AI at Work: Who Wins & Who Loses?

Our goal with The Daily Brief is to simplify the biggest stories in the Indian markets and help you understand what they mean. We won’t just tell you what happened, but why and how too. We do this show in both formats: video and audio. This piece curates the stories that we talk about.

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In today’s edition of The Daily Brief:

1. What AI means to the world of work

2. The CCI is trying really hard

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What AI means to the world of work

Let’s face it. Everything you’ve heard about AI is faff.

Ever since we were hit by the AI wave, there has been constant speculation around what it means for us. What does an AI-infused working world look like? How does it change the world of work? What should you do to make sure you keep your job? You’ve probably heard theories on this from every single person who considers themselves smart, but there’s very little that you can actually go by.

Until now, that is. The folks at Anthropic — the company behind Claude.ai — looked through millions of conversations people had with Claude to see what they were using it for. This is the first time we have any actual data on how people use AI for work-related tasks.

Here are our five biggest takeaways from their paper.

1. Who is using AI the most?

AI isn’t advancing across all sectors of the economy simultaneously. Instead, its use is concentrated in a few domains. Now, we know this is rather obvious, but for the first time, we have actual data to back it up.

Half of the world’s AI work happens in coding or writing. Computer and mathematical tasks make up over a third (37.2%) of all AI interactions while writing and content creation account for another 10.3% (we can neither confirm nor deny if we’re part of this statistic 🥲).

Together, these relatively small domains—math, coding, writing, etc.—account for half of all AI usage. That’s fairly intuitive. All these fields primarily involve manipulating information, which is precisely what AI systems excel at. Meanwhile, jobs that require physical activity, like construction, or even simple physical presence, like healthcare support, show minimal AI adoption.

Source: Anthropic

Look at this graph for a better sense of things. There are many jobs that (at least in the United States) hire large numbers of workers, and yet, these jobs make for a negligible number of the conversations that Claude has. And then, there are a few outliers, which hire a minuscule portion of American workers but absolutely dominate AI usage.

Source: Anthropic

2. AI will hit those that are rich, but not rich enough

The study reveals an unexpected "sweet spot" for AI adoption.

When you compare how much people earn with how much they use AI, a weird pattern emerges. AI use is concentrated in the top 25% of the American workforce. The sorts of jobs that work well with AI are those that are reasonably difficult, requiring a 4-year bachelor’s degree — which pays in the range of $75,000-$125,000. This includes software developers, programmers, and the like.

But here's what's particularly interesting: both very high-wage positions (like medical super-specialists) and low-wage jobs (like food service workers) use AI surprisingly rarely. This is probably a result of two very different things at play: technical limitations and practical barriers. Highly regulated fields like medicine face technical restrictions on AI adoption. AI also isn’t at a place where it can replace genuine expertise. Meanwhile, many lower-wage jobs involve physical tasks that AI simply can't perform.

Source: Anthropic

3. Do people give work to AI, or do they work alongside it?

There are two ways people can use AI. One: they can automate tasks — trusting AI to do something entirely. Two: they can use AI to augment human capabilities.

Here’s the surprising bit. Most people don’t automate tasks. In fact, the majority of AI use — 57% — involves augmenting what humans do anyway. People tend to converse with AI — iterating on designs, refining documents together, or actively exploring ideas, rather than simply delegating tasks. In the 43% of cases when automation does occur, it tends to be for fairly routine well-defined tasks, like formatting documents or debugging specific coding errors.

Source: Anthropic

4. Will AI take all of your jobs?

Here’s the big question: will AI take your job? While the study doesn’t answer this question, here’s a trend to look at: AI has reached most jobs, but there are very few jobs where all tasks can be automated.

A vast majority of jobs have been touched by AI. In ~57% of jobs, at least 10% of tasks have been taken up by AI. Only a tiny fraction — about 4% — of jobs use AI for more than 75% of their tasks. Most jobs use AI a lot less. Even among heavy AI users, it tends to supplement rather than dominate their work. In 36% of jobs — the AI-power users — only a quarter of tasks are sent to AI.

The upshot is that we’re unlikely to see people’s work comprehensively being replaced anytime soon. At least so far, it’s much more likely that people will simply integrate AI into their work.

