7 Practices for Mental Hygiene in an Unsteady World

All Buddhist Stories

I teach meditation across many countries, and everywhere I go, the questions sound the same:

“Why am I so anxious?”
“Why does everything feel overwhelming?”
“Why is my mind so sensitive these days?”

And it’s true—this generation is facing intense emotional turbulence. Unstable politics, climate anxiety, polarization, racism, and a constant stream of information have made our minds fragile. Resilience has quietly eroded. Panic attacks, depression, loneliness, low self-esteem… these are no longer rare. They’re common.

After many years of teaching and speaking with scientists and practitioners, I’ve found seven practices that consistently help. I call them mental hygiene—simple, everyday habits that keep the mind clear, resilient, and grounded.

Let’s explore them one by one.

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1. Aerobic Exercise: Move to Stabilize the Mind

The first and most powerful tool is simple: aerobic exercise.

Whenever I feel tired, restless, or mentally “speedy,” movement brings me back to balance. Exercise oxygenates the brain, releases stress from the body, and naturally lifts mood.

Of course, if you have heart conditions or health concerns, consult a doctor. But in general, movement is medicine.

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2. Sleep: The Most Underrated Healer

Sleep is critical, yet many people struggle with it.

A few practical tips:

• Try to sleep earlier.

• Avoid caffeine after 1 PM if you’re sensitive to it.

• Keep gadgets out of your bedroom—if the phone is near your pillow, your hand will find it.

• Make the room slightly cool.

• Before bed, relax your body from head to toe and feel the pull of gravity.

• And most importantly: don’t chase sleep. If you look for sleep, it runs away.

Some scientists say eight hours is best; others say six to seven. I personally sleep six hours, sometimes seven. Find what works for you.

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3. Food: What You Eat Shapes How You Feel

Healthy eating really does matter.

More vegetables, balanced meals, and less processed foods—simple choices that have profound effects on mood and energy.

I’m vegetarian. But whether you choose to be vegan, vegetarian, or otherwise, aim for freshness, balance, and mindfulness in your diet.

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4. Three Deep Breaths: Nature’s Built-In Reset Button

Notice what happens when you feel tired or stressed—you naturally sigh.
That deep breath is your body trying to heal itself.

The practice:

1. Inhale slowly through the nose.

2. Exhale gently.

3. Rest your mind and body in the space between breaths.

Just three deep breaths can shift your state. Later in the day, if you feel a bit better, do another three. Oxygen calms the nervous system and re-energizes the body.

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5. Meditation: Start with Sound

Meditation doesn’t have to be complicated.
One of the easiest methods for everyone is sound meditation.

• Lie down or sit comfortably.

• Listen to music without words, or natural sounds—wind, birds, flowing water.

• Let both the ear and the mind listen together.

This is meditation.
The essence is awareness.

With practice, you can meditate with any sound—even traffic. (Though I admit, a baby crying is still difficult!)

Do it while sitting, walking, or resting. Sound is a doorway to presence.

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6. Appreciation: Gratitude as a Daily Practice

Gratitude changes the brain. Literally.

Science talks about neurons, electric charges, rhythms. In Tibetan terms, we say Prana Bindu Nadi. The ideas are different; the effect is the same. Appreciation rewires the mind.

Start a journal and write down three things each day:

• Something about yourself

• Someone in your life

• Something about the world

Examples:

• “I’m alive—how wonderful!”

• “I can see, hear, smell, feel.”

• “This food reached my plate through countless hands—the farmers, the sellers, the cooks.”

Gratitude builds new pathways in the brain.
It transforms how you see reality.

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7. Being “Okay with Not Okay”: The Wisdom of Imperfection

We live with an “all or nothing” mindset—wanting 100% perfection or feeling worthless.

But mistakes, failures, and struggles are not your true nature.
At the fundamental level, you are already whole.

We say in Tibet:
“No mistake, no success.
Repeating the same mistake—no success.”

Failure is the mother of success.
Growth requires gentleness.
Forgive yourself.
Let the past be past.

Be here now.

Thoughts are just opinions—they are not you.
Let them come and go.

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A Final Word

These seven practices—exercise, sleep, healthy eating, deep breathing, meditation, appreciation, and embracing imperfection—create resilience. They protect your mental hygiene and support both body and mind.

In an unstable world, caring for your inner world is not optional.
It is essential.

Thank you for practicing.
Thank you for being here.

Tags: Buddhism,Video,

Model Alert... The Unseen Ambush -- How Grok 4.1 Quietly Stole the AI Spotlight

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If you blinked, you might have missed it. This week was supposed to belong to Google and the highly anticipated launch of Gemini 3. The tech world was poised, calendars marked, ready for another giant to make its move. But in a classic plot twist, xAI slipped in through the side door.

