Awez’s Journey: A Short but Emotional Ride

Social media sensation and dancer Awez Darbar entered the Bigg Boss house with a lot of hopes among fans. From the very beginning, he was seen as a person who had good energy, stayed detached from unnecessary drama, and tried to maintain real relationships with other contestants.

But ironically, that relaxed and cool attitude could have ultimately done him in in a reality show like Bigg Boss, where bluster, uncompromising views, and fight scenes are known to drive screen time and popularity among the public. In contrast to louder, more aggressive housemates, Awez appeared too withdrawn, “playing it safe,” or even “invisible” to segments of the audience.

The Eviction: What Led to It?

In eviction week, several contestants were nominated, among them people who had been involved in hot fight scenes or developed enormous fan bases during the weeks. Awez maintained himself and did not become negative, though he unfortunately never created much hype in the house.

As a result:

• He was given little screen time.
• He was not involved in strong friendships or rivalry.
• The public vote, who many of them hadn’t seen or heard much of him for quite a while now, may not have been inclined to do so at a large scale.

In the end, the public vote is largely presence and not personality — and Awez just did not have as much of that in the competitive cutthroat arena that is Bigg Boss.

His Exit: Graceful & Emotional

On eviction, Awez left the house with his head held high, recounting that despite it being a brief stay, it was introspective and reflective. He said that Bigg Boss enabled him to realize a new facet of his personality and learn how perception is constantly under 24/7 watch.

Following his eviction, he was showered with affection from other contestants and fans. Even inside the house, there were some contestants — more so Abhishek Bajaj — who were seen getting emotional about his eviction, a rare display of genuine human bonding in the otherwise cutthroat atmosphere.

Final Thoughts

So yes, Awez Darbar was voted out for low votes, but it does not mean he lost. In a series like Bigg Boss, where fun matters over integrity or finesse, his calming presence, emotional quotient, and positive vibes impressed — even if it failed to win the contest.

Sometimes it is advisable to leave a reality show with dignity rather than survive at the cost of your character.

 1. Artificial Intelligence & Automation

Topic: The rise of smart machines and decision-making systems

Why it matters:

• AI is moving from “cool tech demo” to business-critical infrastructure.

Every industry—healthcare, logistics, and more—are attempting to understand how they can use AI to save money, improve decision-making, or customize customer experiences.

Key winners:

• Semiconductors & hardware (e.g. Nvidia, AMD, TSMC)
• AI infrastructure & cloud platforms (Microsoft Azure, AWS, Google Cloud)
• AI software & services (enterprise AI tools, generative AI startups)

Human insight:

AI is no longer a buzzword—it’s becoming the productivity driver of the 21st century. Just like the internet in the 1990s. Expect this theme to take shape but last for decades.

2. Clean Energy & Climate Tech

Theme: Decarbonization of the global economy

Why it matters:

• Governments are spending trillions on green energy transitions.
• Climate change is now a political issue no longer—it’s a real business and risk management issue.
• Energy security has become a geopolitics, and it’s pushing nations towards renewables.

Big winners:

• Solar, wind, and hydrogen industries
• Battery tech / energy storage
• Carbon capture and smart grid infrastructure
• EV ecosystem (cars, charging, raw materials like lithium, cobalt)

Human insight:

This is a long game. These types of transitions will last decades, but the policy-backed momentum and demand-led momentum are now in place. Volatility will be there, but the trend is irreversible.

 3. Healthcare Innovation & Biotech

Theme: Personalized medicine, biotech innovation, and aging populations

Why it matters:

• The world population is aging quickly, especially in the West, Japan, and China.
• Medical technology is evolving faster than ever—CRISPR, mRNA, gene therapy, AI diagnostics.
• COVID accelerated biotech investment and shifted R&D timelines.

Main beneficiaries:

• Biotech firms with emerging therapies
• Pharma firms with strong R&D pipelines
• Health-tech startups focused on telemedicine, diagnostics, and wearable health

Human insight

With human life expectancy growing, healthcare will no longer be curing disease, but longevity and quality of life. In this space, innovation has tangible, emotional value for consumers, creating long-term investment prospects.

4. Digital Infrastructure & Cybersecurity

Theme: An increasingly interdependent, yet increasingly vulnerable digital world

Why it matters:

• The digital economy keeps growing—more data, more devices, more cloud.
• Cyber attacks are getting out of hand, and no business or government has immunity.
• Regulatory pressure is rising to shield consumer data.