Source: Anthropic

5. What can AI not do?

Finally, the study talks about what AI can and can't do well.

There are a few tasks where it really excels — like critical thinking, reading comprehension, writing, or programming. But then there are physical skills, managerial skills, or social skills, where AI usage drops dramatically. This is fairly obvious, once again. It aligns with what we know about AI and what it can do. But once again, there’s now clear evidence for this.

Source: Anthropic

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The CCI is trying really hard

The Competition Commission of India (CCI) has introduced a new draft regulation, boldly claiming that it will transform India's competitive landscape for good. But before we get into the details of this new regulation, let’s set the stage.

The Competition Commission of India (CCI) is the nation’s antitrust authority, tasked with keeping markets fair, competitive, and efficient by preventing monopolistic practices that hurt consumers and smaller businesses. Economic theory often references perfect competition—a scenario where numerous firms operate efficiently, consumers have all the information they need, and prices perfectly reflect costs. It’s a nice idea, but the reality is far more complicated.

Nobel prize winner Friedrich A. Hayek, in his paper titled “The Meaning of Competition” argued that perfect competition has "no claim to be called 'competition'" at all.

In practice, firms don’t always compete on merit alone. Large companies can manipulate prices, buy out rivals, or exploit regulatory loopholes because they have the resources to do so. This is where the CCI steps in, especially when it comes to abuse of dominance—when a powerful firm exploits its position in a way that undermines competition and distorts the market.

Dominance itself is not wrong, abusing it is

To act on anti-competitive behavior, the CCI first has to determine if a firm is dominant, which depends on factors like market share, financial muscle, vertical integration, supply chain access, and overall industry influence. Simply being dominant, however, isn’t illegal: a firm might grow big through efficiency, innovation, or superior service. Problems start when that dominance is weaponized to eliminate competitors unfairly.

Dominance can be exploited in multiple ways, both at the cost of consumers and the market as a whole.

For instance, when a group of companies comes together to form a cartel and rig prices in a way that exploits helpless customers but profits the players, it is a clear case of consumer exploitation. This is relatively easier to address legally. But there is another side: sometimes the market players themselves are exploited, even though the customer appears to benefit. Predatory pricing is one such example, where lower prices may help customers but hurt competitors. This is much harder to resolve.

Predatory Pricing: How It Works and Why It Looks Dangerous

Predatory pricing sounds simple but can be devastating. When a firm sells below cost, it forces rivals—especially smaller ones—into a corner. Unable to sustain the same losses, these competitors eventually fold. After that, the predator can hike prices, recover its losses, and often make consumers pay more over time.

A classic example is Amazon’s clash with Diapers.com (Quidsi) in the early 2010s. Amazon sold diapers at a loss to capture market share. Diapers.com, a budding e-commerce player, couldn’t match the relentless price war. Amazon ended up acquiring Quidsi, and after some time, diaper prices rose. This case clearly showed how selling below cost can force out competitors and pave the way for lasting dominance.

Why CCI’s New Regulation Exists

Regulators everywhere find it tough to detect and prove predatory pricing, and India is no exception. The CCI’s new draft regulation offers a clearer definition of cost to determine if a company is selling below it on purpose. The regulation outlines different cost measures, such as:

• Average Variable Cost (AVC) – The typical yardstick, where pricing below AVC strongly indicates predatory pricing.

• Average Total Cost (ATC) – A broader measure is sometimes used if AVC doesn’t suffice.

• Long-Run Average Incremental Cost (LRAIC) – A forward-looking approach useful in industries with high capital needs.

• Average Avoidable Cost (AAC) – Reflects costs that could have been avoided if the product hadn’t been sold.

By clarifying these cost definitions, the CCI intends to make it simpler to see if a firm is unfairly undercutting competitors.

But Can We Really Police Predatory Pricing?

While the CCI’s effort is praiseworthy, proving predatory pricing still isn’t easy. Here’s why:

1. Cost Determination is Subjective
   Different cost measures can yield different outcomes. Which one is appropriate? A firm might claim its pricing is driven by long-term efficiencies rather than a deliberate plan to destroy competition. How can we know the truth?
   

