Hemant Mohapatra Profile picture
Mar 4, 2019 11 tweets 3 min read Read on X
So @lyft is paying $8m/mo to @AWS -- almost $100m/yr! Each ride costs $.14 in AWS rent. I keep hearing they could build their own DC & save. My early days at @Google cloud, heard the same from customers: "at scale, owning is cheaper". It wasn't - they all came around. Here's why:
Construction of a mid-sized Enterprise DC (just 5000sqft), at just "tier3" availability (3 9s) will cost around 40m. If you want 5 9s redundancy you will need 1-2 failovers, so 3x that. Incld racks, cooling, power, construction and land. Using a colo @Equinix will likely save 20%
But your DC costs will amortize over 10 years, correct? Yes. But there's more. Construction will take 12-24mos. For that time, company loses focus, hires non-core engg, vendors, and planners that understand bldg codes, fire safety, env rules, security, maintenance etc.
Then for 10 years you have: ongoing support, maintenance & repair, costs of power, heating/cooling, and biometric security of physical assets. Power bills alone run in xxMs that's why Google DCs are so remote and near Geo/hydro/solar power sources.
Moreover, you need to build for 10yrs out, not today, so you'll likely either keep building more and more, or overbuild capacity by 50-100%. Your initial estimate of 40m (x3) is now 60-80m (x3).
Next comes some of the most expensive stuff: fiber! Without gigabit connectivity you are toast. Building your own undersea cables will cost 100s of Ms, so you'll be beholden to buying dark fiber from tier1/2 telcos and pay exorbitant rates for intercontinental traffic.
Next comes the outages: no matter what you do, you'll never have hot-swappable everything managed by ultra fast robotic arms that replace hard drives in seconds. Your hw will fail at rack/server level at 2-3x the avg cloud. Massive costs to missing SLAs to the biz that you bear.
Finally comes certifications for PCI, for HIPAA, for Gov, bla bla bla. You'll coordinate with the consultants day and out and it'll take anywhere between 3-12 months to get most of your infra certified to run your biz the right way.
I ran a "TCO" analysis at Google to convince a large customer why GCP was better. The numbers were clear, but customer wasn't convinced. Went to Verizon cloud, which shut down, then HP cloud, which also got shut down. Went on-prem. Then came to us. Zynga story is well known too.
Few rare examples of this working in parts. Netflix & Dropbox. When companies reach "internet scale" & have to do a lot of customization on the stack, running own infra may make sense IFF you have the GMs to cover 2yrs CAPEX & plan upfront. Both firms still use the cloud a lot.
E.g. if you are +30% of the internet traffic (nflx) it doesn't make sense to pay rent to telcos any more and feed their margins. You have the volume and stable demand to justify ownership. For the rest, cloud is where they'll live and die.

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More from @MohapatraHemant

Nov 25
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.Image
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.Image
Read 10 tweets
Jul 13
~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 👏
Read 14 tweets
Jul 5
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:Image
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.
Read 18 tweets
Jul 2
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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Read 10 tweets
Jun 8
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/sJ4IZhttps://medium.com/@MohapatraHemant/capex-opex-supercycles-the-dusk-of-saas-and-the-dawn-of-ai-saas-8aa5cfe74c93
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.Image
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.
Read 9 tweets
Feb 10, 2023
This is for SaaS & esp India-SaaS but applies more broadly as well. I’ve been investing in India-SaaS since '18 & harping abt this endlessly to anyone who’d listen. I’ll say it again: the age of “business model innovation” in SaaS is over. It’s done. What does this mean? 🧵1/20
If you don’t have a fundamental product or tech innovation at the heart of your company, you are looking at a 10yr slog ending in a $5-10M ARR plateau → a [10-20]% CAGR lifestyle biz; a sub-$50M M&A by the category leader; or a slow death to $0. What changed, you may ask? 2/20
10yrs ago, “GTM as product” still had a shot. Building a basic SaaS product was *hard*. Cloud was in relative infancy. At GCP, the 1st thing customers would ask us was ‘why should we entrust our mission critical software, infra, data to a 3rd party?” Today we laugh @ this Q. 3/20
Read 20 tweets

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