Big winners:

• Cloud computing businesses
• Cybersecurity platforms (CrowdStrike, Palo Alto Networks, Zscaler, etc.)
• Data center REITs and fiber-optic network companies

Human insight:

Digital infrastructure is the pipes and roads of the new economy. You don’t always see it, but you depend on it. As reliance grows, so will the importance—and profitability—of protecting and expanding that infrastructure.

 5. Consumer Tech & Experience Economies

Theme: Digital-first, personalized lifestyles

Why it matters:

• Consumers, especially Gen Z and Millennials, value experiences more than material possessions.
• There is more emphasis on digital, on-demand, frictionless everything.
• AI is making personalization at scale possible.

Key beneficiaries:

• Streaming, gaming, and creator platforms
• Deeply personalized e-commerce
• Augmented/virtual reality (AR/VR) for next-generation experiences

Human insight:

It’s not just what people buy—it’s how they live, connect, and entertain. Companies that understand evolving lifestyles will dominate.

6. India and Emerging Markets

Theme: Global economic rebalancing

Why it matters:

• India will likely be the fastest-growing large economy in the decade ahead.
• Rising middle class, digital adoption, infrastructure growth.
• Emerging markets are decoupling from China and becoming more diversified.

Principal beneficiaries:

• Indian tech and banking
• Consumer and fintech plays
• Emerging market ETFs with a South Asia, Africa, and LatAm focus

Human insight:

The world is shifting away from a U.S.-centric unipolar economic model towards a more multipolar world. Sophisticated investors who understand the nuance of these economies—beyond the best-selling headlines—can create substantial alpha here.

7. Education, Reskilling & Human Capital

Topic: Continuous learning in an AI-powered world

Why it’s important:

• Traditional work roles are being transformed by AI.
• People will need to reskill, adapt, and learn continuously.
• The education sector is being disrupted through edtech, microlearning, and certifications.

Principal beneficiaries:

• EdTech platforms (Coursera, Duolingo, BYJU’S, etc.)
• Corporate learning platforms
• Vocational training / STEM-centric initiatives

Human insight:

The future belongs to the ones who adapt fastest. Companies that help people do that—through accessible, affordable education—have an expanding and sticky customer base.

What About Legacy Sectors?

Financials?

Still in it—especially with rising interest rates improving margins. But legacy banks have to catch up with fintech innovation and regtech.

Industrials & Infrastructure

Yes, especially if they are connected with clean energy, defense, automation, or public-private partnerships in the new world.

Real Estate?

Selective bets (e.g., data centers, logistics, senior housing) could perform better, but traditional commercial real estate lags in a hybrid workplace.

Last Thought

“Themes come and go, but megatrends change everything.”

The above-discussed industries aren’t trends—they’re tied to fundamental global shifts in how we:

• Power the world
• Heal and extend human life
• Communicate and safeguard data
• Educate ourselves
• Consume and invest

First, What Are Long-Term Interest Rates?

Long-term interest rates—such as the yield on the 10-year U.S. Treasury bond—measure the price of borrowing money for extended periods of time. They’re typically shaped by:

• Expectations of inflation
• Central bank actions (such as Fed rate decisions)
• Government debt issuance
• World economic outlook

And whereas short-term rates are directly related to central bank actions (such as the Fed Funds Rate), long-term rates capture what investors believe about the future: growth, inflation, and risk.

Why Do Long-Term Rates Matter to Growth/Tech Stocks?

Let’s begin with a investing fundamentals rule of thumb:

• The value of a stock is the present value of its future cash flows.
• Here’s where higher rates enter the picture:
• As interest rates rise, future cash flows are discounted more and more.
• That is, those future profits are less valuable today.

And growth/tech stocks—many of which have huge profits years from now—take the biggest hit.

So when long-term rates increase, the math of valuation begins to work against such companies.

Why Are Tech and Growth Stocks Particularly Sensitive?

1. They’re Priced for the Future

Most growth stocks—picture companies like Tesla, Amazon, Nvidia, or high-growth SaaS companies—invest huge amounts today in expectation of grand rewards down the line.

Their valuations are constructed on the premise that:

• They’ll continue growing fast for years to come.
• Profits in the future will support lofty prices today.

But when interest rates go up, those “big profits down the road” are discounted more, so their current value (and thus their stock price) is less.

2. They Tend to Depend on Inexpensive Capital

Startups and high-growth companies frequently borrow funds or issue equity to drive growth. Higher interest rates result in:

• Borrowing costs are higher.
• Venture capital disappears.
• Capitalists insist on profitability earlier.

This can compel companies to reduce expenses, postpone expansion, or increase prices, all of which can hamper growth.

