be able to go into. And for anyone concerned about engineers in the future, this will be an obvious area for these skills as well.

Early versions of Mythos Preview often exhibited overeager and/or destructive actions—the model bulldozing through obstacles to complete a task in a way the user wouldn't want. We looked at what was going on inside the model during particularly concerning examples. (3/14)