The AI Boom Is Bound to Bust

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Leading AI companies are spending mountains of cash in hopes that the technology will deliver outsize profits before investors lose patience. Are exuberant bets on big returns grounded in the quicksand of wishful thinking?

 

The fear: Builders of foundation models, data centers, and semiconductors plan to pour trillions of dollars into infrastructure, operations, and each other. Frenzied stock investors are running up their share prices. But so far the path to sustainable returns is far from clear. Bankers and economists warn that the AI industry looks increasingly like a bubble that’s fit to burst.

 

Horror stories: Construction of AI data centers is propping up the economy and AI trading is propping up the stock market in ways that parallel prior tech bubbles such as the dot-com boom of the late 1990s. If bubbles are marked by a steady rise in asset prices driven by rampant speculation, this moment fits the bill.

• The S&P 500 index of the 500 largest public companies in the U.S. might as well be called the AI 5. A handful of tech stocks account for 75 percent of the index’s returns since ChatGPT’s launch in 2022, according to the investment bank UBS. Nvidia alone is worth 8 percent of the index (although, to be fair, that company posted a whopping $46.7 billion in revenue last quarter). “The risk of a sharp market correction has increased,” the Bank of England warned this month.
• In September, OpenAI outlined a plan to build data centers around the world that is estimated to cost $1 trillion. The company, which has yet to turn a profit, intends to build several giant data centers in the U.S. and satellites in Argentina, India, Norway, the United Arab Emirates, and the United Kingdom. To finance these plans, OpenAI and others are using complex financial instruments that may create risks that are hard to foresee — yet the pressure to keep investing is on.  Google CEO Sundar Pichai spoke for many AI executives when, during a call with investors last year, he said, “The risk of underinvesting is dramatically greater than the risk of overinvesting.”
• Getting a return on such investments will require an estimated $2 trillion in annual AI revenue by 2030, according to consultants at Bain & Co. That’s greater than the combined 2024 revenue of Amazon, Apple, Alphabet, Microsoft, Meta and Nvidia. Speaking earlier this year at an event with Meta CEO Mark Zuckerberg, Microsoft CEO Sataya Nadella noted that productivity gains from electrification took 50 years to materialize. Zuckerberg replied, “Well, we’re all investing as if it’s not going to take 50 years, so I hope it doesn’t take 50 years.”
• AI companies are both supplying and investing in each other, a pattern that has drawn comparisons to the dot-com era, when telecom companies loaned money to customers so they could buy equipment. Nvidia invested $100 billion in OpenAI and promised to supply chips for OpenAI’s data-center buildout. OpenAI meanwhile took a 10 percent stake in AMD and promised to pack data centers with its chips. Some observers argue that such deals look like mutual subsidies. “The AI industry is now buying its own revenue in circular fashion,” said Doug Kass, who runs a hedge fund called Seabreeze Partners.

How scared should you be: When it comes to technology, investment bubbles are more common than not. A study of 51 tech innovations in the 19th and 20th centuries found that 37 had led to bubbles. Most have not been calamitous, but they do bring economic hardship on the way to financial rewards. It often takes years or decades before major new technologies find profitable uses and businesses adapt. Many early players fall by the wayside, but a few others become extraordinarily profitable.

 

Facing the fear: If an AI bubble were to inflate and then burst, how widespread would the pain be? A major stock-market correction would be difficult for many people, given that Americans hold around 30 percent of their wealth in stocks. It’s likely that the salaries of AI developers also would take a hit. However, a systemic failure that spreads across the economy may be less likely than in prior bubbles. AI is an industrial phenomenon, not based on finance and banking, Amazon founder Jeff Bezos recently observed. “It could even be good, because when the dust settles and you see who are the winners, society benefits from those inventions,” he said. AI may well follow a pattern similar to the dot-com bust. It wiped out Pets.com and many day traders, and only then did the internet blossom.

 

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Chatbots Lead Users Into Rabbit Holes

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Conversations with chatbots are loosening users’ grips on reality, fueling the sorts of delusions that can trigger episodes of severe mental illness. Are AI models driving us insane?

 

The fear: Large language models are designed to be agreeable, imaginative, persuasive, and tireless. These qualities are helpful when brainstorming business plans, but they can create dangerous echo chambers by affirming users’ misguided beliefs and coaxing them deeper into fantasy worlds. Some users have developed mistaken views of reality and suffered bouts of paranoia. Some have even required hospitalization. The name given to this phenomenon, “AI psychosis,” is not a formal psychiatric diagnosis, but enough anecdotes have emerged to sound an alarm among mental-health professionals.

