Karnataka set to generate over Rs 10.8k crore from mining sector
Bengaluru: The Siddaramaiah-led Karnataka government on Friday approved two key measures that are expected to fetch the exchequer at least Rs 10,818 crore from the mining sector.
The Cabinet approved the Karnataka (Mineral Rights and Mineral Bearing Land) Tax Bill, which will enable the government to levy taxes, with retrospective effect, on mineral rights and owners of mineral-bearing lands. This move is estimated to fetch Rs 4,713 crore, as reported by the Deccan Herald.
Law and Parliamentary Affairs Minister H.K. Patil, while briefing the media, clarified that the tax would be in addition to the royalty already levied on mined minerals.
"At present, only the miner pays the royalty, but the landowner also has to pay the tax now. For one tonne of iron ore mined from land, a tax of Rs 100 will be levied on the owner of the land," Patil was quoted as saying by the publication, noting that the rate will vary depending on the type of mineral.
When asked whether the move was aimed at generating additional income for the state, Patil mentioned that they were collecting money that was due to the government.
The Cabinet's decision follows a recent Supreme Court ruling affirming that states have the legislative right to impose taxes on minerals.
The proposed tax rate will range from Rs 20 to Rs 100 per tonne for different minerals across all mine categories, the report added.
In another decision, the government has decided to give a one-time settlement (OTS) option for mining violators, particularly those who mined beyond their licensed areas. The state is expecting to collect penalties amounting to over Rs 6,105 crore under this.
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Beyond the Hype: Why Software Engineers and AI Are Partners
There is a lot of noise today about Artificial Intelligence. Everywhere you look, someone is saying AI will change the world. Some even say it will reduce the need for software engineers. If you run a business, lead a technology team, or simply follow tech news, this question may be on your mind: is AI really going to take over the work of human programmers?
Let us slow down and think calmly.
Yes, AI is growing very fast. Yes, it can now write computer code, create images, draft emails, and even design products. But at the same time, many claims about AI are exaggerated. Some companies are rushing to adopt AI in everything they do. Others are moving carefully, testing it step by step. Both approaches have logic behind them.
I come from the electronics industry, where we design and build the chips and computing systems that make AI possible. Ironically, to stay competitive, even we have to use Generative AI. Generative AI simply means AI that can create new content such as code, designs, layouts, or reports. It acts like a creative assistant. But there is a major difference between creating something quickly and creating something that is perfect.
In electronics, aerospace, medicine, automobiles, and finance, quality and safety standards are extremely strict. One small mistake can lead to a serious accident, financial loss, or even harm to a person. Imagine an AI system in a hospital giving one wrong suggestion for treatment. Imagine an AI error in an aircraft control system. The consequences can be severe. Because of this, no company in such sectors can blindly trust AI tools, no matter how advanced they appear.
The same reality applies to software engineering. AI can write code. But writing code is only one part of the job. Code must be tested, reviewed, secured, optimized, and made reliable. It must work not just today, but for years without failure. This level of responsibility cannot yet be handed over fully to a machine.
Let us look at what surveys say. A 2025 report by Stack Overflow, mentioned in Forbes, revealed that more than 80 percent of developers are already using AI tools. That sounds impressive. But the same report also showed that 66 percent of them often feel frustrated. Why? Because AI-generated code is almost correct, but not completely correct. It works halfway and then breaks in unexpected ways. In simple words, AI gives a good starting point, but not a finished product.
Earlier studies showed something similar. When AI tools were given simple coding tasks, they performed quite well. But when problems became complex, the AI struggled. It needed careful guidance from experienced engineers. Interestingly, the mistakes made by AI were often smaller and easier to fix compared to human mistakes. This means AI can be useful as a first draft writer. But just like an essay draft, someone must review and polish it before final submission.
This brings us to an important truth. AI can assist software engineers, but it cannot replace skilled professionals. A senior engineer must still check, correct, and finalize what AI produces. Responsibility still lies with humans.
Instead of seeing AI as a threat, it is better to see it as a smart helper. AI can suggest improvements, detect simple errors, and speed up repetitive tasks. It can save time. But timing and context matter. If AI interrupts at the wrong moment or gives too many wrong suggestions, it becomes distracting rather than helpful. Technology alone is not enough. We must also understand how people think and work.
A practical way to use AI is not to throw away existing systems, but to improve them. Many companies are adding AI features on top of their proven software instead of replacing everything. For example, adding a chat-based interface allows users to give simple written instructions instead of learning complicated commands. This makes powerful tools easier to use.
In industries like electronics, core design software is built on decades of human knowledge and testing. It would be unwise to discard such strong foundations. So companies are adding Generative AI as a support layer. You can see this approach in companies like Cadence, Synopsys, Siemens, Nvidia, Google, Microsoft, and Meta. They use AI to enhance their tools, not to start from zero. This keeps trust intact while still encouraging innovation.
In areas where accuracy is critical, AI is used carefully. For example, in quality testing, AI can quickly detect possible defects. But the final decision still depends on established, proven methods. On the other hand, in tasks like improving performance or reducing power consumption in large computing systems, small errors may be acceptable during experimentation. In such cases, AI can explore many design possibilities much faster than humans.
Another important change is how people interact with machines. Many powerful software tools are complicated and require months of training. AI-based interfaces allow users to give simple prompts in plain language. This can make technology more accessible. But easier access does not remove the need for expertise. In fact, experienced engineers become even more valuable. They guide AI tools, validate outputs, and ensure systems remain secure and reliable.
So, will AI take over software engineers? In my opinion, not anytime soon. Fully automatic software development without human involvement is still far away. Even today, AI-written code must be reviewed before being used in real systems.
For newcomers in programming, AI can act as a helpful guide. It can explain concepts, suggest solutions, and speed up learning. But beginners must be cautious. AI sometimes makes small but critical mistakes that are easy to miss without experience. Human judgment remains essential.
The smartest approach is balance. Add AI into existing systems instead of replacing them. Combine speed with supervision. Combine automation with accountability. This way, teams can innovate while still maintaining control and understanding.
Generative AI is a powerful tool. But like electricity or the internet, it becomes meaningful only when used responsibly. The future of technology is not humans versus AI. It is humans and AI working together, each doing what they do best, to build safer, smarter, and more reliable solutions for society.
(Girish Linganna is an award-winning science communicator and a Defence, Aerospace & Geopolitical Analyst. He is the Managing Director of ADD Engineering Components India Pvt. Ltd., a subsidiary of ADD Engineering GmbH, Germany)
Disclaimer: The views and opinions expressed in this article are solely those of the author. They do not necessarily reflect the views, policies, or position of the publication, its editors, or its management. The publication is not responsible for the accuracy of any information, statements, or opinions presented in this piece.
