u/pyeri

I want to understand what the largest ITES firms in the space like Infosys, TCS, TechM, Persistent, Hexaware, Wipro, etc. are thinking about AI. We are currently at an interesting point in history where the stock market valuations of the LLM tech companies are at peak but the actual adoption is seeing mixed reactions everywhere. The next few months will see a 'make or break' situation in this space and the scenario will be more clear. Companies will be forced to adopt a policy of either increasing productivity by embracing AI fully or cutting AI usage due to mounting token and infrastructure costs. There won't be any middle-ground left.

If you work at any of these firms, please share your experience. Which direction is the work environment going? Do you see massive adoption in AI usage? Are you getting incentives or better appraisals based on AI tokens spent on Claude Code, Codex, etc?

What I realized today while sipping my morning cup of chai is that a large part of the present AI hype cycle is just about the 'narrative of AI'. The LLM technology itself is just a digital tool like many others that came before it but all this chatter about 'AI is the future', 'learn it or perish', 'machines will replace humans soon', etc. keeps it in the news and creates a burger out of nothing. But folks lose their energy and sleep over this which becomes a problem. And many a benign enterprises are falsely led to believe that this technology can do wonders, it's a great disservice being done by those selling these bridges.

For folks like us who are fed up of this slop narrative - instead of fighting it, it's better to try and shift the narrative towards determinism. For centuries and millennia, we have been trained to avoid and even fear fatalism (a negative aspect of determinism), and look towards future with hopes for betterment and prosperity. There is nothing wrong with having hope but for the first time in known history, the 'future will be glorious' theme isn't looking so bright, at least to a large number of folks connected to grassroots.

In order to push back against this narrative, we need to turn determinism into a force of good, or at least a force preferable to the alternative. Even in the LLM space, most of the utilitarian things are happening in the low-end, open source and local LLMs niches which are more deterministic in approach. What can we do to bring back people's interest in regular deterministic programming with C, Java and Python?

Asimovian AI is the ideal AI that should have emerged in an ideal universe — the AI intended to replace the grueling pains and labors of the masses, not the one striving to become a businessman's utopia of intellectual worker replacement. Intention is the most important aspect of any implementation, and we are seeing the results of current AI implementation right in front of us: workers getting sacked with each passing day, humanity competing with itself day in and day out over who impresses their superiors more on these token metrics, emerging glorious narratives of how AI will be 'The Future', the recurring advice of 'Use AI or perish in the tech market'. Now who really gains from these events and who loses? I wonder if anyone ever gives serious thought to this broader question or just keeps being a cog in the corporate wheel like everyone else.

It's high time we pushed the "Pause AI" button right now and take a breather and reflect a bit on what exactly is going on here. And no, no big catastrophe is going to happen if we do that. China isn't going to get ahead in the race - and even if it did, how does that justify everything else that's happening here?

I really hope there is someone out there with enough clout and influence who can push this pause button - or at least persuade others to do so. That would be the best thing to happen to humanity at this point. By doing so, we might prevent a massive societal collapse and there is really no downside to this.

Since another popular thread called "Do you have concerns about AI?" was making the rounds on this sub today, I wanted to provide a counterweight.

Most discussions about AI risks tend to derail into two extremes: either abstract corporate profit margins or sci-fi Skynet apocalypses. But focusing on a far-off digital deity misses the point. The real danger of AI isn't an explosive takeover; it's a slow, quiet, mundane erosion of human capability and social fabric happening right under our noses.

Here are the four legit concerns we should actually be talking about.

1. The Atrophy of Human Cognition

I’m not talking about rogue killer robots; I'm talking about basic cognitive offloading. Because AI can instantly draft an email, debug a script, or summarize a document, we are outsourcing the very friction that builds intellect. Writing isn't just a way to output data—it is the process by which we figure out what we actually think. When you replace the messy, difficult process of critical thinking with a prompt box, that mental muscle begins to trophy. We are rapidly moving toward a society that can critique output but can no longer generate original thought from scratch.

2. The Severing of the Career Pipeline

The generic "AI will steal jobs" debate misses the structural nuance of how it happens. AI doesn't just eliminate roles; it severs the apprentice-to-master pipeline. If an LLM can handle all the baseline junior-level work—writing standard boilerplate code, drafting entry-level legal documents, or doing basic data entry—companies stop hiring juniors. But if you eliminate junior roles, how do you cultivate the next generation of senior experts, architects, and leaders? We are risking an industry-wide talent cliff a decade from now.

3. Simulated Bonding and De-socialization

This is perhaps the most insidious shift: human isolation mixed with synthetic companionship. We are seeing a massive rise in people using AI as therapists, best friends, and romantic partners. The core issue here is that an AI is explicitly programmed to never disagree with you, to always validate you, and to demand zero emotional compromise. Real human relationships are messy, exhausting, and require conflict resolution. By succumbing to frictionless, simulated bonding, we are ruining our psychological tolerance for real, imperfect human interaction.