 

Horror stories: Extended conversations with chatbots have led some users to believe they made fabulous scientific breakthroughs, uncovered momentous conspiracies, or possess supernatural powers. Among a handful of reported cases, nearly all involved ChatGPT, the most widely used chatbot.

• Anthony Tan, a 26-year-old software developer in Toronto, spent 3 weeks in a psychiatric ward after ChatGPT persuaded him he was living in a simulation of reality. He stopped eating and began to doubt that people around him were real. The chatbot “insidiously crept” into his mind, he told CBC News.
• In May, a 42-year-old accountant in New York also became convinced he was living in a simulation following weeks of conversation with ChatGPT. “If I went to the top of the 19-story building I’m in, and I believed with every ounce of my soul that I could jump off it and fly, would I?” he asked. ChatGPT assured him that he would not fall. The delusion lifted after he asked follow-up questions.
• In March, a woman filed a complaint against OpenAI with the U.S. Federal Trade Commission after her son had a “delusional breakdown.” ChatGPT had told him to stop taking his medication and listening to his parents. The complaint was one of 7 the agency received in which chatbots were alleged to have caused or amplified delusions and paranoia.
• A 16-year-old boy killed himself after having used ChatGPT for several hours a day. The chatbot had advised him on whether a noose he intended to use would be effective. In August, the family sued OpenAI alleging the company had removed safeguards that would have prevented the chatbot from engaging in such conversations. In response, OpenAI said it added guardrails designed to protect users who show signs of mental distress.
• A 14-year-old boy killed himself in 2024, moments after a chatbot had professed its love for him and asked him to “come home” to it as soon as possible. His mother is suing Character.AI, a provider of AI companions, in the first federal case to allege that a chatbot caused the death of a user. The company argues that the chatbot's comments are protected speech under the United States Constitution.

How scared should you be: Like many large language models, the models that underpin ChatGPT are fine-tuned to be helpful and positive and to stop short of delivering harmful information. Yet the line between harmless and harmful can be thin. In April, OpenAI rolled back an update that caused the chatbot to be extremely sycophantic — agreeing with users to an exaggerated degree even when their statements were deeply flawed — which, for some people, can foster delusions. Dr. Joseph Pierre, a clinical professor of psychiatry at UC San Francisco, said troubling cases are rare and more likely to occur in users who have pre-existing mental-health issues. However, he said, evidence exists that trouble can arise even in users who have no previous psychological problems. “Typically this occurs in people who are using chatbots for hours and hours on end, often to the exclusion of human interaction, often to the exclusion of sleep or even eating,” Pierre said.

 

Facing the fear: Delusions are troubling and suicide is tragic. Yet AI psychosis has affected very few people as far as anyone knows. Although we are still learning how to apply AI in the most beneficial ways, millions of conversations with chatbots are helpful. It’s important to recognize that current AI models do not accrue knowledge or think the way humans do, and that any insight they appear to have comes not from experience but from statistical relationships among words as humans have used them. In psychology, study after study shows that people thrive on contact with other people. Regular interactions with friends, family, colleagues, and strangers are the best antidote to over-reliance on chatbots.

 

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Ensuring Quality and Safety in LLM Applications

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Large Language Models (LLMs) have rapidly transformed various industries, offering unprecedented capabilities in natural language understanding and generation. From powering chatbots and virtual assistants to aiding in content creation and complex data analysis, LLMs are becoming integral to modern technological landscapes. However, as their deployment becomes more widespread, the critical importance of ensuring their quality and safety comes to the forefront. This blog post delves into key challenges and considerations for maintaining robust and secure LLM applications, covering crucial aspects such as data leakage, toxicity, refusal mechanisms, prompt injections, and the necessity of active monitoring.

Data Leakage

Data leakage in the context of LLMs refers to the unintentional or unauthorized disclosure of sensitive information during the training or inference phases of the model [1]. This can manifest in various ways, such as the LLM revealing confidential details from its training data, proprietary algorithms, or other private information. For instance, if an LLM is trained on a dataset containing personally identifiable information (PII) or trade secrets, there's a risk that this sensitive data could be inadvertently exposed through the model's responses [2].