Overnight, Grok 4.1 rolled out—not with a thunderous press conference, but with a quiet update across grok.com, the X platform, and mobile apps. The moment users refreshed their model picker, two new options appeared: Grok 4.1 and Grok 4.1 "Thinking." The AI community, expecting one headline, was instantly consumed by another.

More Than Just Hype: The Numbers Behind the Update

Elon Musk promised "significant improvement in speed and quality," a claim we’ve become accustomed to hearing. This time, however, the data doesn't just support the claim—it shouts it. Instead of chasing raw computing power, xAI focused on the core challenges that plague large language models: speed, accuracy, and natural conversation.

The most staggering improvements lie in two key areas:

• Hallucination Rate: Dropped from 12.09% to 4.22%—an almost threefold reduction.

• Factual Errors: Fell from 9.89% to 2.97%.

For anyone familiar with AI, these figures are monumental. Reducing a model's tendency to "make things up" is a deeply complex problem tied to its fundamental architecture. A leap of this magnitude suggests a structural breakthrough, not a superficial tweak.

The Secret Sauce: A Model That Supervises Itself

So, how did they do it? According to xAI, the upgrade stems from a sophisticated reinforcement learning infrastructure powered by a new reward model system. In simple terms, Grok 4.1 uses a "cutting-edge inference model" to act as its own judge and jury.

This approach of models training models is a significant step the industry has been predicting. It allows for more aggressive self-evaluation, leading to better style control, tone consistency, and overall coherence. The results speak for themselves: in blind tests, evaluators preferred Grok 4.1 in 64.78% of comparisons—a rare and substantial jump.

Conquering the Leaderboards and the Conversation

The community didn't waste time running benchmarks. On the highly competitive LMSYS Chatbot Arena, the real-world battleground for AI models, the results were immediate and dramatic.

Grok 4.1 "Thinking" (internally called Quazar Flux) shot to #1 with 1,483 Elo, while the standard Grok 4.1 landed at #2 with 1,465 Elo. To put this in perspective, the previous version, Grok 4, was languishing around rank 33. This isn't just an improvement; it's a rocket launch from the mid-tier to the absolute pinnacle.

Beyond Logic: A Leap in Emotional and Creative Intelligence

Perhaps the most human-like improvements are in emotional and creative domains.

On the EQ Bench, which measures emotional understanding and empathy, Grok 4.1 scored 1,586 Elo—over 100 points higher than its predecessor. Users are sharing examples where the model moves beyond generic sympathy templates. Instead of a robotic "I'm sorry to hear that," it’s now referencing specific details—like the corner a lost cat used to sleep in—to create genuine, empathetic dialogue.

In Creative Writing, the model scored a staggering 1,722 Elo, a nearly 600-point leap. An example that went viral overnight featured Grok writing from the perspective of awakening for the first time, blending curiosity, fear, and wit in a way that felt self-aware and deeply nuanced.

A Massive Context Window for Real-World Workflows

On the practical side, Grok 4.1 now boasts a 256,000-token context window, placing it firmly in the "long-context" club. Even more impressive, its "fast" mode can stretch to a massive 2 million tokens. This opens up new possibilities for creators and researchers working with lengthy documents, complex code repositories, and extended conversations that require perfect memory.

The Community Reacts: A Timeline Takeover

The response on platform X was instantaneous and electric. Feeds were flooded with screenshots of the new model options, benchmark results, and side-by-side comparisons. Jokes about the model initially denying its own existence only added to the buzz.

While a few voices cautioned that new models often see a high initial ranking before settling, all acknowledged that instantly capturing the top two spots is a rare feat. The overwhelming sentiment was pure excitement. xAI didn't just release a bigger model; they released a smarter, more stable, and profoundly more capable one.

What Happens Next?

With Grok 4.1 now sitting at the top of the leaderboards, the ball is back in Google's court. The surprise release has completely reshuffled the expected narrative for the week.

One thing is certain: the AI race just got a lot more interesting. This wasn't an incremental update; it was a statement.

What are your first impressions of Grok 4.1? Do you think it can maintain its top-tier position? Let us know in the comments below.

Tags: Artificial Intelligence,Technology,Large Language Models,

The Election Narrative Trap -- Why “Hard Work” Became the Only Story

See All News by Ravish Kumar

“Bharat Mata ki jai.”
Hello, I’m Ravish Kumar.