Real-World Example: The 2022-2023 Tech Sell-Off

When inflation surged in 2022 and the Federal Reserve hiked interest rates aggressively, we witnessed:

• The 10-year Treasury yield jump sharply
• High-growth tech stocks tank, with many dropping 40–70% from peak

Investors switch into value stocks, dividend payers, and defensive sectors (such as energy, utilities, and healthcare)

It wasn’t that Meta, Shopify, and Zoom were doing poorly. It was that their future profits counted less in a higher-rate world.

But It’s Not All Bad News

1. Some Tech Companies Are Now Cash Machines

The big-cap tech giants—such as Apple, Microsoft, Alphabet—are now enormously profitable, cash-rich, and less dependent on borrowed cash. That makes them less sensitive to rate moves than smaller, still-rising tech names.

2. Rate Hikes Eventually Peak

When inflation levels off or the economy decelerates, central banks can stop or reverse rates, reducing pressure on growth stocks.

3. Innovation Can Outrun the Math

At times, the force of disruption is compelling enough to overcome increasing rates. For instance:

• The emergence of AI is allowing businesses to create efficiencies that fuel growth—even in an elevated-rate world.

Some tech infrastructure plays (such as Nvidia) can be treated as a utility, not a bet.

 What Should Investors Do?

Understand Your Exposure

Not all tech stocks are alike. A growthy, loss-making AI startup will act very differently from a cash-generation-rich enterprise software business.

Watch the Yield Curve

The slope of the yield curve (short term vs long term rates) will say a lot about what the market expects for growth and inflation. A steepening curve tends to be optimistic economically (favorable to cyclicals), but an inverted curve can portend issues down the road.

 Diversify by Style

An average portfolio could have both:

• Growth stocks (for long-term growth)
• Value/dividend-paying stocks (to provide cushions against rate shocks)

 The Bottom Line

Increasing long-term interest rates have the effect of gravity on growth stocks. The higher the rates, the greater the pull on valuations.

But this does not imply doom for tech. It means investors must:

• Recalibrate expectations
• Focus on quality
• And remember that not all tech grows in the same environment

Just as low rates fueled the rise of growth stocks over the past decade, higher rates are now reshaping the landscape. The companies that survive and adapt—those with real earnings, real products, and real cash flow—will come out stronger.

 The Timeless Problem with Learning Language

Language learning is intimate, but traditional testing just can’t manage that. Students are typically assessed by rigid, mass-produced methods: standardized testing, fill-in-the-blank, checklist-graded essays, etc. Feedback can be delayed for days, frequently in the form of generic comments like “Good job!” or “Elaborate on your points.” There’s little nuance. Little context. Little you engaged.

That’s where AI comes in—not to do the teachers’ job, but as a super-competent co-pilot.

 AI/LLMs Change the Game

1. Measuring Adapted Skills

• AI models can examine a learner’s language skills in real time, in listening, reading, writing, and even speech (if integrated with voice systems). For example:
• As a learner writes a paragraph, my LLM can pass judgment on grammar, vocabulary richness, coherence, tone, and argument strength.
• Instead of just giving a score, it can explain why a sentence may be unclear or how a certain word choice could be improved.
• Over time, the model can track the learner’s progress, detect plateaus, and suggest focused exercises.

It’s not just feedback—it’s insight.

2. Personalized Feedback in Natural Language

Instead of “Incorrect. Try again,” an AI can say:

“‘You’re giving ‘advices’ as a plural, but ‘advice’ is an uncountable noun in English. You can say ‘some advice’ or ‘a piece of advice.’ Don’t worry—this is a super common error.'”

This kind of friendly, particular, and human feedback promotes confidence, not nervousness. It’s immediate. It’s friendly. And it makes learners feel seen.

3. Shifting to Level of Proficiency and Learning Style

AI systems are able to adjust the level and tone of their feedback to meet the learner’s level:

• For beginning learners: shorter, more direct explanations; focus on basic grammar and sentence structure.
• For advanced learners: feedback might include stylistic remarks, rhetorical impact, tone modulations, and even cultural context.

It also has the ability to understand how the individual learns best: visually, by example, by analogy, or by step-by-step instructions. Think of receiving feedback described in the mode of a story or in the way of colored correction, depending on your preference.

4. Multilingual Feedback and Translation Support

For multilingual students or ESL, AI can specify errors in the student’s home language, compare the structures of different languages, and even flag “false friends” (i.e., words that are the same but have different meanings in two languages).

