Mad Hedge AI Archives - Mad Hedge Fund Trader

BETTER 68 YEARS LATE THAN NEVER

(MSFT), (GOOGL), (AMZN)

Here's a fun bit of trivia: The term "artificial intelligence" was coined in 1956 at a Dartmouth College workshop.

The organizers thought they could teach machines to use language, form abstractions, and solve problems reserved for humans. Their estimated timeline? About two months.

Sixty-eight years later, Microsoft (MSFT) just showed us how hilariously wrong that timeline was - and more importantly, how massively profitable being right about AI's timeline can be.

While everyone's been watching ChatGPT make headlines, Microsoft has quietly turned its OpenAI partnership into a money-printing machine. We're talking about 33% growth in Azure cloud sales, with AI services alone responsible for 12 percentage points of that growth.

Let that sink in for a moment.

In the latest quarter, Microsoft's revenue jumped 16% to $65.6 billion, accelerating from the previous quarter's 15.2% growth. And here's what really gets my attention: Microsoft's net margins are already sitting pretty at 38%, with a clear pathway to 45% as AI scales up.

But wait - it gets better.

Remember when everyone thought the $10 billion OpenAI investment was crazy? Well, management is now projecting $10 billion in annual AI-related sales.

That might seem like pocket change for a company expected to rake in $286 billion, but here's what the market is missing.

Microsoft 365 Copilot alone is projected to generate $4.6 billion in revenue this fiscal year. And that's with only 4% of the Office user base signed up. Do the math on full penetration, and you'll see why I’m getting excited.

Now, let's talk about OpenAI's new "o" series models - the secret weapon nobody's discussing at cocktail parties. Unlike traditional AI that processes queries in one shot, these babies use what the pointy-heads call "chain-of-thought" (CoT) reasoning.

Think of it as AI that can actually think longer and harder about problems, like a chess grandmaster considering multiple moves ahead.

The results on the ARC-AGI-PUB benchmark tests are making Google's (GOOGL) engineers sweat through their hoodies. And thanks to those exclusivity clauses my legal friends keep raving about, Microsoft gets first dibs on every breakthrough.

But here's the kicker that makes this a potential portfolio game-changer: Microsoft is sitting on $116.2 billion in cash with only $45 billion in debt. That's a war chest that makes competitors nervous, especially when you're talking about the billions needed for those precious Nvidia GPUs that power AI data centers.

Sure, the stock isn't cheap at 33 times forward earnings. But with double-digit revenue growth locked in for years and quickly expanding margins, that multiple starts looking reasonable.

Now, I'm not saying it's all sunshine and APIs. Google and Amazon (AMZN) are throwing billions at their own AI programs.

There's also that pesky clause about OpenAI potentially restricting Microsoft's access if they achieve artificial general intelligence (though my tech buddies tell me that's about as likely as San Francisco having affordable housing).

And yes, any broad market tantrum could hit high-multiple tech stocks like a concrete pillow. But here's what keeps me up at night: What if we're still underestimating the impact of truly intelligent AI on enterprise software?

Those Dartmouth professors in 1956 thought they could solve AI in two months. They were off by about seven decades. But Microsoft isn't making the same mistake of underestimating the timeline - they're playing the long game.

Between the OpenAI partnership, that fortress balance sheet, and enterprise customers practically begging to pay for Copilot, this could be one of those rare moments when a mega-cap tech stock is actually undervalued.

Just remember you heard it here first. By the time the Wall Street Journal catches up, the easy money will already be gone.

Speaking of gone - I just found those original Dartmouth workshop notes buried in my office. Apparently, they also predicted flying cars by 1957 and robot butlers by Christmas.

Maybe I should feed those predictions to ChatGPT and see if it laughs. Though at Microsoft's current pace, it might just start building those robot butlers instead.

YOUR HOSPITAL'S FAX MACHINE IS ABOUT TO BE WORTH $100 BILLION

Mad Hedge AI

(NVDA), (MSFT), (GOOGL), (AMD), (META)

You know what keeps catching my attention these days? The way artificial intelligence is sneaking into every corner of the market faster than my daughter can fix those broken calculators in her UC computer science class (more on that later).

Let me throw a number at you that made me sit up during my morning coffee: the global AI market, currently parked at half a trillion dollars, is barreling toward three trillion by 2032.