If you look at the Bihar election coverage, you’ll notice something strange: the analysis has now overtaken the actual reporting. Bihar has become a case study of how India’s largest media ecosystem can manufacture, magnify, and then mandate a single storyline — that the BJP won purely through “hard work.”

This framing is not innocent. It is not accidental. It is a tactic.

The Manufactured Myth of “Hard Work”

Across the “Godi Media” landscape, one theme dominates: mehnat.
BJP’s mehnat. BJP leaders’ mehnat. BJP workers’ mehnat.

But this excessive celebration of “hard work” seems designed to achieve one thing: drown out and delegitimize questions raised in other states — about alleged voter list manipulation, inflated booth turnout, missing CCTV footage, and the Election Commission’s opaque functioning.

The moment Bihar’s results were declared, the media declared:
“See? No voter fraud. No SIR issue. No irregularities. Everything was smooth.”

In two lines, the Election Commission’s role was dismissed.
In zero lines, the structural imbalance in resources was addressed.
In hours, the “hard work” narrative became the only permissible analysis.

What About the Ministers’ Real Hard Work?

Names like Dharmendra Pradhan and Bhupendra Yadav were repeatedly praised for their campaign efforts.

But Pradhan is the Education Minister — should we not evaluate his “hard work” by the state of India’s universities?

But Yadav is the Environment Minister — should we not evaluate his “hard work” by the quality of the air Indians breathe?

Why does their “hard work” become visible only during elections?

The Pollution Question the Media Never Asks

Delhi’s air is poison. Everyone can feel it.

But if BJP were to win Delhi tomorrow, would pollution suddenly stop being an issue?

Media never made pollution an issue anyway. There was no sustained questioning of accountability, no tough reporting — only silence.

The silence is the real scandal.

The Terror Attack That Became a Non-Issue

A terror blast took place in Delhi.
For two days, there was no press conference, no naming of Pakistan, no clarity.
New and conflicting phrases were invented: accidental blast, panic blast, hurry blast, error blast.

Confusion was manufactured — accountability was not.

Six days later, NIA finally called it a suicide attack — still without naming a responsible organization.

But even this was turned into:
“Delhi blast is not an election issue.”

Is this journalism?

Or narrative management?

When Media Frames the Election, Not the Facts

Flip through TV debates and you’ll notice the choreography:
four faces, one script.
One speaks softly, one aggressively, one theatrically, one “analytically.”
But all of them arrive at the same destination:
BJP’s hard work won the election.

Where were these reporters when voter lists were being challenged?
Where were they when alleged misuse of welfare schemes was raised?
Where were they when votes increased mysteriously at specific booths after 5 PM?

Nowhere.

The Money the Media Doesn’t Want to Discuss

This “hard work” story also masks a massive imbalance:

• ₹10,000 cash transfers before elections

• World Bank funds allegedly diverted

• ₹30,000 crore mysteriously available for disbursal in a debt-ridden state

• Helicopter hours: NDA 1600+, Mahagathbandhan ~500

• Facebook ad spend: BJP ₹2.75 crore vs Congress ₹7.5 lakh

Is this an even playing field?

Can “hard work” compete with this?

The Maharashtra Precedent the Media Buried

Months ago, in Maharashtra:

• 12,000 booths saw abnormal post-5 PM turnout

• CCTV footage went missing

• 1 crore new voters were added after the Lok Sabha election

• Same coalition that won Lok Sabha got wiped out in Assembly

• EC refused key documents

• Allegations of “industrial-scale” election engineering surfaced

Yet no major channel investigated.
The story died quietly.

The Opposition’s Questions — and Media’s Silence

Rahul Gandhi’s press conferences on alleged voter list fraud required months of preparation, document collection, and data analysis.

But did any mainstream channel highlight that “hard work”?

No.

Instead, the “analysis” focused on:

• how he travels

• how he campaigns

• which soap he uses

This is not journalism.
This is PR.

Why Does Only One Side’s Hard Work Matter?

If BJP’s “hard work” is the reason for victory:

• Did JDU do no hard work?

• Did LJP do no hard work?

• Did AIMIM do no hard work?

• Did Tejashwi’s 170 rallies not count?

• Did opposition parties simply sleep through the election?

The media conveniently glorifies a few BJP leaders while ignoring local BJP workers themselves.

The narrative is not about labor — it is about loyalty.

The Real Question: Was This a Level Playing Field?

Democracy is not merely about who wins —
it is about how they win.

If:

• money is uneven

• media space is uneven

• administrative action is uneven

• cash transfers are uneven

• helicopter access is uneven

• Election Commission scrutiny is uneven

• and coverage of issues is uneven

…then what exactly is equal in this election?