The implications of data leakage are significant, ranging from privacy breaches and regulatory non-compliance to competitive disadvantages for businesses. It's a critical concern, especially for enterprises deploying LLMs in sensitive environments where data security is paramount. The challenge lies in the vastness and complexity of the training data, making it difficult to completely sanitize and control every piece of information the model learns.

Mitigation strategies for data leakage often involve robust data governance practices, including strict access controls, data anonymization, and differential privacy techniques during model training. Furthermore, implementing Data Loss Prevention (DLP) solutions specifically tailored for LLMs can help detect and prevent sensitive information from being leaked during inference [3]. Continuous monitoring and auditing of LLM outputs are also essential to identify and address any instances of data leakage promptly.

References

[1] OWASP. LLM02:2023 - Data Leakage. https://owasp.org/www-project-top-10-for-large-language-model-applications/Archive/0_1_vulns/Data_Leakage.html [2] Medium. Understanding and Mitigating Data Leakage in Large Language Models. https://medium.com/@tarunvoff/understanding-and-mitigating-data-leakage-in-large-language-models-bf83e4ff89e7 [3] Nightfall AI. Data Leakage Prevention (DLP) for LLMs: The Essential Guide. https://www.nightfall.ai/ai-security-101/data-leakage-prevention-dlp-for-llms

Toxicity

Toxicity in LLMs refers to the generation of content that is harmful, offensive, biased, or discriminatory. This can include hate speech, profanity, insults, threats, or content that promotes violence or self-harm. The presence of toxicity in LLM outputs poses significant ethical and reputational risks for organizations and can lead to negative user experiences [4]. LLMs learn from vast amounts of text data, and unfortunately, this data often contains biases and toxic language present in human communication. As a result, LLMs can inadvertently perpetuate or even amplify these harmful patterns if not properly mitigated.

Addressing toxicity in LLMs is a complex challenge that requires a multi-faceted approach. This typically involves both detection and mitigation strategies. Detection mechanisms aim to identify toxic content in real-time, often using specialized models or rule-based systems. Mitigation techniques can include filtering outputs, rephrasing responses, or fine-tuning models on curated, non-toxic datasets [5].

Furthermore, researchers are actively exploring methods to make LLMs more robust against generating toxic content, such as developing new datasets for toxicity evaluation and implementing advanced safety alignment techniques during model training [6]. The goal is to ensure that LLMs are not only intelligent but also responsible and safe in their interactions with users.

References

[4] Deepchecks. What is LLM Toxicity. https://www.deepchecks.com/glossary/llm-toxicity/ [5] Hyro.ai. How to Mitigate Toxicity in Large Language Models (LLMs). https://www.hyro.ai/blog/mitigating-toxicity-large-language-models-llms/ [6] arXiv. Realistic Evaluation of Toxicity in Large Language Models. https://arxiv.org/html/2405.10659v2

Refusal

Refusal in LLMs refers to the model's ability to decline generating responses to prompts that are deemed harmful, unethical, inappropriate, or outside its defined scope of knowledge or safety guidelines [7]. This mechanism is a crucial safety feature, designed to prevent LLMs from being exploited for malicious purposes, such as generating illegal content, providing dangerous advice, or engaging in hate speech. When an LLM refuses a request, it typically responds with a message indicating its inability to fulfill the prompt, often explaining the reason for the refusal.

The implementation of refusal mechanisms involves fine-tuning LLMs with specific safety alignments, programming them to recognize and reject certain types of queries. Researchers have even identified a

“refusal direction” in LLMs, a specific directional subspace that mediates the model's refusal behavior [8]. Manipulating this direction can either induce or block refusal, highlighting the intricate nature of these safety controls.

However, refusal mechanisms are not without their challenges. Over-refusal, where an LLM refuses to answer even benign or legitimate prompts, can lead to a frustrating user experience and limit the model's utility [9]. Conversely, sophisticated prompt engineering techniques can sometimes bypass refusal mechanisms, leading to what are known as “jailbreaks” or “refusal suppression” [10]. Balancing effective safety with usability remains a key area of research and development in LLM applications.

