The AI Paradox: Why Traditional IT Services May Thrive, Not Die

Investment Learning
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The simple answer is that the analyst is not worth his salt . Developing LLM from scratch is totally different from the existing business of IT services companies . If the cash in hand was the only thing that was needed ,maybe the question could have been put to the Ambanis instead .Developing LLM’s needed cutting edge research and inputs from best of the brains in US universities ….not available to TCS etc . Also , a listed company can only spend a significant portion of its cash reserve on a near sure bet ..adventurism is not rewarded by shareholders .Venture capital is for that purpose and anthropic and OpenAI used just that .Consider, before 2022, what percentage of reserve cash was spent by Meta or Google on AI ? Once AI wind picked up ,then only they came out with it or collaborated with already successful or nearly there startups.

There is so much misunderstanding about what IT services companies do that its easy to think IT is IT …but fact is its an Ocean . Companies like TCS or Cognizant earn their money through lending resources at hourly rates (Time and material ) or as fixed bid or managed service for a certain set of jobs encompassing activities like operations ,maintenance of existing infra, integration and upgrade of new tech into existing infra or by developing new solutions for the application layer . They mostly do consulting and execution both ..pure consulting is for companies like Accenture or IBM or specialist companies on niche technologies.

Analysts might think that its just replacing those resources with AI ,but fact is that in most real enterprises ,which are clients of TCS and its ilk, there exists an amazing jumble of technologies spanning generations of Software (OS and Apps) and hardware and old mess. It takes lots of actual intelligent human beings to keep things running smoothly and keep it updated …AI is not at that level at all where it can replace humans . It is very useful and good for productivity increase for both developers and support guys …resulting in some man hours saving . But usually there is always something else to be fixed or done .

The job loss is happening because IT companies have to bid against each other and the clients also will prefer to have AI (because its the shiny new thing to brag about) . So TCS or HCL have to say that they are going to do things with AI.For the fixed bid or managed service projects(80% of all projects) its actually beneficial to them if AI helps to get things done with 3 guys instead of 10 .Problem is that very few activities can be easily handed over to AI agents .In real life , the 5 ,10% of exceptions or special cases is what takes up 90% of man hours because over the years ,most setups have a lot of automation in place .And this is where ,AI will not be very useful …

But the managements need to show the clients that they can do what they said they will do when bidding ..else the projects may not extend . So they try by putting in AI savvy people to help integrate AI, but client is not paying extra for it .So to maintain margin, the upper management is ruthlessly reducing manpower from existing teams in every project across the board .

Observe Cognizant…their CEO increased revenue in past 2 years and now is focussing on profitability .Yet, since the AI is not actually generating extra profit, they have to cut headcount..just as TCS did last year .Only difference…they are doing it silently ..just ask around ,how many people are being released to the benches .I guess everyone knows what happens one or two months down the line . This same thing has happened for lots of other companies…americans can fire aloud in America but its not politique to do so in India.

Inevitable fallout will be the quality of delivered works will go down …projects will change hands from one IT support company to another until the AI dreams meets and merges with reality for the majority of people .Thats some time away maybe but unless AI achieves superintelligence …I do not see it happening any other way.

I have been in one of these IT companies for donkeys years now and I find AI very useful too,but its not ready for autopilot at all .The hallucination and false answers are abundant and not negiligible at all. Ultimately , the responsibility of its actions have to be taken by some human being or a company . One can fire an employee and get away with a production issue but what will you do when the error is due to the AI ?

PS: edit was for grammar correction.

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What explains the Chinese entry into LLMs? Is it a combination of good brain power like Maybe former workers in the U.S. or is it a copy of the open source LLMs put out by American companies, coupled with the financing done by the government behind the scenes? What would you say is the single largest factor in their entry into the LLMs and giving American companies a tough fight?

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China already had homegrown internet ecosystem in which they building everything for their sovereign needs than relying on US tech giants. They had a thriving tech ecosystem. Companies like Alibaba, Tencent, Baidu, ByteDance, Huawei, and Xiaomi developed mature ecosystems long before generative AI.

Their tech entrepreneurs taking the risk doing something innovative instead of simply being content with dhandha. They are no saints, the distilled a lot of data US frontier LLM systems like ChatGPT, Claude etc. But it feels justified given US was trying to restrict them advanced GPU access to prevent their progress. Now they designing their own chips.

They started out with open source and are now slowly closing the models to proprietary systems as their quality is neck to neck with US systems.

India is playing catch up and will probably get there. Beyond a certain threshold there is very little incremental difference in the outputs of different LLMs. LLM Convergence will be detrimental to Model Providers like OpenAI as it will turn it into a commodity.

