The story might have legs:
1. There is multi phase expansion in place which would need time to get audited for more CDMO work
2. Existing CDMO customer should do sustained business. A global pharma innovator does not spend months auditing a factory, transferring proprietary process chemistry, and validating batches just to buy for four quarters and walk away.
FY26 Baseline: ~500 to 535 kiloliters (KL).
Phase 1 (Q2 FY27 - Completed Ahead of Schedule): Commissioned 614 KL in Q2 FY27 (beating the initial 600 KL target).
Phase 2 (Target Q2 FY28): Civil work has already started to take capacity to 800 KL by Q2 FY28.
Phase 3 (Target FY29): Expansion to 1,000 KL.
Phase 4 (Target FY30): Expansion to 1,200 KL, more than doubling the company’s FY26 manufacturing base.
youtu.be/sS5Ik4HCW7Q?si=fhBu…
Morepen interview
Two major CEOs contesting on X. Peak competition
release all the numbers.
does 80% yoy become 65% when you remove passthru inference?
what does it become when you remove ai natives?
and how much of it is from one time software license --> service transition?
fcf, RPO, cRPO?
op margin non-gaap, gaap? dilution?
curious ppls want to know
ps: @alighodsi happy for your success. the world is big and life long.
teapawt.org/bhorowitz/status/21150…
Lot of businesses with below mental work end up working best:
Supply a relatively small fraction of the end product's cost, yet purity at parts-per-billion levels, or tolerance levels for a deviation are low aka multi-step reliability is mission-critical.
Gemini 4 on Antigravity is different beast
🚨Gemini 4 Argon @GoogleDeepMind with @antigravity
has made a significant leap on our RSI-Inference Bench rsi-inference.com with a close tie with GPT-6-Astra.
It's the first model outside @AnthropicAI & @OpenAI to pass a Day-0 Model Support task ✨
Congrats @GoogleDeepMind @PichaiSunder 👏!
Simrat retweeted
🚨Gemini 4 Argon @GoogleDeepMind with @antigravity
has made a significant leap on our RSI-Inference Bench rsi-inference.com with a close tie with GPT-6-Astra.
It's the first model outside @AnthropicAI & @OpenAI to pass a Day-0 Model Support task ✨
Congrats @GoogleDeepMind @PichaiSunder 👏!
We just added Google's Gemini-4-Argon to RSI-Inference Bench 🎉
Lands at #6 of 14 on the joint leaderboard, right behind GPT-6-Astra.
•Day-0 support: 6th, 81% of hidden tests passed (Pass@1 6.7%)
•Inference optimization: 4th, 1.39× speed-up
Spotify is creating friction in ad supported tier in India to push users to become paid subs. Music to the ears of music labels in India 🎵🔥
Pratt (the aircraft engine maker) is integrating AI-powered engine inspection software to accelerate borescope analytics. Already in use on legacy programs, this capability is being implemented across our MRO networks and has reduced overall inspection time by more than 30% where it’s been deployed.
Diamonds and stones certification co in India is using AI to strengthen its moat, growth and margins:
Someone asked whether AI could lower barriers to entry for small competing labs.
1. Management’s answer highlighted data scale: machine learning in gemology requires millions of high-resolution stone images paired with multi-grader human verification.
2. Grading 4.35 million stones per quarter gives IGI a proprietary training dataset that no startup lab can replicate.
3. Operationally, they have deployed AI-based work allocation and scheduling across its laboratories, alongside ML-assisted grading. This keeps turnaround times tight (2 to 3 days in India even during peak festive rushes) and allows the existing team of gemologists to grade higher volumes without linear headcount growth.
One of the major Copper company listed in US which mines in Latin America, says that usually miners pay smelters to get more and more copper out of mined raw ore concentrate but now the smelters are paying miners as smelters are abundant and miners and specifically high quality copper ore capacities are limited.
This also brings need of players which are play on falling ore grades. Someone selling grinding balls has a structural tailwind to churn out more and more copper from low grade rock.
Its new mines also need new 220 kV lines, substations and water, which are anyway constrained due to DC buildout.
Scarce mine supply pushes buyers toward scrap.
Few interesting points came in Nokia concall which sits above optical fibre companies:
1. When GPUs are scarce, a better network lets customers get more use from the GPUs they already have. So network spending can increase even when GPU supply is tight.
2. Telcos are doing capex for long haul and subsea networks. These carry traffic between data centres on long-haul and subsea routes. So part of what Nokia calls “telco” demand is really AI demand.
3. Distributed inference is driving significant demand leading to more optical content. More smaller DCs will come for inference as large sites are less and less available and more smaller DCs make the network more resilient, secure and robust. Same point came in optical fibre company concall in India
AI architecture requires significantly more physical connectivity than traditional cloud computing. The number of fibers per switch rack has skyrocketed from roughly 500 in the traditional cloud era to 12,000 for today's 72-GPU AI nodes, and is heading toward 16,000 fibers in upcoming architectures. This scale-out, scale-across, and scale-up dynamic makes high-density optical fiber strictly non-negotiable.
Diamonds and stones certification co in India is using AI to strengthen its moat, growth and margins:
Someone asked whether AI could lower barriers to entry for small competing labs.
1. Management’s answer highlighted data scale: machine learning in gemology requires millions of high-resolution stone images paired with multi-grader human verification.
2. Grading 4.35 million stones per quarter gives IGI a proprietary training dataset that no startup lab can replicate.
3. Operationally, they have deployed AI-based work allocation and scheduling across its laboratories, alongside ML-assisted grading. This keeps turnaround times tight (2 to 3 days in India even during peak festive rushes) and allows the existing team of gemologists to grade higher volumes without linear headcount growth.
Simrat retweeted
Chamath reveals 1% of companies are 90% of AI token spend, and 80% of them are spending money from unprofitable VCs
"So the cycle here, is VCs raise from pension funds and LPs and foundations and family offices. They put money into a startup. That startup takes that money and invests in growth."
"Part of growing is by consuming other people's AIs. And so that company then starts to show revenue traction. Those folks then say, 'Hey, look how well we're doing,' raises a bunch of money from the same VCs. And it's just this circularity."
"Why that's important is what Ken Griffin is pointing out is that for AI to really scale as a predictable driver of long term earnings, it has to diffuse out of the startup ecosystem."
"You can't be in a situation where on the one hand, you're talking about breathtaking valuations, but 75 cents of every dollar is actually just a startup sending it back and forth so that they can mark themselves up. That's, I think, a structural problem."
"We've seen that structural problem in the past. That happened in the early days of cloud, when AWS initially started to expose EC2 and S3. The consumers of an EC2 instance or an S3 bucket were startups.”
“But the difference there is that AWS was abstracted from Amazon retail, which was already then a roughly break-even profitable business, at least on a free cash flow basis."
"So what's different here is you have a bunch of money-losing entities just spinning a bunch of money around and it has to diffuse more broadly."
Some good points coming on skewness and nature of current AI demand. Bubble or no bubble, one must keep eyes open.