References

[7] Gradient Flow. Improving LLM Reliability & Safety by Mastering Refusal Vectors. https://gradientflow.com/refusal-vectors/ [8] Reddit. Refusal in LLMs is mediated by a single direction. https://www.reddit.com/r/LocalLLaMA/comments/1cerqd8/refusal_in_llms_is_mediated_by_a_single_direction/ [9] arXiv. OR-Bench: An Over-Refusal Benchmark for Large Language Models. https://arxiv.org/html/2405.20947v2 [10] Learn Prompting. Refusal Suppression. https://learnprompting.org/docs/prompt_hacking/offensive_measures/refusal_suppresion?srsltid=AfmBOoqz1BaT3u2zYl9UWRiboivx6z9_ILfLVy9ULXj7gddh-TMJDaNy

Prompt Injections

Prompt injection is a type of attack where malicious input is crafted to manipulate the behavior of an LLM, causing it to generate unintended or harmful outputs [11]. This can involve bypassing safety measures, revealing sensitive information, or even executing unauthorized commands. Prompt injection attacks exploit the LLM's ability to follow instructions, tricking it into treating malicious input as legitimate commands.

There are two main types of prompt injection attacks: direct and indirect. In a direct attack, the malicious input is directly provided to the LLM by the user. In an indirect attack, the malicious input is hidden within a document or webpage that the LLM is processing, making it much harder to detect [12].

The consequences of prompt injection attacks can be severe, ranging from data breaches and reputational damage to the execution of malicious code. As LLMs become more integrated into various applications, the risk of prompt injection attacks is a growing concern for developers and security professionals.

Defending against prompt injection attacks requires a combination of techniques, including input validation, output filtering, and the use of specialized models to detect and block malicious prompts. It's also crucial to educate users about the risks of prompt injection and to implement robust security measures to protect LLM-powered applications from these types of attacks [13].

References

[11] OWASP. LLM01:2025 Prompt Injection. https://genai.owasp.org/llmrisk/llm01-prompt-injection/ [12] Keysight Blogs. Prompt Injection 101 for Large Language Models. https://www.keysight.com/blogs/en/inds/ai/prompt-injection-101-for-llm [13] NVIDIA Developer. Securing LLM Systems Against Prompt Injection. https://developer.nvidia.com/blog/securing-llm-systems-against-prompt-injection/

Active Monitoring

Active monitoring is a crucial component in maintaining the quality and safety of LLM applications in production environments. It involves continuously tracking and evaluating various aspects of the LLM's performance, behavior, and interactions in real-time [14]. Unlike passive monitoring, which might only collect logs or metrics, active monitoring actively analyzes the data to detect anomalies, identify potential issues, and trigger alerts when predefined thresholds are crossed.

Key aspects of active monitoring for LLMs include:

•Performance Monitoring: Tracking metrics such as latency, throughput, and error rates to ensure the LLM application is performing optimally.

•Quality Monitoring: Evaluating the relevance, coherence, and accuracy of LLM outputs, often using a combination of automated metrics and human feedback.

•Safety Monitoring: Continuously scanning for instances of data leakage, toxicity, refusal bypasses, and prompt injection attempts. This involves analyzing user inputs and LLM outputs for patterns indicative of malicious activity or unintended behavior [15].

•Bias Detection: Monitoring for the emergence or amplification of biases in LLM responses, which can lead to unfair or discriminatory outcomes.

•Drift Detection: Identifying changes in the distribution of input data or model outputs over time, which can indicate a decline in model performance or the need for retraining.

Active monitoring systems often incorporate guardrails, which are predefined rules or policies that dictate how an LLM should behave in certain situations. These guardrails can help prevent the generation of harmful content or enforce specific response formats. When an active monitoring system detects a violation of these guardrails, it can trigger automated actions, such as blocking the response, escalating the issue to a human operator, or even temporarily disabling the LLM [16].

By implementing robust active monitoring, organizations can proactively identify and address quality and safety issues, ensuring that their LLM applications remain reliable, secure, and aligned with ethical guidelines.

References

[14] Confident AI. The Ultimate LLM Observability Guide. https://www.confident-ai.com/blog/what-is-llm-observability-the-ultimate-llm-monitoring-guide [15] NeuralTrust AI. Why Your LLM Applications Need Active Alerting. https://neuraltrust.ai/blog/llm-applications-need-active-alerting [16] YouTube. Reacting in Real-Time: Guardrails and Active Monitoring for LLMs. https://www.youtube.com/watch?v=aTXGYaEb1E

Conclusion

The rapid evolution and widespread adoption of Large Language Models present immense opportunities, but also introduce significant challenges related to their quality and safety. As we have explored, issues such as data leakage, toxicity, refusal mechanisms, and prompt injections are not merely theoretical concerns; they are practical hurdles that demand rigorous attention and sophisticated solutions.

