Browse Comments — Relevant (AI ∩ value)

Very relevant perspective — AI creates the most value when it augments skilled people, not when it’s treated as a blanket replacement strategy.

AI is not cheap labor. It is leverage. But leverage only works if the workflow is designed well. If every engineer runs agents on every task without cost control, context, review, and clear ownership, the bill will explode. So I think it is not “AI vs humans”. I think the key q-n we should ask is: where does AI improve output per dollar?

AI is transforming work, but the hype ignored one reality: scale without economics breaks fast. The winners won’t be companies replacing humans blindly, but those using AI to amplify skilled teams, control costs, and solve high-value problems sustainably.

Prashant K. Sahni Honestly one of the sharper takes in this thread. AI did not create the inefficiency, it just made it visible and gave companies a cleaner narrative to act on it.

I fundamentally believe that AI cannot drive the growth companies expect by removing human work force. However, it can definitely be a force multiplier for people and companies who learns to work with it. I think doing this will save more money for businesses than the tangible /Intangible benefits of replacing humans with AI and sharing some direct benefits companies will have. 1. Reduced OpEx because it makes teams 10x productive and efficient 2. More time/bandwidth/headspace for teams to focus on things where AI cannot be trusted or used. 3. Companies save money on rework due to human errors.

SAURABH SINGH AI is a powerful copilot, but enterprise-level reasoning, critical thinking, understanding business context, and architecture is still a human responsibility. AI can speed up execution, but it does not reduce the mental effort.

Exactly. AI is powerful, but many companies treated it like a replacement strategy instead of a productivity tool. The real value still comes from human judgment, creativity, and problem solving,AI just accelerates it.

This is the correction phase of the AI hype cycle that many experienced engineers expected. AI absolutely boosts productivity, but “replace engineers” was always a flawed framing. Engineering is not just code generation — it’s architecture, trade-offs, debugging ambiguous failures, domain understanding, operational ownership, and long-term maintainability. What many companies underestimated:• Token economics at enterprise scale• Context-window inefficiencies on large codebases• Human review overhead• Hallucination-driven rework• The cost of bad architectural decisions generated confidently at high speed The real winning model is likely to be:Small, highly skilled engineering teams + AI augmentation — not AI replacing teams entirely. The companies getting the best ROI from AI today are usually the ones using it as a force multiplier for senior engineers, not as a wholesale substitute for engineering judgment.

The industry is moving from AI hype to AI accountability. The real question was never whether AI can replace humans — it’s whether companies can use it sustainably, strategically, and at scale. The future belongs to organizations that combine human expertise with AI efficiency, not those trying to choose one over the other.

Soon down the road, at this scale, AI will likely run out of compute capacity too. Recently, in North America, so much push back is coming against new AI data centers. Even, some environmental agencies are scrapping proposals of new AI data centers as studies are out about surrounding environmental impacts of such infrastructures. Even on local computation infra, when we observe the mac mini shortage, it tells the same story, not able to keep up with computation demand. Economics is all about demand and supply & when one gets disbalanced, we know what comes next.

AI is incredibly powerful, but clients today are looking beyond the hype. The companies winning with AI are not the ones replacing people blindly, but the ones combining strong talent with the right AI tools. In sales, relationships, trust, negotiation, and understanding client pain points still require human intelligence. AI can accelerate can accelerate pour workflows, research, outreach, and analytics, but it works best as an enabler, not a replacement.

Human Centric frameworks that puts AI-Forward is a good balance. But orgs who put AI-First without having that Human I. The loop are starting to realize the drawbacks.

SAURABH SINGH Interesting perspective Crazy how fast companies adopted AI without fully thinking about the cost impact.Feels like we’re still figuring out where AI actually adds value vs where humans are still more efficient.

Your point about token based pricing turning AI into a costly tool highlights why governance around usage is becoming as important as the technology itself. How are teams balancing the speed gains with strict cost monitoring practices?

I am convinced the prices will only increase as the constraints surrounding AI services tighten (energy, water, real-estate, chips, etc..) but while the price was always going to be high once the subsidies started to whilttle away what i think is interesting is how ROI of AI will be measured going forward. For example, human hourly rate vs AI task completion is not apples to apples and may not necessarily spell the replacement of a persons job but if that task would have taken a team of Data Scientists a week then perhaps a $2k price seems fair for a quick run... the question might become, what work should the team delegate this week? This of course requires organisational understanding on AI friendly work, forward planning and a strategic adoption where it makes sense.

Interesting perspective SAURABH SINGH. This is a good reminder that technology adoption and economic viability are not always the same thing. AI can dramatically increase leverage in the right workflows, but scaling usage without clear ROI, governance, and operational discipline can quickly turn efficiency gains into cost inflation. The long-term winners will likely be the companies that treat AI as a strategic multiplier for human capability — not simply as a replacement strategy.

This is what happens when companies don’t truly understand what they are getting. Ai is good , if utilised in the right way, but if your just installing AI , just because it’s AI .... Then your doomed to fail!

Yes, organizations purchased AI licenses across teams without fully evaluating where they truly fit or add value. As a result, many employees have access to AI tools but barely use them. Some use them only like a Google search engine, while others use them without proper context or prompting. Because of this, AI budgets are getting exhausted very quickly. Now organizations are beginning to realize this challenge and are asking teams to follow specific guidelines for using AI more efficiently and with lower token consumption.

The honest version of the Microsoft / Uber story isn't "AI is too expensive." It's "we deployed it without unit economics in place." Those are different problems. The first says stop. The second says instrument, budget, gate by ROI. Most orgs that "discovered the economics were never stress-tested" don't stress-test any tool until the bill arrives - this reads as a procurement maturity story dressed up as an AI story.

This is such an important reality check especially when studies are already showing AI is economically viable in only a fraction of roles despite the massive hype around replacing humans. I think a lot of companies underestimated that scaling AI also means scaling infrastructure, oversight, context management and decision accountability.