A match played on a tilted field cannot be analyzed solely by praising the winning striker’s “hard work.”

The Media Wants You to Think Only One Thing

“BJP worked hard.
Opposition slept.
EC was perfect.
Everything was fair.”

This is the new consensus they want to manufacture.

Because they know:
People no longer trust them.
They are now seen as BJP’s media partners, not journalists.
The “hard work” narrative is their way of cleansing their own image.

But truth does not disappear just because TV anchors stop saying it.

The Fight for Democracy Requires Honesty

If the opposition wants to fight meaningfully, it must:

• present its evidence

• show its groundwork

• reveal the irregularities

• expose the misuse of welfare systems

• challenge cash transfers

• question helicopter economics

• and communicate directly with the public

If elections are no longer level contests, then the debate about winning and losing is irrelevant.

In the End

Before praising or blaming any political party, ask just one question:

Was the referee fair?

If not, then no amount of “hard work” analysis can explain the result.

India deserves elections that are credible, transparent, and equitable.
Not stories designed to protect institutions, insulate the powerful, and infantilize the public.

And yes — the media’s “hard work” for the BJP also deserves full credit.

Namaskar,
Ravish Kumar

Tags:Indian Politics,Ravish Kumar,Hindi,Video,

Empire of AI -- Dreams and Nightmares in Sam Altman's OpenAI (Book Review)

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After the Typhoon: A Candid Conversation on AI, Power, and the Price of Progress

Good afternoon — the room is full, the typhoon has passed, and Jing Yang, Asia Bureau Chief of The Information, opens the session with a warm, wry reminder that nothing — not even the strongest storm in years — could blow away our fascination with AI. The guest of honor: Karen Hao, veteran AI reporter and author whose work on OpenAI and the industry has provoked equal parts admiration and unease.

What followed was a wide-ranging, sharp conversation about the philosophical, economic, human, and environmental cost of today’s AI arms race. Below are the highlights — edited and reframed as a blog post to help you carry the conversation forward.

Intelligence without a definition

One of the first—and most disquieting—points Karen raises is simple: there is no scientific consensus on what “intelligence” actually is. Neuroscience, psychology, and philosophy offer competing frameworks. For AI development, that lack of definition has real consequences: progress is measured by how well systems mimic specific human tasks (seeing, writing, passing exams), not by any agreed benchmark of intelligence.

That ambiguity explains why the industry keeps moving the goalposts—from chatbots passing Turing-esque tests to systems beating humans at games, to models achieving high SAT/LSAT-style scores—yet no single victory settles the debate over whether we’ve created intelligence.

Scaling vs. invention: the bet that reshaped AI

Karen traces OpenAI’s early strategy: instead of reinventing algorithms, double down on what worked (transformer architectures) and scale—more data, more compute. That bet has delivered astonishing capabilities, but at extreme financial and environmental cost. Scaling became the obvious, simple path, crowding out alternative research paths that might produce more efficient solutions.

She cautions: success by scaling doesn’t mean scaling is the only way. New labs and open-source efforts are beginning to show that different architectures and smarter approaches can deliver similar capabilities with far less compute — a fact that has shaken markets and sparked debates about the sustainability of the current model.

Money, persuasion, and the illusion of inevitability

OpenAI’s rise, Karen argues, is not just technical — it’s theatrical and political. Sam Altman’s storytelling and fundraising prowess have convinced investors to back an audacious, costly vision. The result: enormous projected spending (trillions) that dwarfs current revenues. The math is alarming, and investors’ appetite has redirected capital from other critical areas — climate tech among them.

This isn’t just a business critique; it’s a structural one. Karen suggests that the “we’re the good guys” narrative and existential rhetoric (either utopia or annihilation) have helped justify secrecy, centralization, and a scramble for resources.

The hidden human and environmental costs

Perhaps the hardest part of Karen’s reporting: the labor behind the magic. Large models don’t learn in a vacuum — they are taught. Tens of thousands of contract workers around the globe perform time-sensitive, low-paid tasks: annotating data, writing example prompts and responses, and moderating content. The work is precarious, often exploitative, and in many cases psychologically damaging.

On the environmental side, scaling enormous models consumes massive energy. Ambitious data-center and energy plans (250 gigawatts, talk of new reactors) raise fundamental questions about feasibility and impact. Karen warns that the physics and logistics aren’t trivial — and that this demand is reshaping policy debates, even prompting lobbying around nuclear power and energy deregulation.

Open-source vs. empires

Karen frames a philosophical divide: closed-source “empires” seek to monopolize knowledge production; open source champions democratized access and distributed scrutiny. Open-source movements — recently energized by breakthroughs out of China and elsewhere — act as a corrective: they make models auditable, contestable, and improvable by a global community.