We're talking about a 20% compound annual growth rate, the kind of numbers that make even jaded old traders like me take notice.

I recently found myself poring over a 273-page congressional task force report on AI (yes, the things I do for you, dear reader).

Between coffee stains and margin notes that started looking like modern art, I discovered 66 findings and 89 recommendations that actually make sense. Imagine that – Congress getting something right about technology!

Speaking of getting things right, the FDA just pulled off something remarkable. They've created what they call "Predetermined Change Control Plans," which is bureaucrat-speak for letting AI medical companies update their algorithms without filing enough paperwork to deforest the Amazon.

This could help save the healthcare industry $13 billion in 2025 alone – not bad for a government initiative.

As expected, the usual names like Nvidia (NVDA), Microsoft (MSFT), Google (GOOGL), AMD (AMD), and Meta (META) have their hands all over this. They're building data centers that use enough power to light up Lake Tahoe twice over, and the numbers justify their aggressive expansion.

Corporate AI adoption shot up 270% between 2015 and 2019, with nearly one hundred million new AI-related jobs expected by 2025.

Even the Pentagon is getting into the game, pouring billions into AI development. Which makes sense – when you're looking at technology that could reshape global competition, you don't want to be left behind.

And the impact is spreading far beyond Silicon Valley. Take healthcare, for instance. Some 90% of U.S. hospitals are planning to implement AI solutions by 2025, driving the healthcare AI market from $12 billion to over $100 billion by 2030.

Remember when hospitals were still using fax machines? Some still are - probably the same ones with waiting room magazines from 2010. But now they're racing from those paper jams straight into AI diagnostics.

The congressional report flags what you'd expect - privacy concerns around AI handling sensitive medical data. After all, we're talking about systems that can access everything from your blood pressure readings to your insurance claims. But unlike those old fax machines, this technology is moving too fast for traditional regulations to keep up.

The task force gets it right: success with AI requires a delicate balance. Just like I tell my daughter about her computer science projects, this isn't just about the technology. It's about how we use it.

That means clear government guidelines, aggressive but responsible innovation from industry, and serious intellectual firepower from academia. Get this formula wrong, and we'll have bigger problems than misrouted faxes.

For those watching this digital gold rush (and I know you are), here's my take: AI isn't just another tech bubble filled with hot air and PowerPoint presentations.

The projected $15 trillion in global economic value by 2030 isn't just a number pulled out of thin air – it's the kind of growth that creates generational wealth opportunities.

Just remember what I always say about transformative technologies: there's a time to go all in (like buying tech stocks in 2009), and there's a time to be strategic.

Right now, we're in that sweet spot where the technology is real, but the market hasn't fully priced in the implications.

Speaking of implications, my daughter just texted me that her next computer science project involves teaching AI to recognize broken circuit boards. Given how fast this technology is moving, I wouldn't be surprised if next semester she's programming AI to fix the circuits itself.

And that's exactly why I'm keeping a close eye on this sector. When college sophomores are doing what billion-dollar companies were struggling with just a few years ago, you know you're onto something big.

TOO SMART FOR ITS OWN GOOD, TOO CHEAP TO IGNORE

Mad Hedge AI

(GOOG), (AAPL), (META)

While driving to my local Trader Joe's last weekend, a friend of mine called, panicking about missing out on the AI revolution. "Should I dump my tech giants and buy every AI startup I can find?" he blurted, sounding like he’d just read a headline predicting the end of civilization as we know it.

I had to chuckle. It reminded me of the dot-com boom when my dentist was giving me stock tips while drilling my teeth. But here's the thing - this time really is different, and I've got the mind-bending numbers to prove it.

Let me share something that will knock your socks off: every four months - about the time it takes for the seasons to change - the cost of running AI models drops by half. That's not a typo.

Compare that to Moore's Law, which took a leisurely 18-24 months to achieve the same cost reduction. We're witnessing the fastest cost decline of any technology in human history.

Want a real head-spinner? In 2020, training a GPT-4 model would have set you back $6 billion - enough to buy several Caribbean islands or a fleet of private jets. By 2026, your teenager's smartphone will pack the same punch. Talk about deflation.