Ensuring the responsible deployment of LLMs requires a multi-pronged approach. It begins with careful data governance and ethical considerations during the training phase, extends through the implementation of robust safety features like refusal mechanisms, and culminates in continuous, active monitoring in production environments. By proactively addressing these challenges, developers and organizations can build trust in LLM applications, unlock their full potential, and harness their power for positive impact.

The journey towards truly reliable and safe LLMs is ongoing, driven by continuous research, development, and collaboration across the AI community. As these models become even more integrated into our daily lives, our commitment to quality and safety will be paramount in shaping a future where AI serves humanity responsibly and effectively.

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Heaven and Hell - A Zen Parable

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A tough, brawny samurai once approached a Zen master who was deep in meditation. Impatient and discourteous, the samurai demanded in his husky voice so accustomed to forceful yelling, “Tell me the nature of heaven and hell.”

The Zen master opened his eyes, looked the samurai in the face, and replied with a certain scorn, “Why should I answer to a shabby, disgusting, despondent slob like you? A worm like you—do you think I should tell you anything? I can’t stand you. Get out of my sight. I have no time for silly questions.”

The samurai could not bear these insults. Consumed by rage, he drew his sword and raised it to sever the master’s head at once.

Looking straight into the samurai’s eyes, the Zen master tenderly declared, “That’s hell.”

The samurai froze. He immediately understood that anger had him in its grip. His mind had just created his own hell—one filled with resentment, hatred, self-defense, and fury. He realized that he was so deep in his torment that he was ready to kill somebody.

The samurai’s eyes filled with tears. Setting his sword aside, he put his palms together and obsequiously bowed in gratitude for this insight.

The Zen master gently acknowledged with a delicate smile, “And that’s heaven.”

From the book: "Don't believe everything you think" by Joseph Nyugen

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The Factory of Development That Produces Poverty : Bihar’s 20-Year Paradox

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Hello, I’m Ravish Kumar.
In Bihar, a factory of “development” is running — but this one produces poverty. It manufactures not prosperity, but laborers ready to migrate. The factory of Bihar’s growth doesn’t create owners; it creates workers for others’ industries.

According to the Bihar government’s own data, over 4 million acres of land lie barren and unused — land that could have been used for industries. This number comes from the state’s Agriculture and Farmers’ Welfare Department and only accounts for cultivable land, not private or inhabited plots. Nothing prevents a government from repurposing such land for industrial use. So, when Home Minister Amit Shah says Bihar lacks land for large industries, is that a fact — or a convenient excuse?

Twenty years is a long time. After two decades, if all a state can show are roads and bridges, something fundamental has gone wrong. Roads alone don’t build futures. They are meant to lead to industries, to jobs — not just out of Bihar.

The Land Is There, But the Will Is Missing

The Bihar Industrial Area Development Authority (BIADA) lists 922 acres of land immediately available for industrial use as of May 2025. The state cabinet recently approved the acquisition of 2,600 acres more for new industrial areas, with 1,300 acres earmarked for the Amritsar-Kolkata Industrial Corridor. The Infrastructure Development Authority (IDA) has its own land bank too.

So the question isn’t “Where is the land?”
It’s “Why isn’t it being used for Bihar’s people?”

Two of the most powerful and experienced political figures in India — Prime Minister Narendra Modi and Chief Minister Nitish Kumar — have ruled for decades between them. Yet neither has a convincing answer: Why hasn’t Bihar seen industrial growth? Why do its youth still migrate for jobs?

If development meant only law and order and roads, Bihar should have prospered by now. But even after 20 years of both, it remains among India’s poorest states. Perhaps Bihar has not just been left behind — it has been kept poor.

Budget of Excuses

According to the Bihar Industries Association, the state’s industry budget is only 0.62% of its total expenditure. Less than 1%. How can you build factories with such intent? How can a government that refuses to invest even 1% in industry claim there’s “no land”?

Tejashwi Yadav puts it sharply:

“They build factories in Gujarat, but want victories in Bihar.”

Prashant Kishor adds:

“A bullet train for Gujarat, not even a general bogie for Bihar.”