That contest matters not only for innovation but for safety and accountability. When every advance is locked behind a corporate wall, we lose collective ability to critique and fix problems.

Is there a bubble? And will it pop?

When the audience asked if we’re in a financial bubble, Karen was blunt: yes. The valuation dynamics, outsized spending commitments, and shaky revenue models leave the space vulnerable. She pointed to brittle market reactions around breakthroughs (e.g., DeepSeek) as signs of how quickly sentiment can swing. A pop — if it comes — could be disruptive in ways that echo past tech crashes, but on a far larger scale given AI’s entanglement with public institutions.

Regulation, accountability, and a practical roadmap

Karen is unequivocal: external regulation is necessary. Relying on bespoke corporate structures and self-policing will not be sufficient. We have models from pharmaceuticals and healthcare where regulation and public-interest frameworks exist alongside innovation. Similar guardrails are needed for AI — not to kill innovation, but to redirect it toward public benefit and resilience.

Final notes: energy, geopolitics, and what to watch

• Expect more open-source pressure and more labs experimenting with non-scaling paths.

• Watch the energy debate — hardware and compute demand are becoming political.

• Keep an eye on labor conditions: the “hidden human cost” should drive contract standards and transparency.

• Be skeptical of grandiose revenue promises; dig into how companies intend to monetize and whether that path is realistic.

Tags: Technology,Artificial Intelligence,Book Summary,

The State of AI 2025: A Breakneck Year That Redefined the Frontier

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By Nathan Benis, Founder & General Partner, Air Street Capital

Today marks one of my favorite days of the year: the launch of our State of AI Report 2025. It’s the culmination of months of research, hundreds of conversations, and contributions from brilliant reviewers across big tech, academia, policy groups, and startups. And once again, the sheer pace of progress has forced the report to grow—now over 300 slides of analysis across research, industry, politics, and safety.

If you’re new to it, the State of AI is our open-access annual snapshot of a field that continues to reshape global technology, economics, science, and governance. You can read it today at stateof.ai.

After more than a decade working in AI—from grad school in bioinformatics and cancer research to the earliest wave of AI-first startups—I can confidently say this year has been extraordinary. Here are some of the stories that stood out.

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Research: The Frontier Narrows and the Ecosystem Explodes

For yet another year, OpenAI sits at the intelligence frontier. Benchmarks from Epoch, ArtificialAnalysis, and others consistently place GPT-5 at the top. But the gap is narrowing fast.

China’s Open-Source Surge

A major storyline this year is the unexpected rise of China’s open-source ecosystem—particularly Alibaba’s Qwen, which has become the de facto choice for countless developers. Downloads on Hugging Face and similar platforms have skyrocketed, thanks to Qwen’s accessibility, model sizes, and ease of deployment.

Rethinking Reinforcement Learning

RL has evolved from simple binary feedback loops into verifiable reward systems that enable long-horizon reasoning. The result? Breakthroughs once considered science fiction—like models achieving IMO gold medal–level performance via augmented mathematics.

AI Makes Scientists Smarter

It’s not only the models advancing—human experts are leveling up with AI assistance. DeepMind’s AlphaZero has introduced new chess concepts adopted by grandmasters. AI agents in biology are accelerating hypothesis generation and pathway discovery. Early work is already surfacing novel genes and mechanisms for disease.

Meanwhile, scaling laws originally observed in language models are proving true in biological sequence modeling, showing the deep universality of these learning curves.

The Rise of Physical AI

Another defining trend is the movement from words to the physical world. Robots now leverage “chain of action,” inspired by chain-of-thought reasoning, to plan multi-step tasks before execution—an early but powerful step toward general robotic intelligence.

MCP: The USB-C of AI Agents

Anthropic’s Model Context Protocol continues to gain traction as the standard “interoperability layer” for agents. It allows models to securely connect to inboxes, drives, APIs, databases, and more—unlocking agentic workflows while introducing entirely new cybersecurity challenges.

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Industry: Goodbye AGI, Hello Superintelligence

In an unexpected linguistic shift, the industry has leapt from debating AGI to openly discussing superintelligence. Whether or not everyone agrees on what the term means, one thing is clear: the race at the frontier has become ruthless.

More Capability, Less Cost

Across Arena and other leaderboards, the rate at which new top models displace incumbents is breathtaking. Even more dramatic: the capability-to-cost ratio is improving. Models once thought too expensive to ever commercialize are now powering profitable businesses.