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Do you believe that companies like Meta ,Google etc. invested in LLM’s because of betterment of mankind ? They did so because they thought they had a cutting edge product with huge scope of monetization . They had market share all over the world for selling it …except China. China does not allow American companies to run western propaganda ..they have their own ecosystem to peddle their own .So of course they have enough economic reason to go for LLMs.Its not all about business for them.But of course, if their companies can challenge the Americans at their own game in international markets …why not ?

Secondly, LLMs are characterised by the content they are trained on .So a model trained on western free world data will have western personality and vibes . China can not allow that to happen .So they must build LLMs trained on chinese data .The threat of an actual conflict with US also must be kept in mind .India also now has this same idea but we became serious about it after Trump came along with his Anti globalisation stance .

Also, why always this question…China did so..why not us ? Why not ask instead about UK, Japan,Germany etc ? Did their companies start building general AI models before chatgpt came out ?

Once the american behemoths with their deep pockets and global market share are on the march , is it prudent to go up against them with comparatively puny resources like TCS,Infy ? Just think is it even profitable for the LLM companies ..even now ? On plain Compute basis ,it is ,but the R&D costs make it 1.6 rupee per 1 rupee generated revenue. Its worse when you have to consider that they have to spend even more for continuous improvements and newer models . OpenAI spent 34 billion dollars to make 13 billion sales last year . With new competition ,I do not imagine things will get easier for them to become profitable .

I would be happy if the IT company I invested in, does their best to further their bottomline …I don’t like them to jump on the bandwagon just to pander to mob mentality . It makes sense to go for specialized industry specific LLM’s but building AI datacenter does not .

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Thats an excellent question. Things always need to be looked wholistically and this is a good way to ask & understand better. Thank you

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People from almost every field I talk to, as they are well aware of the complexities involved, think AI can’t do what they do.

This view requires reconsideration. All of us know that AI is building software systems now which itself is a very complex engineering activity. Let us ignore that. What I consider even more complex is fixing spaghetti code which was built over decades with original developers long gone. I had to pinch myself, when I learned that for a decade old elusive bug in such a legacy system, AI (Claude) found the root cause of the problem and provided the fix.

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That said, I do believe there is AI bubble in the investing. Our IT services is probably being undervalued now. The bubble may burst anytime.

However, I don’t think AI as a technology is a passing fad. What I mentioned above is what it can already do. One thing about technology, it will keep improving. Some of our views seem to underestimate existing AI technology itself and also forgetting it will keep getting better.

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This reminds me of our dot-com bust when use cases for the internet were not clear in the early 2000s till Google came along and found a way to monetize it. Today the internet is indispensable. I feel the AI ecosystem is headed the same way and we do need to jump on to the bandwagon and ride along. A few people may fail but in the end it will leave us as one of the contenders in the forefront, like how the IT sector has become big because of the internet.The counterargument about IT companies not investing in startups: should consider that Microsoft and Amazon and Google , existing players, are the biggest investors in these new-age companies. I am afraid that our IT companies have not taken the same kind of financial risks like the IT giants in the US.

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That is factually correct. The dna of these companies are innovation, research & start up mindset while that of Indian IT has been to implement, maintain & control what they produce….

One simple question - if everyone produce, then who will serve? (in deeper sense even control) Unless AI begins to produce & serve & produce & serve….and if it does all of that, then what happens to those who currently produce?

Something to think about….

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https://finance.yahoo.com/technology/ai/articles/anthropic-blackstone-bet-next-trillion-131047020.html?soc_src=social-sh&soc_trk=wa.

I dont know. Maybe this is the way ahead?

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Its the PE deploying for PE …the investors in the deployement company using their investors only as customers :joy: I think its the right time for Indian IT to gradually increase their own AI ecosystem increase their billing rates :grin:

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Same perspective as Infosys’ Nandan Nilekani. I see that he put some money behind his words. About ₹75L. I doubt if this is serious money for him.

Person Transaction Mode Quantity Percent
29 May 2026
Nandan M Nilekani ACQ Transmission 6,400 –

Any of you find leaders of our IT services, invest their money at these apparently cheap valuations considering the bullishness they convey regularly?

Disclosure: I have token investments in IT services. I’m currently neither bullish nor bearish. Not bullish since AI brings structural change to this industry and I find it too early to judge the impact on our IT services, especially on the large ones. Not bearish since our IT services will be the obvious choice for enterprises to adopt AI or business as usual if AI is one expensive fad.

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Another brilliant article by a ex-senior Accenture person to see what is happening to Accenture/IT services and how their valuations stack.