And you know who's feeling that sting most? Our friends at Google (GOOG). Back in 2020, they looked invincible with their 8.5 billion daily searches, mountains of data, and a budget that could buy a small country.

But by 2023, OpenAI had beaten them to the punch with ChatGPT, leaving Google playing catch-up. Even now, using Google's best AI models costs customers 40% more than OpenAI's equivalents. That's like showing up to a price war with premium pricing.

Speaking of tech giants moving at glacial speed, Apple (AAPL), the company that redefined cool, seems oddly uncool about AI.

Four years after OpenAI released GPT-3, Apple finally entered the game, admitting its models wouldn't measure up to OpenAI, Anthropic, or Meta's (META) Llama 3. (Meta's name choices may be questionable, but their AI game isn't.)

Why the lag? Big companies have big reputations, and AI has a way of going rogue. One moment it’s crafting poetry, and the next, it’s confidently hallucinating facts.

For firms like Apple or Google, whose brands rely on trust, the risk of unleashing an unpredictable AI model is a PR nightmare waiting to happen. So, they’re hedging their bets, sanding off AI’s sharp edges.

Take Apple’s upcoming image generator: it offers a limited set of options, as if customers can’t handle full creative freedom. It’s safe but stifling—the antithesis of AI’s potential.

Still, AI is infiltrating every sector, not just tech. In healthcare, AI models are passing medical licensing exams, potentially revolutionizing diagnostics and treatment.

Finance is using AI to personalize digital wallets, and Tesla (TSLA) is collecting mind-boggling amounts of real-world data to develop autonomous vehicles and—gulp—humanoid robots.

Actually, Tesla has been collecting more real-world data than Google could dream of. Their camera-equipped cars generated 80 quadrillion tokens of data last year alone. By decade's end, they'll hit 300 quadrillion.

And if you’re wondering where this data is going, think self-driving cars, next-level automation, and maybe a future where your AI assistant knows you better than your spouse. (Let’s hope it’s less judgy, too.)

Speaking of betting big on AI, the venture capital crowd is having a field day. In 2024, they're throwing money at AI startups like it's confetti at a Silicon Valley wedding - over $90 billion globally.

US startups are grabbing more than 40% of those dollars, and in the last three months, it's jumped to over half of all venture funding. It's like 1999 all over again, but this time with actual revenue streams.

Which brings me back to my friend and his panicked question about ditching his tech giants. My advice to him—and to you—is simple: this isn’t an either-or scenario. It’s not about choosing between established players and scrappy newcomers.

It’s more it’s about understanding who’s positioning themselves to ride the AI tsunami. Some will adapt and thrive. Others will crash and burn.

So, what does that mean for the market? In the short term, buckle up. Volatility is inevitable when a technology moves this fast. But long term, the opportunities are staggering.

As for my friend, I told him to think of AI like the early days of the internet: messy, chaotic, and bursting with potential.

Then I hung up because yet another SUV was swerving dangerously close, and the future, it seems, doesn't yet include AI-powered traffic cops.

GROWTH IN THE ORCHARD

Mad Hedge AI

(AAPL), (NVDA), (ADBE), (AI), (APP), (SOUN)

I spent thirty minutes last night trying to get an AI app to turn my cat into a Renaissance painting. The result looked more Picasso than Rembrandt, but it got me thinking about Apple's latest App Store strategy. They're pushing AI apps hard, and not just because someone in Cupertino has a thing for digital pet portraits.

Back in my hedge fund days, we had a saying: "Watch what they do, not what they say." Apple's (AAPL) quiet curation of AI-powered apps tells us more about where the market's heading than any flashy keynote ever could.

The numbers behind this shift are the kind that used to make my traders spill their morning coffee. We're looking at a 35% compound annual growth rate in the AI app market through 2030.

For perspective, that's the kind of growth rate I used to see in emerging market funds during the early 2000s boom - except this time, it's backed by actual revenue and not just optimistic projections.

Speaking of projections, the global AI market could hit $1,339 billion by 2030. I remember when reaching a billion in assets under management was considered a milestone. Now we're throwing around numbers that make billion seem quaint.

NVIDIA's (NVDA) been riding this wave like... well, like NVIDIA. Their GPU technology has become so fundamental to AI development that trying to run modern AI without it would be like trying to run my old hedge fund with an abacus. Trust me, I checked their order books - everyone from basement developers to major corporations is lining up for their chips.