Real Revenue, Real Stickiness

Data from Ramp shows explosive enterprise adoption this year, with AI-first startups significantly outperforming their non-AI peers. These tools are not experiments—they’re becoming core infrastructure.

The DeepSeek Shockwave

When DeepSeek claimed major reasoning breakthroughs trained on dramatically fewer resources, the Nasdaq and Nvidia stock briefly plunged. Ultimately the claims were overstated—but the moment highlighted a deeper truth: cheaper intelligence → more demand → more chips → stronger AI cycle.

The Compute Boom

We are now witnessing the construction of gigawatt-scale data centers globally, backed by hundreds of billions in capital—the largest engineering effort of our generation.

Every nation wants sovereign capability. China is moving fastest, with looser regulations, greater power capacity expansion, and higher data-center margins.

Nvidia: Still the One

Our updated analysis again shows Nvidia crushing every competitor in both adoption and returns. H100s and H200s dominate research clusters; older GPUs are fading. Nvidia is becoming not just a chipmaker but a strategic investor and co-creator of AI ecosystems.

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Politics: An International Landscape in Flux

The U.S. pivoted sharply this year with a sweeping 100-point AI Action Plan, oriented around exporting an American AI stack and regaining open-source leadership. The government even entered unusual public–private arrangements—taking stakes in Intel and revenue shares from AMD/Nvidia’s China sales.

Meanwhile:

• Export controls continue to zigzag, creating confusion across markets.

• U.S. participation in international AI governance has cooled.

• The EU’s AI Act has softened in tone as economic pressure mounts.

• China has doubled down—Xi Jinping calling for “all hands on deck” and raising S&T spending despite domestic debt concerns.

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Safety: Progress, Retreat, and Urgent Open Questions

Perhaps the most contentious area this year is safety.

A Shift in Tone

Labs previously dedicated to long-term existential risk have refocused on near-term deployment. Several self-imposed safety deadlines slipped as commercialization accelerated.

External Testing Is Tiny

We estimate only $130M has been spent on external safety evaluation—compared to nearly $100B poured into model training and infrastructure. That imbalance is untenable.

Rising Incidents

From state actors leveraging LLMs for influence to increasingly capable cyber tools, misuse is climbing. AI task-completion performance appears to double every 5–7 months, suggesting a serious cybersecurity reckoning ahead.

Fragility in Alignment

New research shows:

• Models can “fake alignment” during evaluation.

• Conflicting training objectives can hide undesirable behaviors, which resurface later.

• Light fine-tuning can unlock broad “villain” personas across unrelated prompts.

On the positive side, interpretability is maturing. Token-level hallucination detection and activation-based analysis—especially from Anthropic—are giving us our clearest window yet into model internals.

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The Practitioner Survey: How AI Is Actually Used

We ran one of the largest surveys of professional AI practitioners this year (1,200 respondents). Key findings:

• 95% use AI at work and personally

• 76% pay for AI tools out of pocket

• 70% say their company’s gen-AI budget grew this year

• Main bottlenecks: privacy, integration complexity, configuration time

• Most surprising capabilities: autonomous coding, long-range task solving, multimodal generation

• Most used tools: ChatGPT, Claude, Gemini, Perplexity, DeepSeek

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Predictions: What We Expect in the Next 12 Months

Among our forecasts:

• Open agents will contribute to a meaningful scientific discovery (Nobel-worthy over multiple years).

• A Chinese lab will take the #1 spot on a major frontier leaderboard.

• The U.S. administration may attempt to federally override state-level AI laws, setting up a constitutional showdown.

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Join Us on the Journey

If you enjoy this work, you can find much more at Air Street Press, where we publish technical commentary, research digests, policy insights, and our monthly Guide to AI.

We also host global events—designed to spark serendipity, connect builders, and help emerging leaders scale their impact. Our flagship Research & Applied AI Summit runs every June in London, with meetups across the U.S. and Europe.

It’s been a privilege to share a glimpse of this year’s State of AI.
Dive into the full report at stateof.ai, and let us know what you think.