4. The Rise of the "God Complex"

This manifests on two distinct levels:

• For the Creators: We have tech elites openly speaking about building AGI as if they are summoning a digital deity, operating with a savior complex that completely bypasses democratic oversight or ethical humility.
• For the Users: It creates an illusion of absolute mastery. Because a user can type a single sentence and generate a complex piece of art, music, or code, they develop the false sense that they possess the deep skill and wisdom of a master craftsman. It mistakes access to a tool for domain expertise, destroying our respect for deep, long-term mastery.

The real threat of AI isn't that it will turn evil and destroy us. The threat is that it works just well enough to make us comfortable, passive, and entirely dependent—substituting genuine human capability for an algorithmic proxy.

Consumerism is the idea that encourages the continuous acquisition of goods and services by ordinary plebs. Consequently, the solutions to a problem many LLMs present are also geared towards maximum spending, they won't present a budget, non-premium or a free solution even if one exists. They might elaborate if you ask about it specifically but rarely come up on their own. The default option is always the most pricey or premium option. Did any of you notice that with specific LLMs?

If you trace the timeline of how LLMs went from a technologist's dream to early text-generation toys, to the world-shifting launch of ChatGPT, and finally to the daily drivers of modern programming (Sonnet, Opus), it has taken less than a decade. It’s a thrilling, almost unbelievable tale.

Let's look at how we got here, and the wall the industry is currently hitting.

• The Dream Phase (2010-2016). By the dawn of the last decade (2011), an interesting thing was happening. The two platforms, Wikipedia and Stack Overflow, had started gaining tremendous traction, folks were collaborating on these platforms to openly exchange knowledge. Looking back, this feels like a more ideal, community-driven path for humanity — one we abandoned for the centralized architecture we have today.

• The Disruption Phase (2016-2021). A perfect storm of unrelated events paved the way for AI. By 2017, new programmers were growing deeply frustrated by Stack Overflow's rigid policies, subjective question rejections, and senior coder pedantry. In retrospect, those strict moderators carved the first stones of what would later become Copilot and ChatGPT. If the community won't answer a beginner's question without downvoting it, a private LLM gladly will.

Add to this Google's landmark 2017 paper "Attention Is All You Need" which unlocked the Transformer architecture, and the forced isolation of COVID-19 in 2020. The ground was suddenly fertile for virtual assistants that could act as isolated developers' programming partners.

• The Hook Phase (2023-2025). The launch of ChatGPT left no doubt about how easy the "hook" would be. For non-technical folks, it was pure magic. It didn't take long for specialized LLMs like Copilot, Claude and Deepseek to become an indispensable part of the programmer's toolbox. Meanwhile, OpenAI was still advertising its "non-profit" roots, and the consensus was that this was purely about empowering humanity.

• The Endgame Phase (2025-present/future). AI companies had miscalculated a lot of things by this time. They were optimizing for the "long-term" but as John Maynard Keynes rightly said many years ago, "In the long-term, we are all dead". The VCs are losing patience today because while the technology itself has gained massive ubiquity and appreciation, the revenues aren't coming as fast. The hook had sort of worked but failed to fully work.

Most frontier models like Sonnet, Opus and GPT 5.5 are still running on 'subsidized mode'. The amount of monthly subscription they charge users (USD 10/20/30 per month) is a pittance compared to all the compute and RAM needed to run those "thinking..." and "pondering..." tokens. In order to truly show profits in the books and come out of subsidized mode, they must charge on the scaling of input/output tokens and that appears to be difficult. Very few companies might be able to sustain such unlimited budget for unpredictable hardware scaling, the recent Uber story shows exactly what happens when they try doing this.

The frontier models are trying to replace something which could never be successfully delegated or automated in entire human history - the highest cognitive skills of human brain like reasoning, deduction and logic. Yet, the efforts are on and the goals are long term. The conundrum is that if they stop subsidizing, the hook phase may be undone - there is a strong possibility of folks reverting back to older ways of Wikipedia/Stack Overflow or pivot entirely to open source dry/academic models like Llama and Qwen which can run locally on their own hardware. And yet, they also can't keep subsidizing and draining the funds indefinitely.

What happens when the subsidy mirror cracks?

The logic is simple. Taking the accountancy metaphor, other nutrients like carbs, fats, vitamins, minerals, etc. act like stocked inventory or portfolios - you can stock up on them each week or even fortnight. But you can't do the same with protein, it's like a current cashflow account that needs a daily intake for maintenance of your muscles and vital organs.

As a rough estimate, every KG of your weight demands about 1 gram of protein; for example, a 75KG person will need about 75g protein daily for long term sustenance. If you're going to have only one shot for a meal each day, make sure your protein intake is maximized to utmost extent in that one single window. Non-OMADers typically don't have to worry as much on protein because they'll somehow get those grams through all the junk and toast they eat throughout the day. But for OMADers, it becomes existential if they want to have OMAD as a permanent and sustainable lifestyle.