Adobe's (ADBE) Firefly suite is particularly interesting. They've managed to thread the needle between AI innovation and artist compensation - something my legal team would have appreciated during our copyright disputes in the '90s. Their stock performance reflects this elegant solution to a complex problem.

Here's what's really catching my attention: the AI in mobile apps market is set to grow from $16.7 billion to $249.8 billion by 2033.

I've seen enough market cycles to know when numbers are just hype, but these align with what I'm seeing on the ground. Companies are integrating AI faster than my daughter downloads TikTok videos - by 2024, 72% of organizations will have AI in their operations.

Let's talk about C3.ai (AI) for a moment. Their stock chart looks like my heart rate monitor during the 2008 financial crisis, but there's substance beneath the volatility.

AppLovin (APP), meanwhile, is doing something fascinating with AI-driven advertising that reminds me of the early days of programmatic trading, just infinitely more sophisticated.

The subscription models these companies are using remind me of the early days of software-as-a-service, except now we're dealing with AI-as-a-service. The key difference? These apps actually deliver value beyond just moving desktop software to the cloud.

Capital expenditure in AI is expected to cross $1 trillion in 2025. Remember when hitting a billion dollars in tech investment felt monumental? Now, in a world where trillion-dollar valuations are becoming the norm, that barely qualifies as a headline.

What's particularly intriguing is the job creation potential - 133 million new roles by 2030. Having witnessed multiple technological transitions in finance, I can tell you this feels different. We're not just automating tasks – we're creating entirely new categories of work.

The AI app sector brought in $1.8 billion in 2023, with projections suggesting $30 billion by 2030.

These aren't just numbers pulled from an analyst's wishful thinking - they're based on current adoption rates and revenue patterns that remind me of the early internet boom, minus the pets.com-style absurdity.

For those looking to play this trend, I'm seeing opportunities across the spectrum.

Hardware leaders like NVIDIA continue to dominate their niche. Adobe has positioned itself perfectly at the intersection of creativity and AI.

Even companies like SoundHound (SOUN), while still finding their footing, show promise in voice AI that goes beyond asking your phone for weather updates.

Apple's AI app focus isn't just another tech trend - it's a clear signal of where consumer technology is headed. And after decades in the market, I've learned to pay attention when signals this strong appear.

Now if you'll excuse me, I need to go try that AI art app again. My cat's demanding a Baroque period portrait this time. And unlike my creative direction, they’re delivering results worth framing.

HERE WE GO AGAIN?

Mad Hedge AI

(BLOK), (MSTR), (LEGR), (JD), (BIDU), (BABA), (AMD)

There's an old Wall Street saying that the market has a way of turning exuberance into experience. After decades of trading, I've watched this play out more times than I can count, and these days that wisdom is ringing in my ears like a persistent trading reminder.

Just last week, I watched the S&P 500 touch $5,994.89 (up another 0.3%) while the Nasdaq Composite flirted with $20,000, sitting pretty at $19,886.60 (up 0.6%). Feels a bit like déjà vu, doesn't it?

Let's rewind the tape to November 30, 2022. While most folks were arguing about whether to serve turkey or ham for the holidays, OpenAI quietly dropped ChatGPT into our laps. Talk about a stealth bomber.

Since then, the S&P 500 has rocketed up 49%, while the tech-heavy Nasdaq has left Earth's orbit entirely with a 75% gain. And that's not a typo, folks - I triple-checked those numbers.

Full disclosure: I've been around this rodeo circuit long enough to see a few "next big things" come and go. Remember blockchain? (If you're wincing right now, you probably bought some crypto at the top). Let me share a little story about that particular circus.

Take the Amplify Transformational Data Sharing ETF (BLOK) - a name that probably took longer to create than some blockchain projects lasted.

This fund has actually kept pace with the Nasdaq and outperformed the S&P 500 since 2018. Impressive, right? Well, hold onto your hardware wallets, because here's where it gets interesting.

Peek under the hood, and you'll find MicroStrategy (MSTR), a company that's up nearly 3,000% since January 2018. But here's the kicker - they didn't get there by revolutionizing blockchain.

They basically turned themselves into a publicly traded Bitcoin piggy bank. It's like entering a marathon and winning by taking an Uber to the finish line. Technically effective, but not exactly what the prospectus advertised.