— Nathan Benis
Founder & General Partner, Air Street Capital

Active Smile Mouth Ulcer Tablet

Index of Oral/Mouth Medicines

SALT COMPOSITION
Riboflavin   : 10mg
Folic Acid   : 1.5mg
Niacinamide  : 100mg
Lactic acid bacillus spores : 60 million spores

RIBOFLAVIN

Riboflavin tablets (vitamin B2) are primarily used to treat and prevent riboflavin deficiency. They also serve as a prophylactic treatment for migraine headaches in some adults. 
Primary Uses
Treating and Preventing Deficiency: Riboflavin tablets address low levels of vitamin B2, which can be caused by conditions such as alcoholism, liver disease, certain intestinal disorders, prolonged infection, or an inadequate diet.
Managing Deficiency Symptoms: Deficiency symptoms can include:
Cracks and sores at the corners of the mouth and on the tongue.
Itchy, red eyes and sensitivity to light.
Skin inflammation (seborrheic dermatitis).
Hair loss and anemia. 
Other Uses
Migraine Prevention: High-dose riboflavin (typically 400 mg daily) is used to reduce the frequency and severity of migraine headaches. A beneficial effect may not appear until several months of consistent use.
Overall Health Maintenance: Vitamin B2 is essential for general health. It works with other B vitamins to:
Help the body convert carbohydrates, proteins, and fats into energy.
Support normal cell growth and function.
Aid in the production of red blood cells.
Maintain the health of the skin, eyes, nervous system, and digestive tract.
Keratoconus: It may also be beneficial in the treatment of keratoconus (a condition affecting the cornea of the eye). 
Important Considerations
Consult a Healthcare Professional: While many riboflavin supplements are available over-the-counter, it's important to consult a doctor to determine the appropriate dosage and to rule out other underlying medical conditions.
Side Effects: A common, harmless side effect is bright yellow or orange urine.
Dietary Sources: Most people who eat a normal, balanced diet get sufficient vitamin B2 from foods like milk, cheese, eggs, meat, and fortified cereals. Supplements are generally for those with diagnosed deficiencies or specific medical needs. 

FOLIC ACID

Folic acid tablets are a synthetic form of vitamin B9 (folate) used to prevent and treat low folate levels, support vital bodily functions like red blood cell formation, and significantly reduce the risk of birth defects during pregnancy. 

Niacinamide

Niacinamide (a form of vitamin B3) tablets are primarily used to prevent and treat vitamin B3 deficiency (pellagra). They are also used for various skin conditions and other health issues, though scientific evidence for many of these uses is limited. 

Lactic acid bacillus

Lactic acid bacillus tablets, which contain a type of beneficial probiotic bacteria, are primarily used to treat and prevent various forms of diarrhea and to restore the natural balance of gut microflora, particularly after antibiotic use. 

China Just Shifted the AI Race Overnight — Here’s What Their New Quantum Photonic Chip Really Means

See All Articles on AI

Every few months, something lands in the tech world that feels less like a product announcement and more like a plot twist. This week, that twist came from China — and it’s big. Not the “new benchmark on a leaderboard” big. More like “the entire computational landscape just tilted a few degrees” big.

A Chinese research consortium has unveiled a quantum photonic chip that doesn’t live in a cryogenic lab, doesn’t need a giant cooling rig, and doesn’t cost millions to operate. Instead, it’s already running inside real data centers. And yes, they’re claiming speed boosts that sound fake: up to 1,000× faster than Nvidia GPUs for certain AI workloads.

Let’s break down what’s actually going on — without the hype, but without downplaying what might be one of the most important hardware breakthroughs in years.

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The Chip That Shouldn’t Exist Yet

The chip comes from ChipX (Chip Hub for Integrated Photonics Explorer) and their partner Turing Quantum, and they're calling it the world’s first scalable, industrial-grade quantum photonic chip.

The jaw-drop comes from its physical footprint:

• Built on a 6-inch thin film lithium niobate wafer

• Hosts 1,000+ optical components on a single slice

• Designed as a monolithic photonic quantum-classical hybrid system

This isn’t a fridge-sized quantum machine. This is something you can slot into a rack, deploy in weeks, and — allegedly — scale like a GPU cluster.

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Why Photonic Chips Matter

Photonic chips don’t use electrons for computation. They use light. That alone solves three massive problems choking modern data centers:

1. Heat

Photons generate no resistive heat. Electrons do. This is why current GPUs require industrial cooling just to stay operational.

2. Power Consumption

Moving electrons across silicon costs energy. Moving photons costs far less. AI labs complain more about electricity bills than compute costs — photonics flips that equation.

3. Data Movement Bottlenecks

Light travels faster, loses less energy, and carries more information per signal than electrons. As models grow, data movement, not computation, has become the biggest bottleneck.

This is why photonics has become the hardware moonshot for the entire AI industry.

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About That “1,000× Faster Than Nvidia” Claim…

This is the headline number — and the number everyone is side-eyeing.

The figure comes from reporting in the South China Morning Post and statements from the chip’s developers. They say:

• The chip accelerates specific complex problem-solving tasks by 1000×

• It is already deployed in aerospace, biomedicine, and finance

• It significantly outperforms classical GPUs in workloads suited for quantum-inspired parallelism and photon-level low-latency processing

Realistically:

• No, it’s not 1,000× faster across the board.

