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TCS Research —

TCS has had a research division for decades. They publish papers, hold patents, and have researchers with genuinely strong credentials. On paper it looks impressive.

In practice the picture is more complicated.

Their AI work lives under the TCS AI .Cloud umbrella and feeds into products like Cognix and their Ignio AIOps platform. Ignio is actually one of their more credible AI products — it does cognitive automation for IT operations and has real enterprise deployments. It's not flashy but it works and clients pay for it.

Their research papers get published in decent venues. They've done work on document understanding, computer vision for manufacturing, NLP for customer service. Competent, applied, incremental work.

The honest problem? Almost none of it is pushing any boundary. It's research in service of selling IT contracts, which means the research agenda is set by what a Fortune 500 client might pay for, not by what's scientifically interesting or frontier. You're not going to see TCS Research publishing anything that makes the ML Twitter crowd stop scrolling.

The other problem is talent retention. Researchers who are good enough to do frontier work are also good enough to get jobs at Google DeepMind or Meta AI. TCS's compensation and research culture can't compete with that. So what you're left with is solid, experienced researchers doing solid, unambitious work.

They recently announced TCS Pace as their innovation ecosystem and have been talking up generative AI integration across their service lines. Mostly this means helping enterprise clients implement AI — which, again, is a real business. Just not a research story.

Infosys AI — the Topaz problem

Infosys launched Topaz in 2023 with considerable fanfare. An AI-first set of services, platforms, and solutions. Over 150 use cases. Thousands of AI assets. The press release was very long.

The substance underneath is thinner than the announcement suggested.

Topaz is mostly a consulting and implementation offering dressed up in AI language. There's some genuine tooling — their work on AI-assisted code generation and document processing is real — but the core of it is Infosys helping enterprise clients use AI, largely meaning helping them use OpenAI or Microsoft Azure AI, with Infosys services wrapped around it.

Their research lab Infosys Research publishes work, mostly in NLP, knowledge graphs, and responsible AI. Some of it is decent. None of it is remarkable.

What Infosys did do that deserves credit is invest early in training their workforce on AI. The Infosys Springboard platform and internal AI upskilling programs have put basic AI literacy in front of hundreds of thousands of employees. That's not research but it's not nothing either — at scale it matters.

The deeper issue with Infosys's AI story is that the incentive structure works against serious research. Their business model rewards billable hours and managed services. A research breakthrough that makes a process ten times more efficient is good for the client and bad for the revenue model. This is a structural problem that no amount of Topaz branding solves.

Why both of them are kind of stuck in the same place

If you step back, TCS and Infosys have almost identical problems.

Both are services businesses pretending to be, or genuinely trying to become, product and research businesses. The transition is hard for any company. It's especially hard when your core business is so profitable that there's no burning need to change.

Both have research labs that exist partly for prestige, partly for recruiting, and partly to generate IP that can be referenced in client pitches. That's not a formula for frontier AI.

Both are structurally dependent on their clients' AI budgets rather than building AI capability that clients come to them specifically for. Right now a lot of that client budget is going directly to Microsoft, Google, and AWS anyway.

And both have the same talent problem. The researchers good enough to do serious work have options. The ones who stay are often doing so for stability, not because TCS or Infosys is the most exciting place to do AI.

There's also an organisational culture issue that people who've worked there describe pretty consistently — slow decision making, risk aversion, layers of approvals for anything novel. That's a reasonable way to run a $25 billion services business. It's a terrible way to run an AI research lab.

Where there's genuine hope though

This isn't all bleak and I don't want to be unfair.

Both companies have started making acquisitions and partnerships that suggest they understand the gap. TCS's work with academic institutions and their investments in domain-specific AI for industries like life sciences and manufacturing show some strategic clarity. Infosys's partnerships with AI-native companies and their work on responsible AI frameworks are at least pointed in the right direction.

More importantly — the enterprise AI implementation market is enormous and growing. The boring, unsexy work of taking AI models and making them actually work inside large legacy organisations? That's extraordinarily hard and extraordinarily valuable. TCS and Infosys are genuinely good at it. That's not nothing. As AI moves from research to deployment, that capability becomes more important, not less.

