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Hemant Mohapatra

@MohapatraHemant

Jan 31

I write this as an optimist. Over the last 20+ years since the Indian economy opened up, we’ve been closing the gap between us and the western nations. That gap is widening once again, thanks to AI. We are at a point in the global innovation cycle that it is clear to me what has brought India so far won’t take us much further. We need to rethink our approach to entrepreneurship and investing if we are to compete. Below is a short diagnosis, and a strawman proposal ⬇️

1. Innovation cycles, like the one we are entering, favor larger, developed economies over emerging ones. The incumbent vs. newcomer dynamic in startups applies to nations too, especially when countries move at startup speed. China and the US are.

2. These cycles initially favor CAPEX-heavy models. Consider the infrastructure required for oil and gas in the 1920s, railroads in the 1800s, or semiconductors and space exploration in the late 20th century.

The Jiofication of India over the last decade exemplifies this. As the underlying resource / innovation (oil, cpu, gpu, network, AI-token) becomes commodity, price becomes the primary differentiator. Those with the endurance to withstand aggressive price competition win, attracting more capital and scaling further.

Countries with robust capital markets, policies, supply chains, and talent pipelines hold the advantage and often end up “owning” the bottom layer of innovation which they can seek rent on as the cycle plays out often for 30-50 years.

x.com/MohapatraHeman…

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Hemant Mohapatra

@MohapatraHemant

Nov 25, 2024

One of the things we hear often about AI is how it’ll bring a productivity surge to society and have a deflationary impact on GDP. Technical revolutions, over a long enough horizon, do tend to make societies better off economically but we wanted to go deeper into what is the nature of productivity today, where does it come from, what do the productivity curves for large tech & non-tech categories look like, and then understand exactly where, and how will AI have the most impact on these productivity curves. @AbhiramTarimala and I dug into some of these core concepts in today's piece and a lot of python code & charting later, we present to you "AI - the last employee, How bigtech AI CAPEX spending will reshape future corporate cost structures" - link at the end of the 🧵:

Productivity = Automation + Specialization. Nations grow GDP/capita by automating (do more with the same people) or specializing (create higher-margin products). AI supercharges both. Taiwan going from an agrarian economy with a GDP/capita of ~150$ in the 1950s to a semiconductor powerhouse with a GDP/capita of $7000 by the 1980s is an example. Also worth noting that without automation, it’s hard to free up enough resources to move up the specialization curve. We’ll see later in the article that the same logic would apply to businesses — improving specialization & automation is directly related to productivity.

Our starting hypothesis was that as most orgs scale, fixed costs (FC) go up, and marginal productivity (MP) goes down. We want to be on the "genius savant" lines on FC and MP curves; instead most cos are the orange lines. A genius savant company will scale infinitely with very few people, and will maintain high productivity forever.

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Hemant Mohapatra

@MohapatraHemant

Jul 13, 2024

~12yrs ago, I got a job @Google. Those were still early days of cloud. I joined GCP @<150M ARR & left @~4B (excld GSuite). Learned from some of the smartest ppl in tech. But we also got a LOT wrong that took yrs to fix. Much of it now public, but here’s my ring-side view👇

By 2008, Google had everything going for it w.r.t. Cloud and we should’ve been the market leaders, but we were either too early to market or too late. What did we do wrong? (1) bad timing (2) worse productization & (3) worst GTM.

We were 1st to “containers” (lxc) & container management (Borg) - since '03/04. But Docker took LXC, added cluster management, & launched 1st. Mesosphere launched DCOS. A lot of chairs were thrown around re: google losing this early battle, though K8 kinda won the war, eventually 👏

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Hemant Mohapatra

@MohapatraHemant

Jul 5, 2024

So now that Nvidia has far outstripped the market cap of AMD and Intel, I thought this would be a fun story to tell. I spent 6+yrs @ AMD engg in mid to late 2000s helping design the CPU/APU/GPUs that we see today. Back then it was unimaginable for AMD to beat Intel in market-cap (we did in 2020!) and for Nvidia to beat both! In fact, AMD almost bought Nvidia but Jensen wasn’t ready to sell unless he replace Hector Ruiz of AMD as the CEO of the joint company. The world would have looked very different had that happened. Here’s the inside scoop of how & why AMD saw the GPU oppty, lost it, and then won it back in the backdrop of Nvidia’s far more insane trajectory, & lessons I still carry from those heady days:

After my MS, I had an offer from Intel & AMD. I chose AMD at 20% lower pay. Growing up in India, AMD was always the hacker’s choice - they allowed overclocking, were cheaper, noisier, grungier and somehow just felt like the underdog david to back against the Intel goliath!

Through the 90s, AMD was nipping @ Intel’s heels but ~2003 we were 1st to mkt w/ a 64-bit chip &, for the FIRST time, had a far superior core architecture. Oh boy, those were exciting times! Outside of SV, I haven’t seen a place where hardcore engg was so revered. Maybe NASA.

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Hemant Mohapatra

@MohapatraHemant

Jul 2, 2024

Speed of execution is the moat inside which live all other moats. Speed is your best strategy. Speed is your strongest weapon. Speed has THE highest correlation to mammoth outcomes. Those who conflate speed w/ 'thoughtlessness' haven't seen world class execution @ speed. E.g.:

Many confuse speed w/ impatience. Impatience is your boss pinging you @ 9pm then calling @ 6am to check if a task is done. Speed is strategic. It is a permeated sense of urgency built w/ a shared belief that what you are doing is important & if you don’t do it, someone else will.

AMZN defines speed. Their 2015 SEC filing () is a must-read: (1) deliberate irreversible decisions (~10%?) (2) expedite all else. Founding teams need to learn how to apply judgment w/ <70% of data (<50% for early stage cos). Move fast, “disagree & commit”. shorturl.at/xDEU1

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Hemant Mohapatra

@MohapatraHemant

Jun 8, 2024

For those of you following anything SaaS, you'll note there have been a lot of calls around 'saas is dead' lately. If you are wondering why, I wrote about this last yr here👇. This isn't just about saas but rather a 50yr macro CAPEX/OPEX cycle at play under the hood. : shorturl.at/sJ4IZ

Over the last 20yrs, both the cost of building and distributing software has COMPLETELY crashed. 20 years ago, it’d take a 4yr CS degree to write software, today thanks to internet anyone who wants to work hard can learn to code. In 20yrs world has gone from 5-6M software developers to 60M+ today. Second, the cost of distribution has gone to zero thanks to SaaS. I remember the days MSFT used to ship us a new MSDN CD monthly; imagine if you had to burn 60M CDs monthly as MSFT today? Software is shipped hourly, globally, all at once to everyone now.

Result? A Cambrian explosion in SaaS Globally. Every revenue pool is fragmented across lots of players now. This is also why even at $200-300M ARR scale, network effects of brand/WOM are becoming harder to see -- there are very few to none of "no one gets fired for buying IBM" type businesses today. Public markets can't foresee a 300M ARR business going to 1B ARR as easily as they would in the past. Ergo, multiples compression across the board.

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