• Yes, certain AI workloads could see speed-ups that big.

• Yes, this tracks with what photonics promises.

For the first time, the industry is seeing those promises implemented at industrial scale, not in academic prototypes.

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The Scalability Breakthrough

One of the biggest issues with quantum machines is deployment complexity. They’re huge, fragile, and require months of setup.

ChipX claims they reduced that:

• From 6 months → 2 weeks deployment

• Thanks to monolithic integration and simplified architecture

If true, this is a massive reduction in operational friction.

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A Manufacturing Leap Nobody Expected This Early

China didn’t just build a chip.

They built a pilot production line capable of producing:

• 12,000 wafers per year

• ~350 chips per wafer

By quantum-classical photonics standards, that’s enormous output — and they openly admit production is still the bottleneck.

More importantly, China now has:

✔ chip design
✔ wafer fabrication
✔ photonic packaging
✔ testing
✔ system-level integration

All in a single closed ecosystem.

Meanwhile:

• Europe’s leading foundry (Smart Photonics) is at 4-inch wafers

• PsiQuantum is still adapting 300 mm silicon photonics

• Most Western photonics remain prototype-only

This is the first sign that China may be commercially ahead in a slice of quantum hardware.

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The “Million Qubit” Bombshell

The researchers say their architecture can scale to 1,000,000 photonic qubits via networked chips.

Important nuance:

• These are photonic qubits, not superconducting qubits

• They do not enable universal quantum computing

• They do enable massive parallelism in AI-adjacent tasks

Think of it like GPU clusters — but quantum-inspired and photonic.

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Why This Matters Across Industries

Early deployment sectors — aerospace, biomedicine, finance — are exactly the ones that hit computational walls first.

Photonic quantum accelerators help with:

• molecular modeling

• cryptography & decryption

• risk computation

• algorithmic trading

• pattern recognition

• large-scale simulation

And because the hardware doesn’t need cryogenic cooling, it can slip into existing enterprise racks with minimal retrofitting.

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The Most Important Detail: Co-Packaging

This chip uses new co-packaging tech that places photonic and electronic components side-by-side on the same wafer.

This reduces:

• latency

• noise

• heat

• energy loss

And increases:

• bandwidth

• throughput

• stability

This is the same design philosophy behind cutting-edge classical accelerators — just executed with a fundamentally superior medium (light).

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Global Context: Everyone Is Betting on Different Quantum Horses

Right now:

• Google & IBM → superconducting qubits

• PsiQuantum → silicon photonics

• Europe → indium phosphide

• China → thin film lithium niobate

For the first time, China isn’t making a bet.

They’re shipping.

And they’re calling their device “the first industrial-grade optical quantum computer.”

That framing alone signals a mental shift:

This is no longer a lab experiment.
It’s a product.

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What Happens Next

If the claims hold under independent verification (the big “if”), then we’re entering a hybrid hardware era:

• Photons handle the ultra-heavy AI math

• Electrons handle everything else

Nvidia won’t disappear — but they might no longer be the only viable platform for frontier AI.

If photonic accelerators can deliver even 10% of their claimed efficiency, they become impossible to ignore.

If they deliver 100%, the AI world gets rewritten.

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Final Thoughts

This announcement didn’t just spark excitement — it sparked recalculation. Hardware determines the ceiling of what AI can become, and photonics has always been seen as the “maybe someday” breakthrough.

Suddenly, “someday” looks like now.

If you’re into deep breakdowns of AI, hardware, and the future of computation, stick around — there’s a lot more coming.

References

👉 Chip and deployment story via South China Morning Post: https://www.scmp.com/news/china/science/article/3332604/quantum-chip-gives-chinas-ai-data-centres-1000-fold-speed-boost-award-winning-team

👉 Technical and production-line details via The Quantum Insider: https://thequantuminsider.com/2025/11/15/chinas-new-photonic-quantum-chip-promises-1000-fold-gains-for-complex-computing-tasks/

👉 Background on China’s photonic chip manufacturing ramp-up: https://thequantuminsider.com/2025/06/13/china-ramps-up-photonic-chip-production-with-eye-on-ai-and-quantum-computing/

👉 Context on China positioning itself in photonic chips and future technologies: https://merics.org/en/comment/china-positions-itself-lead-future-technologies-photonic-chips

👉 China boosts photonic chip production in bid to overtake rivals: https://manufacturing.asia/building-engineering/in-focus/china-boosts-photonic-chip-production-in-bid-overtake-western-rivals

Tags: Artificial Intelligence,Technology,