• “In Spanish, ’embarazada’ means pregnant—not embarrassed! Easy mix-up.”
• That’s the type of contextual foundation that makes feedback sticky.

5. Real-Time Conversational Practice

With the likes of voice input and chat interfaces, LLMs can practice real-life conversations:

• Job interview, travel scenario, or conversation practice course.
• Giving feedback on your pronunciation, tone, or idiomatic usage.
• Even role-reversal (e.g., “pretend that I were a traveler in Japan”) to get used to different contexts.

And the best part? No judgment. You can make mistakes without blushing.

6. Content Generation for Assessment

Teachers or students may ask AI to create custom exercises based on a provided topic or difficulty level: teaching

• Fill-in-blank exercises based on vocabulary from a recent lesson.
• Comprehension questions based on a passage the learner wrote.
• Essay prompts based on student interests (“Write about your favorite anime character in past tense.”)
• This makes assessment more engaging—and more significant.

 Why This Matters: Personalized Learning Is Powerful Learning

Language learning is not a straight line. Others struggle with verb conjugation, others with pronunciation or cultural uses of language. Others get speech-tongue-tied, others are grammar sticklers who can’t write a wonderful sentence.

LLMs are able to identify such patterns, retain preferences (with permission), and customize not only feedback, but the entire learning process. Picture having a tutor who daily adjusts to your changing needs, is on call 24/7, never gets fatigued, and pumps you up each step of the way.

That’s the magic of customized AI.

Of Course, It’s Not Perfect

• Come on, let’s be realistic—AI has its limits.
• It will sometimes fail to pick up subtleties of meaning or tone.
• Feedback at times was too pleasant, or not harsh.
• It also lacks cultural awareness or emotional intelligence in edge cases.

And let’s not forget the risk of students becoming too reliant on AI tools, instead of learning to think by themselves.

That’s why human teachers matter more than ever before. The optimal model is AI-assisted learning: teachers + AI, not teachers vs. AI.

What’s Next?

The future may bring:

• LLMs tracking a student’s work such as an electronic portfolio.
• AI with voice recognition utilized in the assessment of speaking fluency.
• AI grading lengthy essays with feedback that is written in a tone in which one would speak.

Even writing partners who help you co-author tales and revise and explain along the way.

 Final Thought

Personalized language assessment with LLMs isn’t a matter of time-saving or feedbackscaling—it’s a matter of giving the learner a sense of having been heard. Inspired. Empowered. When a student is informed, “I see what you’re attempting to say—here’s how to say it better,” that’s when real growth happens.

And if AI can make that experience more available, more equitable, and more inspiring for millions of learners across the globe—well, that’s a very good application of intelligence.

What Is “Safe AI at Scale” Even?

AI “safety” isn’t one thing — it’s a moving target made up of many overlapping concerns. In general, we can break it down to three layers:

1. Technical Safety

Making sure the AI:

• Doesn’t generate harmful or false content
• Doesn’t hallucinate, spread misinformation, or toxicity
• Respects data and privacy limits
• Sticks to its intended purpose

2. Social / Ethical Safety

Making sure the AI:

• Doesn’t reinforce bias, discrimination, or exclusion
• Respects cultural norms and values
• Can’t be easily hijacked for evil (e.g. scams, propaganda)
• Respects human rights and dignity

3. Systemic / Governance-Level Safety

Guaranteeing:

• AI systems are audited, accountable, and transparent
• Companies or governments won’t use AI to manipulate or control
• There are global standards for risk, fairness, and access
• People aren’t left behind while jobs, economies, and cultures transform

So when we ask, “Is it safe?”, we’re really asking:

Can something so versatile, strong, and enigmatic be controllable, just, and predictable — even when it’s everywhere?

Why Safety Is So Hard at Scale

• At a tiny scale — i.e., an AI in your phone that helps you schedule meetings — we can test it, limit it, and correct problems quite easily.
• But at scale — when millions or billions are wielding the AI in unpredictable ways, in various languages, in countries, with access to everything from education to nuclear weapons — all of this becomes more difficult.

Here’s why:

1. The AI is a black box

Current-day AI models (specifically large language models) are distinct from traditional software. You can’t see precisely how they “make a decision.” Their internal workings are of high dimensionality and largely incomprehensible. Therefore, even well-intentioned programmers can’t predict as much as they’d like about what is happening when the model is pushed to its extremes.

2. The world is unpredictable

No one can conceivably foresee every use (abuse) of an AI model. Criminals are creative. So are children, activists, advertisers, and pranksters. As usage expands, so does the array of edge cases — and many of them are not innocuous.