And don't get me started on the First Trust Indxx Innovative Transaction & Process ETF (LEGR). Despite having tech heavyweights like JD.com (JD), Baidu (BIDU), and Alibaba (BABA) in its portfolio, most of these stocks have been underwater since 2018.

The fund's saving grace? AMD's (AMD) 1,200% moonshot, powered by - plot twist - artificial intelligence, not blockchain.

So what does this tell us about AI stocks heading into 2025? Well, the Nasdaq's got an interesting story to tell.

Since 1971 (yes, I've been watching it that long), it's only had back-to-back losing years twice. The last time was over two decades ago. It's like that friend who keeps failing upward - somehow, it just works.

But here's my two cents after decades in the trenches: investing in megatrends is like trying to pick the next Beatles at a high school talent show. Sure, somebody in that auditorium might be the next Paul McCartney, but good luck figuring out who.

Want my advice? If you're itching to play the AI game, stick to the established players or passive index funds. You know, the ones that actually have revenue and aren't just PowerPoint presentations with "AI" slapped on them.

It's like I always tell newer traders - sometimes the safest way to join a gold rush isn't by prospecting, but by selling picks and shovels to the miners.

After all, history and my battle-scarred portfolio suggest that while markets should keep climbing in 2025, not every AI company is going to be sending champagne to their shareholders.

Sometimes the smartest play isn't trying to outsmart the market - it's just making sure you've got a seat at the table when the feast begins.

And speaking of feasts, did I mention I owned MicroStrategy back when they were actually a software company? But that's a story for another day...

RBC's Global Insight 2025 Outlook: Navigating a World Transformed by AI

Mad Hedge AI

Toronto, Canada - RBC, a leading global financial institution, has released its highly anticipated Global Insight 2025 Outlook report, providing a comprehensive analysis of the economic, financial, and geopolitical trends that are expected to shape the world in the coming years. The report highlights the profound impact of artificial intelligence (AI) across various sectors, emphasizing the need for investors and businesses to adapt to this rapidly evolving landscape.

AI: The Defining Technology of Our Time

RBC's report identifies AI as the most transformative technology of the 21st century, with the potential to revolutionize industries, redefine business models, and reshape the global economy. The report emphasizes that AI is no longer a futuristic concept, but a present-day reality that is already impacting our lives in numerous ways.

"AI is not just a technological advancement; it's a fundamental shift that is reshaping the world as we know it," says Helen Zhang, Head of Global Insight at RBC. "Its impact will be felt across all sectors, from healthcare and finance to manufacturing and transportation. Businesses and investors who fail to grasp the implications of AI risk being left behind."

AI's Impact on the Global Economy

RBC's report predicts that AI will be a key driver of economic growth in the coming years, boosting productivity, creating new jobs, and unlocking new opportunities. The report estimates that AI could contribute up to $15.7 trillion to the global economy by 2030.

However, the report also acknowledges the potential challenges associated with AI, including job displacement, ethical concerns, and the risk of widening inequality. RBC stresses the importance of responsible AI development and deployment, ensuring that its benefits are shared broadly and its risks are mitigated.

AI and the Future of Work

The report delves into the impact of AI on the workforce, acknowledging that while AI will create new jobs, it will also displace existing ones. RBC emphasizes the need for workers to adapt to this changing landscape by acquiring new skills and embracing lifelong learning.

"The future of work will be shaped by AI, and those who are prepared to adapt will thrive," says Zhang. "We need to invest in education and training programs that equip workers with the skills they need to succeed in an AI-powered world."

AI in Key Sectors

RBC's report provides a detailed analysis of AI's impact on various sectors, including:

Healthcare: AI is transforming healthcare by enabling earlier disease detection, personalized treatments, and more efficient drug discovery.
Finance: AI is being used to automate tasks, detect fraud, and provide personalized financial advice.
Manufacturing: AI is optimizing production processes, improving quality control, and enabling predictive maintenance.
Transportation: AI is powering autonomous vehicles, optimizing logistics, and improving traffic flow.