There's also a version of the future where one of them makes a serious bet — acquires an AI-native company, hires a genuine research leader with real autonomy, commits to a five year research agenda that isn't tied to client billing. It hasn't happened yet. But the resources exist. The question is whether the will does.

The talent coming out of IITs and into these companies is genuinely strong. If the organisational structure got out of the way, there are people inside both organisations capable of doing much better work than the current output suggests.

The honest summary

TCS AI labs: real but unambitious. Ignio is their most credible product. Research output is competent and forgettable. Talent problem is serious.

Infosys Topaz: more marketing than substance at launch, with some real tooling underneath. Structurally better suited to AI implementation than AI research. Springboard upskilling initiative is underrated.

Both: services companies with research labs, not research companies with services divisions. Until that flips — or until one of them makes a genuinely bold bet — don't expect either to show up in the conversations that matter in global AI.

The uncomfortable version: India has two of the largest IT companies in the world sitting on enormous resources, talent pipelines, and enterprise relationships. If even one of them built a serious AI research operation — not a press release, an actual one — it would change the Indian AI story meaningfully. So far neither has done it. That's the real missed opportunity here.

I've been sitting with this thought for a while and figured this community would have some real opinions on it.

We've seen AI systems now capable of solving olympiad-level problems, assisting in formal proofs, and even making conjectures. AlphaProof, FunSearch, the stuff coming out of DeepMind — it's moving fast.

But here's what I keep wondering: is this a tool, or is it eventually a replacement for mathematical intuition itself?

Like, a lot of us got into math because of the feel of it — that moment when an elegant proof clicks, when you see a pattern nobody told you to look for. Can AI replicate that? Does it even need to, or does it just need to outperform us on outcomes?

A few things I'd genuinely like to hear thoughts on:

Do you think AI will make pure math research more accessible, or will it concentrate power among those with compute resources?

Is there a risk that math education becomes hollow if students can just offload problem-solving to AI?

Are there areas of mathematics you think will remain fundamentally human for a long time?

Everyone talks about the "prestige" and the "funding," but nobody tells you that actually using that money is like trying to win a fight with a brick wall. If you’re a research aspirant, you probably think you’ll spend your time doing, you know... research. In reality, Its 40% researcher and 60% clerk/accountant.

The "L1" .... If you need to buy a specific sensor for your setup, you can’t just buy the one that works. Because of government "L1" rules, the institute is basically forced to buy from the lowest bidder. So, you end up with some cheap, knock-off version from a random vendor in Noida who has no idea how to support the tech. It breaks in two weeks, and you’re back to square one.

india's math olympiad performance has been quietly getting really good. 4th at IMO 2024 — four gold medals — which is the best we've done since we started participating in 1989. that's not luck, that's years of building a selection and training pipeline through HBCSE. and then 7th again in 2025 with a national record score. these are high school kids. the talent is clearly there.

TIFR's school of mathematics in mumbai does work that genuinely competes internationally. i'm not being patriotic here — i mean people there publish in annals, inventiones, JAMS. number theory, algebraic geometry, ergodic theory — the faculty are serious. IMSc in chennai is excellent. ISI kolkata has a history going back decades. CMI's undergraduate programme produces students who regularly get into top 10 global phd programmes.

the IISERs were a genuinely good decision by whoever made that call in 2006. seven institutes, proper research culture from the undergrad level, students who actually read papers before graduating. compared to what the situation was 20 years ago it's a real improvement.

the money situation is embarrassing. TIFR postdoc pays ₹47,000–54,000 a month. that is your salary if you have a phd and you're doing research at what is supposed to be our flagship math institute. in mumbai. have you tried renting in mumbai on ₹47k? a phd stipend at the best institutes is ₹31,000–37,000. meanwhile india's R&D spending as a share of GDP has actually gone DOWN — from around 0.9% in 2008 to 0.64% in 2021. china is at 2.4%. south korea is nearly 5%. we are going the wrong direction.

i'm not saying this to be dramatic. i'm saying this because i have watched extremely talented people — people who genuinely love mathematics, who would have been happy to stay — do the calculation (ironically) and leave. a us postdoc in math pays around $60k a year. that's roughly 50 lakh rupees. the gap isn't bridgeable by "passion for the subject."