3. Cultural values aren’t universal

What’s “safe” in one culture can be offensive or even dangerous in another. A politically censoring AI based in the U.S., for example, might be deemed biased elsewhere in the world, or one trying to be inclusive in the West might be at odds with prevailing norms elsewhere. There is no single definition of “aligned values” globally.

4. Incentives aren’t always aligned

Many companies are racing to produce better-performance models earlier. Pressure to cut corners, beat the safety clock, or hide faults from scrutiny leads to mistakes. When secrecy and competition are present, safety suffers.

 How Do We Audit AI for Safety?

This is the meat of your question — not just “is it safe,” but “how can we be certain?

These are the main techniques being used or under development to audit AI models for safety:

1. Red Teaming

• Think about the prospect of hiring hackers to break into your system — but instead, for AI.
• “Red teams” try to get models to respond with something unsafe, biased, false, or otherwise objectionable.
• The goal is to identify edge cases before launch, and adjust training or responses accordingly.

Disadvantages:

• It’s backward-looking — you only learn what you’re testing for.
• It’s typically biased by who’s on the team (e.g. Western, English-speaking, tech-aware people).

Can’t test everything.

2. Automated Evaluations

• Some labs test tens of thousands or millions of examples against a model with formal tests to find bad behavior.
• These can look for hate speech, misinformation, jailbreaking, or bias.

Limitations:

• AI models evolve (or get updated) all the time — what’s “safe” today may not be tomorrow.
• Automated tests can miss subtle types of bias, manipulation, or misalignment.

3. Human Preference Feedback

• Humans rank outputs as to whether they’re useful, factual, or harmful.
• These rankings are used to fine-tune the model (e.g. in Reinforcement Learning from Human Feedback, or RLHF).

Constraints:

• Human feedback is expensive, slow, and noisy.
• Biases in who does the rating (i.e. political, cultural) could taint outcomes.
• Humans typically don’t agree on what’s safe or ethical.

4. Transparency Reports & Model Cards

• Some of these AI creators publish “model cards” with details about the training data, testing, and safety testing of the model.
• Similar to nutrition labels, they inform researchers and policymakers about what went into the model.

Limitations:

• Too frequently voluntary and incomplete.
• Don’t necessarily capture the look of actual-world harms.

5. Third-Party Audits

• Independent researchers or regulatory agencies can audit models — preferably with weight, data, and testing access.
• This is similar to how drug approvals or financial audits work.

Limitations:

• Few companies are happy to offer true access.
• There isn’t a single standard yet on what “passes” an AI audit.

6. “Constitutional” or Rule-Based AI

• Some models use fixed rules (e.g., “don’t harm,” “be honest,” “respect privacy”) as a basis for output.
• These “AI constitutions” are written with the intention of influencing behavior internally.

Limitations:

• Who writes the constitution?
• Can there be inimical principles?
• How do we ensure that they’re actually being followed?

What Would “Safe AI at Scale” Actually Look Like?

If we’re being a little optimistic — but also pragmatic — here’s what an actually safe, at-scale AI system might entail:

•  Strong red teaming with different cultural, linguistic, and ethical
• perspectives Regular independent audits with binding standards and consequences
•  Override protections for users so people can report, mark, or block bad actors
•  Open safety testing standards, such as car crash testing
•  AI capability-adaptable governance organizations (e.g. international bodies, treaty-based systems)
• Known failures, trade-offs, and deployment risks disclosed to the public
•  Cultural localization so AI systems reflect local values, not Silicon Valley defaults
• Monitoring and fail-safes in high-stakes domains (healthcare, law, elections, etc.)

But. Will It Ever Be Fully Safe?

No tech is ever 100% safe. Not cars, not pharmaceuticals, not the web. And neither is AI.

But this is what’s different: AI isn’t a tool — it’s a general-purpose cognitive machine that works with humans, society, and knowledge at scale. That makes it exponentially more powerful — and exponentially more difficult to control.

So no, we can’t make it “perfectly safe.

But we can make it quantifiably safer, more transparent, and more accountable — if we tackle safety not as a one-time checkbox but as a continuous social contract among developers, users, governments, and communities.

 Final Thoughts (Human to Human)

You’re not the only one if you feel uneasy about AI growing this fast. The scale, speed, and ambiguity of it all is head-spinning — especially because most of us never voted on its deployment.

But asking, “Can it be safe?” is the first step to making it safer.
Not perfect. Not harmless on all counts. But more regulated, more humane, and more responsive to true human needs.

And that’s not a technical project. That is a human one.