2. Intent is Hard to Prove
   Companies engaged in predatory pricing never say outright, “We’re selling below cost to kill rivals.” They usually describe it as part of a broader strategy—“customer acquisition,” “market penetration,” or “limited-time discounts.” Regulators have to show intent, which is inherently subjective and open to legal disputes.
   

3. Market Dynamics are Complicated
   What might seem like predatory pricing could actually be tough but fair competition. Think about network effects: platforms like Uber or Zomato might keep fares or prices low to attract users and grow the network. Is that predatory pricing? Or is that a business strategy?
   

4. Recoupment is Theoretical
   For predatory pricing to work, the company has to recoup its losses by raising prices after rivals exit. But in many sectors, it’s easy for new competitors to appear when prices rise, making long-term recoupment uncertain.

Academic Debates: Does Predatory Pricing Even Work?

Some economists argue that predatory pricing is just a theoretical concept. Their reasoning:

• The predator loses more money than its competitors, which isn’t financially sustainable.

• The threat of predatory pricing isn’t really convincing, because new players can re-enter the market if prices climb too high.

• Tech markets function differently—low pricing might reflect genuine efficiency rather than malicious intent.

• Low prices typically benefit consumers in the short term, so interfering could backfire.

• Digital pricing, algorithmic competition, and shifting business models often outpace traditional antitrust tools.

So, Will the CCI’s New Rule Change Anything?

The CCI’s draft rule tries to formalize the process of investigating predatory pricing, but it probably won’t lead to sweeping changes, because:

• Big companies can still rationalize their pricing as vigorous but legitimate competition.

• Proving intent is extremely difficult, and courts tend to be cautious about intervening in price decisions.

• In a digital-driven world, older notions of predatory pricing may not apply as neatly.

In essence, while the CCI’s initiative is well-meaning, it’s unlikely to transform market behavior. If anything, it adds another layer of paperwork to an already convoluted regulatory environment. Companies will keep testing the limits, and regulators will keep trying to catch up.

So, while the CCI’s move may be “cute,” don’t expect it to be a game-changer.

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Tidbits

1. Tata Capital is set to raise ₹15,000 crore through green bonds and non-convertible debentures (NCDs), with ₹10,000 crore earmarked for green bonds, market-linked NCDs on a private placement basis and ₹5,000 crore via secured redeemable NCDs. This comes as the company prepares for its mandatory IPO by September 2024, following RBI’s Upper Layer NBFC classification. The funds will strengthen its capital base, expand lending in renewable energy and infrastructure, and enhance its credit profile. Additionally, Tata Capital’s merger with Tata Motors Finance is underway, streamlining its financial services business. This strategic move ensures liquidity, aligns with sustainability goals, and sets the stage for one of the biggest NBFC listings in the coming months.

2. State-owned NALCO has announced a ₹30,000 crore capex plan by FY30, its largest in recent years. The investment includes ₹17,000 crores for a 0.5 mtpa aluminum smelter and ₹13,000 crores for a 1,200 MW captive power JV with NTPC, with 25-30% green energy. The company, currently zero-debt, will adopt a 70:30 debt-equity ratio to fund the project. FY25 capex stands at ₹2,000 crore, with ₹1,700 crore for alumina refinery expansion, targeting 700-800 kt output by FY27. While incremental earnings from downstream products are expected, execution risks and cost escalations remain concerns. Analysts highlight long-term production growth but warn of near-term price corrections and regulatory risks.

3. NTPC plans to build 30 GW of nuclear power capacity over the next two decades, tripling its initial 10 GW target after the government opened the sector to private and foreign investment. The expansion, requiring a $62 billion investment, aligns with India’s goal of 100 GW nuclear capacity by 2047 and 500 GW non-fossil fuel power by 2030. Currently, India’s nuclear capacity stands at 8 GW, solely operated by NPCIL, which aims to increase it to 20 GW by 2032. NTPC is securing land across eight states and advancing partnerships with EDF, GE, and Holtec. Meanwhile, the government has allocated ₹200 billion ($2.3 billion) for SMR development, with at least five operational by 2033. This push could significantly reduce India’s coal reliance, stabilize long-term electricity costs, and create new jobs and investments, reshaping the country’s energy future.

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- This edition of the newsletter was written by Pranav and Kashish

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