Everything Sharad Sanghi said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Sanghi: Hyperscalers are cookie-cutter and cannot customize solutions
“Hyperscalers have by the, Term hyperscale itself. They have huge scale. They are cookie cutter. They can't, they don't have, they can't customize solutions”
Sanghi: CoreWeave operates with close to 70% gross margins in the US
“So the leader in the space core weave in the U S has close to 70% gross margins.”
Sanghi: India is behind on foundation models but will catch up soon
“I think when it comes to foundational models, maybe we are a little bit behind, but I think we'll catch up soon.”
Sanghi: 2025 will see a major shift toward enterprise AI production workloads
“People are right now still doing experiments and very little is in production, but I think this year will be a shift where we'll see more production AI workloads. I think last year there was you know, more of experiments and less production, but I think this y…”
Sanghi: AI data center rack power density will soon reach 130 kW
“I remember when we started net magic, our average density of rack was like six kilowatts per rack. Then it went to 10 kilowatts per hour, then went to 22 kilowatts per hour. Now it's at 40 kilowatts per hour and very soon it will go to 130 kilowatts per hour.”
Sanghi: Data center total revenue is expected to quadruple in a few years
“Data centers are already growing at very high rates, you know, anywhere from 25 to 30% as per industry standards. They're expecting the data center Total turnover or total revenue of the data centers to, you know, literally quadruple in a few years, right?”
Sanghi: OpenAI retains moats over DeepSeek in benchmarks and bias reduction
“In some benchmarks deep, deep seek is equal or better. And, but in a lot of benchmarks, open AI is still better because open AI also gives you this They've got the team of people that, you know, work towards reducing biases, et cetera. So they've done a lot of…”
Sanghi: Nearly all enterprises want or use AI in production but lack know-how
“Almost all enterprises today, whether it's just AI native startup or an enterprise, everybody either is already using AI in production or wants to use AI in production, but a lot of them don't know what, how to go about it, right?”
Sanghi: Selling standalone software in India is difficult
“The software sale in India is not so easy.”
Sanghi: Neysa raised $50M and spent over $42M on infrastructure
“So we've raised fifty million. We've pumped in more than forty two million in our cloud infrastructure.”
Sanghi: AI cloud gross margins in India will likely be 40% to 50%
“I think in India it'll be good because more price conscious and more competitive. I think the gross margin will be lower, probably be in the range of, Somewhere in the range of 40 to 50%.”
Sanghi: AI GPU infrastructure is an order of magnitude more complex than traditional cloud
“I can tell you that the level of complexity with GPUs and with AI infrastructure, given how latency sensitive and how you know, the number of parameters that you have to worry about is much, much an order of magnitude more complex.”
Sanghi: Neysa selects only 1 in 10 candidates due to depth gaps
“At least when we interview candidates for every 10 candidates to interview, we're only able to select about one because, you know, a lot of the talent that we see doesn't have the depth that is required to scale.”
Sanghi: At least 50 Silicon Valley startups focus solely on AI security
“In the Valley. There are like at least 50 startups that are just focusing on AI security.”
Sanghi: Domain-specific vertical AI agents will outperform general AI applications
“I think you'll see more and more people focusing on a particular domain and building more and more agents for that domain, and that, that is something that I think will do well in the next few years.”
Sanghi: Over $2B invested in Netmagic post-NTT acquisition
“So in net magic, we've invested more than two billion dollars after entities acquisition a few billion dollars have been invested in there. Almost four, five hundred million a year is what we're investing in net magic now.”
Sanghi: Nvidia H100 lead times are 4-6 weeks, Blackwell takes 6 months
“You want to each 100 is four to six weeks. It takes time. It used to take six months. Now it takes four to six weeks. Now, of course the new black belt series takes six months”
Sanghi: AI workloads require immediate capacity, unlike traditional data centers
“This business, the way this works is when somebody needs an AI workload, they need it immediately. It's not that they're going to wait for you for six weeks for you to set up something and then they'll come to you. In data centers, at least customers give you …”
Sanghi: AI capital investment is much larger than dot-com boom
“I wouldn't know this hundred X, but I think it is much larger, but you would say investments. If you look at in terms of pure investment is much larger. I mean, you look at it the way Nvidia stock has gone up. Right. So it's from pure investment perspective is…”
Sanghi: India's Angel Tax Was a Disaster Until Government Fixed It
“There was a, I think angel tax was a disaster, but fortunately the government realized it and fixed it.”
Sanghi: Neysa has 65 employees and 1,200 deployed GPUs
“Scale in terms of number of employees, 65 in terms of GPUs. We've already deployed 1200 GPUs.”
Sanghi: Neysa has about 15 enterprise clients plus government POCs
“We have about 15 enterprise clients. We also have some research institutes. We also have some AI native startups, and we also have we are doing some POCs with some government institutions as well.”
Sanghi: NTT Netmagic operates 350MW across 4.5M sq ft, exceeding $500M
“Now in entity, we have currently close to 20 data centers. Each of the data centers is approximately 300,000 square foot. Between 200 to 300,000 square foot. And you know, on an average about 30 megawatts per data center. So we have around three 50 megawatts i…”
Sanghi: Top Indian private banks employ 300 to 400 data and ML engineers
“So some of the leading banks of India, private banks have got three, 400 data science and machine learning engineers.”