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A few days ago, I came across a Zoom panel conversation featuring prominent AI critic Ed Zitron. His arguments made sense to me, so I researched his broader bear thesis on the AI sector.

Reading his points clarifies why LLM companies like OpenAI are rapidly announcing their implementation services using fancy titles like “Forward Deployed Engineering”: they are desperate for sustainable liquidity and cash flow.

Key takeaways from his argument:

  1. Inverted Unit Economics:

    Generative AI exhibits unit economics that are the exact opposite of traditional software. Standard software scales with near-zero marginal cost per additional user. In contrast, generative AI compute demand scales upward with heavy usage. Every active prompt and reasoning step increases variable compute costs.

    Flat-rate $20/month consumer plans struggle to stay profitable under power users, particularly as models adopt agentic loops and test-time reasoning that burn exponential tokens. Unless end-user monetization increases dramatically, software gross margins will compress rather than expand.

    This explains why LLM companies are getting into IT consulting and custom implementation services: their core software product is inherently low-margin or unprofitable at scale, as flat subscriptions fail to cover compute expenses. To achieve standard tech platform margins, they would need a portfolio of self-sustaining “Super Apps” (analogous to YouTube, Gmail, Instagram, or WhatsApp). Currently, they only operate a single flagship chat application, and that too grows more expensive to run as usage scales, rather than cheaper.

  2. Counterparty Concentration Risk around OpenAI:

    Hyperscalers like Microsoft and Oracle have heavy growth dependencies on OpenAI. If OpenAI hits a liquidity wall or fails to hit monetization targets, it directly impairs these public cloud providers’ long-term order backlogs and growth visibility. See screenshot below for factual numbers supporting this:

    Screenshot 2026-08-05 at 9.49.03 AM
    Screenshot 2026-08-05 at 9.49.03 AM1046×1626 188 KB

  3. Circular Revenues & Hardware Depreciation:

    (Widely discussed so won’t repeat it here)

I only hold a small tracking position in IT, so my views aren’t heavily biased, though they may naturally be ill-informed or marred with incorrect interpretations.

Overall, the AI vs. Non-AI dynamic is still in its early stages, and it is too early to claim that AI will completely disrupt traditional IT service providers. The arguments making this disruption sound inevitable, like the labor arbitrage or IT budgets completely shifting to AI are oversimplifications of something that is not so simple and totally undermine the power of “decades of customer relationships and reputation, the complexities and customizations” in services and implementation businesses both at the customer’s organization and in-house operations.

For deeper context on Ed Zitron’s counter AI thesis, you can watch this zoom conversation here: AI: The Wheels Are Coming Off with Julien Garran, Ed Zitron, and George Noble

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Adding a slightly different angle to this thread:

1. LLMs are commoditizing fast. DeepSeek’s R1 hit near-frontier benchmarks at a fraction of the training/inference cost; open-weight models (Llama, Qwen, Mistral) keep narrowing the gap with GPT/Claude. Inference pricing across OpenAI, Google, Anthropic, DeepInfra has been on a steady downward curve — that’s a commodity price pattern, not a moat.

2. The value moves to the layer around the model. Retrieval, tool-orchestration, evaluation, domain grounding — this is where the real product work is now. You can see it in the rise of MCP-style tool-calling standards, vector-DB infra (Pinecone, Qdrant, Weaviate), and vertical AI products like Harvey (legal) or Abridge (healthcare) — none of them win by having a smarter model, they win on the plumbing around a commodity model.

3. That’s exactly the seat Indian IT services should take. Not writing code, but building and running that plumbing for enterprises who won’t build it in-house. Shape changes — less staff-aug, more AI-integration/transformation deals — and you can already see TCS/HCL repositioning their pitch this way.

4. Hardware innovation is early, not late. Nvidia’s Blackwell/Rubin roadmap, custom silicon (Google TPUs, AWS Trainium, Microsoft Maia), Groq’s inference chips, Cerebras’ wafer-scale approach — plus the power side (hyperscalers signing nuclear deals to feed data centers). A lot more is coming here.

5. The bigger unlock hasn’t started: software that makes AI cheap to run. Caching, distillation, agent memory, orchestration efficiency — this is a decade-long buildout, not a 2026 story.

Writing this from inside the industry, not as a spectator — open to pushback.

Loved this topic. Thanks to the person who started it and all the contributors up here.

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60% of India’s population is below 35. This means that this cohort will continue to consume for a long time. Their demand for good and services is expected to power the market and fuel growth. Therefore the high PE from anticipated growth.

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How is that related to IT which get most revenues from services exports?

High Youth employment and overleveraged households balance sheets don’t bode well either way.