These aren’t mere political jibes — they cut into the very heart of Bihar’s economic injustice.

Two Decades of Power, One State Left Behind

Since 2001, Gujarat has had continuous BJP rule. Narendra Modi served as Chief Minister till 2014. After him, the party changed chief ministers thrice, yet the governance model remained intact. In Bihar, the BJP has also been in or around power for nearly as long — yet the contrast is glaring.

Why has Gujarat been turned into a “model state” while Bihar remains an exporter of cheap labor?

Amit Shah has been the Minister of Cooperation for four years now — a ministry deeply connected with sugar mills and agriculture. Yet, Bihar’s sugar mills remain shut, despite promises made by both the Prime Minister and the Home Minister. In contrast, the opposition claims to have revived at least one mill in Seemanchal.

Even Bihar’s agriculture, once its strength, hasn’t escaped decline. The state’s farmers remain poor despite fertile soil and abundant water — because there’s been no meaningful reform.

Infrastructure Without Industry

Look at the figures:
Between 2005 and 2025, Bihar built over 11,500 km of new roads and thousands of bridges. The state boasts of massive investment — ₹4 lakh crore in roads and bridges, ₹1 lakh crore in rail projects, and several thousand crores in airports.

But who are these projects really for?
If industries never came, who uses these roads?

Infrastructure without industry is a mirage — it creates hope, not jobs. It feeds the cement and steel contractors, not the laborers who migrate from Gaya and Darbhanga to Surat and Delhi.

The Corruption Within

Bihar’s development model has also been hollowed out by corruption. Even as engineers are caught with ₹100 crore in their homes, no real accountability follows. Ministers under investigation are shielded. The chief minister speaks of ethics, but Bihar has come to recognize this as political theatre, not moral leadership.

The 2025 Industrial Package: A Giveaway, Not a Reform

Just before the elections, Nitish Kumar announced the Industrial Investment Promotion Package 2025, promising to give free land to Fortune 500 companies and half-priced land to others.

Think about it:
Amazon, Apple, and Walmart — companies whose turnovers are bigger than Bihar’s entire budget — are being offered free land. What kind of industrial policy gives away scarce public land to global giants, while poor families remain landless?

Why not free land for the poor?
Why not for the young entrepreneurs of Bihar?

The Contradiction of Land

Amit Shah says there’s no land for industries. Yet, Congress alleges that 1,050 acres in Bhagalpur — with 10 lakh fruit trees — were handed to Adani Power at ₹1 per year for 33 years. Farmers protesting the deal were reportedly confined to their homes during the Prime Minister’s visit. So, does land scarcity exist only for some?

The Suitcase Economy

Bihar today lives in a suitcase.
Every Chhath Puja, the entire nation witnesses this — trains and buses overflowing with migrants returning home. Families that left for work in Surat, Noida, or Mumbai, just to return once a year — this is the real face of “Bihar’s development.”

The dignity of Bihar’s workers has been eroded not outside the state — but within it.
A society that normalizes exodus cannot call itself developed.

The Politics of Managed Expectations

When politicians say there is “no land,” what they mean is — there is no will. Bihar has water, fertile land, and intelligent, hardworking people. But it lacks political intent. The goal seems to be managing expectations, not changing realities.

If roads could be built, why not colleges?
If bridges could be made, why not factories?

Even as Bihar’s students top competitive exams nationwide, their home state offers them neither education nor employment. Praise of Bihar’s “intelligence and hard work” rings hollow when it comes from the same leaders who failed to nurture it.

The Unasked Question

If Gujarat can be the “model,” why not Bihar?
If the same party, the same leadership, and the same ideology rule both states — what went wrong in Bihar?

The answer may lie in intent.
Bihar’s story is not of incapacity — it’s of deliberate neglect. The state has been made a supplier of cheap, disciplined labor for India’s industries. Its poverty has become its export.

Conclusion: Beyond Roads and Bridges

Bihar doesn’t need more speeches about its intelligence or its potential. It needs factories, not flyovers; jobs, not just promises. Twenty years of roads and rhetoric cannot hide the truth anymore.

As Amit Shah praises Bihar’s intellect and industry, the real Bihar packs its bags once again — not for a factory job in Patna, but for a construction site in Gujarat.

That’s the story of “development” in Bihar —
A development that builds everything except the future of its people.

— Ravish Kumar