there's a study that found over 73% of indian researchers who move abroad never come back. never. and the ones who leave aren't random — they're disproportionately from the good institutions, the ones we spent public money training. it's not brain drain as a metaphor. it's a literal, measurable, ongoing transfer of human capital that we funded and then gifted to the west.

the structural problems that don't get talked about enough

most IIT math departments are service departments. their primary job, implicitly or explicitly, is to make sure engineering students pass calculus. the faculty are evaluated on teaching loads that would make it very hard for anyone to do deep research. this isn't anyone's fault individually — it's how the system is set up. but it means that "math research at IITs" is often a very different thing from math research at TIFR or IMSc, and we shouldn't pretend otherwise.

the postdoc to faculty pipeline is basically a bottleneck. TIFR hires a few people a year. ISI a few more. IMSc a few. the IITs and IISERs have more positions but the hiring process is slow, political in the usual ways, and the positions aren't always in pure math areas. a person finishing a strong phd in, say, analytic number theory or low-dimensional topology faces a genuinely bleak domestic market. the options are: leave for a foreign postdoc (and probably not come back), take a position somewhere where you'll spend 18 hours a week teaching and maybe get two hours of research time if lucky, or just leave academia.

and then there's the JEE thing. i'll probably get flak for this but i'll say it anyway: years of JEE prep does something to how people think about math. JEE trains you to be fast, to pattern-match, to know which trick applies to which problem type. that's a specific skill. it's not the same skill as sitting with a problem for three weeks and not knowing if you're on the right track. a lot of students arrive at IISERs and CMI genuinely shocked that math can involve extended confusion and that this is normal and fine. the olympiad pipeline is a partial corrective but it reaches maybe a few hundred students seriously at the national level. JEE reaches millions.

the weird paradox that nobody wants to say out loud

india is good at producing mathematicians. like, actually good. the olympiad results, the quality of graduates from CMI and IISERs, the names — ramanujan obviously, but also harish-chandra, c.s. seshadri, m.s. narasimhan — these aren't flukes. the country has mathematical culture in the real sense.

what we're bad at is keeping them. we build the pipeline and then we don't finish the job. it's like spending years growing a plant and then not watering it when it's about to bear fruit. the people who could build india's mathematical future are making tenure decisions in chicago and cambridge and paris right now, and a non-trivial number of them would have stayed, or come back, if the conditions were different.

what i actually think would help (not a policy paper, just common sense)

• postdoc and phd stipends need to double minimum. ₹37k in 2025 is not serious. the PM research fellowship (₹70k) is a good idea — expand it massively and stop restricting it so heavily.
• NBHM (the national board for higher mathematics) does useful work but is chronically underfunded. triple its budget. it's not a lot of money in the scheme of things.
• we need more permanent positions at the serious research institutes, not just more IIT expansion. TIFR and IMSc are the crown jewels. act like it.
• there are indian diaspora mathematicians at good western universities who would genuinely consider coming back for the right package. make that package exist. even 20-30 of these people returning would be transformative.
• protect the olympiad ecosystem. HBCSE does heroic work on not enough money. the selection camps and training are a public good.

Can anyone recommend good lecture series/resources for olympiad-style Euclidean geometry/problem solving?

I’m looking for proof-based geometry resources covering topics like triangles, circles, transformations, coordinate geometry, inequalities, etc. — from beginner to advanced level.

Would especially appreciate:

• YouTube playlists
• Full lecture series/courses
• Books with good solved problems
• Resources that build strong intuition and problem-solving skills

Thanks!

Hey everyone! I'm u/diptesh_kun, a founding moderator of r/Indianmathnerds.

This community is for anyone who loves mathematics — whether you're solving basic algebra, preparing for ISI/CMI/JEE/Olympiads, exploring calculus, number theory, proofs, puzzles, or just curious about how math works.

You do not need to be a genius to be here. You just need curiosity.

What you can post here:
• Doubts & questions
• Interesting problems
• Olympiad / entrance prep
• Proofs & concepts
• Study resources
• Math memes
• Beautiful patterns in math

Be respectful, help others, and enjoy the grind.

Let’s build the strongest math community from India. 🚀

Introduce yourself in the comments:
What are you studying right now?

Thanks for being part of the very first wave. Together, let's make r/Indianmathnerds amazing.