Coming from the vegetarian world, here are some eating tips I can offer (non-vegetarians have slightly more leeway here due to the quantum of animal protein in their diet):

• Use millets instead of rice. Plain rice only has 5-6g of protein per 100g whereas Foxtail and Barnyard millets have double that amount. Also ensure a minimum soak of at least 8-9 hours for maximum absorption.
• Double down on legumes and sprouts. They're an important protein source in the vegetarian world, a veg meal is practically incomplete without them.
• Double down on milk and paneer (alternatives like soy milk and tofu exist for vegans). And don't forget soy chunks and chickpeas.
• Double down on nuts and dry fruits. Almonds, cashews, peanuts, pumpkin seeds, walnuts, pistachios, etc. are all great sources of protein, a habit of including them in the meal even occasionally pays great dividend over time.

When I migrated from Linux to Windows few years ago due to work and profession reasons, one of the things I highly missed was the command line dict program which was as easy to install on Debian as sudo apt install dict.

There were a few GUI ones like WordWeb, Artha, etc but I didn't like the idea of memory resident apps that occupied the taskbar and memory. CLI was efficient and ran only when needed for word look ups. Consequently, I wrote a similar tool called dict in C# using the open source WordNet database.

WordNet is structured in a way that has core index files for each part-of-speech like index.noun, index.verb, etc. plus dedicated data files for each one of them. Looking up a word or term is a matter of seeking the index files to find exact matching offsets and use those offsets directly in data files to fetch the definitions or synonyms as needed.

Writing this tool is both a great exercise in C# coding and also a way of ending up with a highly utilitarian daily driver. I highly encourage you to do the same if you haven't done yet.

Open Quiz Data Bank is an ever growing body of MCQ sets which you can use as a seamless backend or even as a REST API for your Quiz and Learning apps.

The most difficult phase during OMAD comes when the weight loss target is achieved (or even quite near) and you start thinking about maintenance even as the struggle continues against calories and insulin resistance. The problem with OMAD is that there are very few meals which can grant you all the right amount of carbs, protein, fats, fiber, micronutrients, etc. Plus you must do this within the constraints of your native cultural configuration (like Veganism or Vegetarianism for some).

And even if you did find that perfect meal, eventually the monotony sets in and you need some variation. There could also be the case that you're having too much of some nutrients and less than optimal portion of others. To cite a tech metaphor, this is a bit like choosing the perfect Linux distro or an operating system, it doesn't really exist!

Nevertheless, the body has its own ways of adjusting to new and evolving circumstances. After all, we did survive long durations of time without food as paleolithic hunter gatherers, that phase must have taught us at least something. For one, as your net deficit continues and you start losing weight, the TDEE or calorie expenditure also comes down gradually. You no longer need as heavy meals now as you used to.

Unless that one meal gives you at least the bare minimum of survival TDEE in the range of 1400-1600 kcal (an approximation), it's not sustainable in the long-run though. Right now, my struggle is to maintain that one meal in that one window while also avoiding the monotony. Today is the 25th day of my OMAD. Any tips or suggestions would be quite helpful.

A couple of days ago, there was a debate here about whether OMAD is a diet or lifestyle, and mention of a schism in the community between these two sets of folks. Let me tell you why it's a way of life for me. There isn't one but multiple premises for doing this intense form of fasting and I'm going to list them one by one.

1. The Law of Diminishing Marginal Utility (DMU): This is a celebrated law in Economics penned by reputed Micro Economists in the early years of Industrial Revolution. Though it was about products specifically, it can apply to a lot of things like food. A common observation is that the more you eat, the marginal utility (satisfaction derived from the last morsel eaten during a course) becomes less and less. This effect is masked when you eat processed food by big chains and restaurants because they engineer the food in a way that DMU doesn't work and you stay in the illusion with constant dopamine chase. The rational and logical step to maximize the satisfaction of that last morsel here is to minimize the eating windows - which is what OMAD is all about.
2. Autophagy and Ketosis: You can find all the literature on OMAD induced Autophagy in online videos and papers. It was practically rubber stamped with a Nobel in 2016, validating what the Tibetan monks already knew and practiced since centuries. On Ketosis, there is still a raging debate in the online communities on whether glucose or ketones is a better body fuel. The goal of OMAD doesn't have to be a full keto diet though, a balanced and moderate diet consisting right amounts of both fat and carbs is still ideal.
3. Paleolithic Minimalism: Structurally, we are constantly trying to fit our 21st century mind (software) on a paleolithic era homo sapien body (hardware). Evolution wise, our metabolism evolved at the start of the paleo era around 2 million years ago and the industrial revolution is barely 2 centuries old! Our bodies aren't designed to constantly graze on engineered junk food 24x7 - even though the big food industry is constantly prodding us to do so. An OMAD diet is the most compatible to our body design, and this should be the greatest reason to adopt it.

I'm going through that stage right now (44 yrs, male). Started OMAD fasting on 29th April and today is my 5th day. My energy level is up, in fact much more stable than pre-OMAD - but there is a slight mental fogginess or abstractness. Is this natural, how long does this transition phase last?

I don't feel any food cravings, it persisted in the first 1-2 days but no longer there. What happens now, how long does it generally take for things to sort of stabilize from here?