Investing in the Age of AI

RBC's report provides guidance for investors on how to navigate the AI landscape, highlighting the opportunities and risks associated with this transformative technology. The report identifies key investment themes, including:

AI infrastructure: Companies that provide the hardware and software necessary for AI development and deployment.
AI applications: Companies that develop and apply AI solutions across various industries.
AI enablers: Companies that provide services and technologies that support AI development and adoption.

RBC's Commitment to AI

RBC is committed to harnessing the power of AI to enhance its products and services, improve its operations, and create value for its clients. The bank has made significant investments in AI research and development, and is actively exploring new ways to leverage AI across its business.

"We believe that AI has the potential to transform the financial industry, and we are committed to being at the forefront of this transformation," says Zhang. "We are investing in AI to provide our clients with more personalized and innovative solutions, and to make our operations more efficient and secure."

Conclusion

RBC's Global Insight 2025 Outlook provides a comprehensive and insightful analysis of the trends that will shape the world in the coming years. The report highlights the transformative potential of AI, while also acknowledging the challenges and risks associated with this technology. RBC's commitment to responsible AI development and deployment, coupled with its investments in AI research and development, position the bank as a leader in the AI revolution.

BIG FISH, LITTLE FISH

Mad Hedge AI

(MSFT), (NVDA), (UBER), (AMD), (INTC), (QCOM), (TSM), (GOOGL)

I have always been a sucker for the small fish that big sharks circle around, especially when those sharks have names like NVIDIA (NVDA) and Uber (UBER). Believe me, after all these decades swimming in the turbulent waters of the hedge fund world, you learn to pay attention to who sidles up to whom.

So when NVIDIA, which controls roughly 80% of the AI chip market and has sent record revenue zinging through its data center division, and Uber, the swaggering king of ride-sharing with a solid 76% market share, both take a shine to a tiny AI startup, I start sniffing around. And I mean really sniffing around.

It’s like looking at a painting’s back corners instead of just the center – that’s where you find the real brushstrokes and hidden signatures. Remember, NVIDIA and Uber aren’t the sort of outfits that drop their change in a random tip jar.

They’re placing bets on a startup that’s fiddling with real-time edge computing. Think rapid-fire data processing right where the action is, no waiting for some distant server to give a nod.

They’re doing it now, quietly, and if that doesn’t catch your attention, nothing will.

Look, I can almost hear you muttering: “Why care about the kid in the corner when you’ve got NVIDIA’s big top show dominating the AI scene?” Remember, Microsoft (MSFT) purchased a staggering 485,000 of NVIDIA’s Hopper AI chips in 2024 alone.

That’s more than double what other US and Chinese competitors managed to grab. If that doesn’t underline NVIDIA’s chokehold on supply, what does?

Uber, which has pumped over $1 billion since 2015 into making cars steer themselves and optimize logistics, clearly plans for a future when autonomous everything is as common as a traffic jam in Manhattan. Linking up with this under-the-radar AI whiz kid might give Uber more finely tuned algorithms, more efficient fleets, and a fatter bottom line.

Still, there’s a lot of chatter right now. Everyone’s yapping about AI as if it’s the cure for every market hangover.

So let’s sink our teeth into something more satisfying: the numbers. The AI chip market, valued at about $53.7 billion in 2023, is expected to top $71 billion in 2024.

Meanwhile, the global autonomous vehicle market, already a hulking $1,500.3 billion in 2022, is forecast to explode to $13,632.4 billion by 2030. That’s a compound annual growth rate of 32.3%.

The direction is screamingly obvious. Everyone, including NVIDIA and Uber, wants a piece.

Their quiet investment in this tiny startup is a subtle tip-off that something big is percolating just beneath the surface.

I’m no stranger to turning over stones others ignore. Over the years, I’ve watched AMD (AMD) and Intel (INTC) spar behind the scenes.

AMD holds an 11% share of the data center AI chip market—tiny next to NVIDIA, yet not negligible. Intel, with a 22% chunk, tries to catch a train that’s already leaving the station.

Qualcomm (QCOM) works the edges with mobile processors and real-time capabilities but doesn’t challenge NVIDIA’s core dominance. TSMC (TSM) quietly churns out the silicon that keeps everyone’s ambitions alive.

Alphabet (GOOGL) and Microsoft, meanwhile, drive demand through software and R&D, pushing everyone toward more powerful chips. All that said, none of these players truly compare to NVIDIA’s entrenched position.

That’s what makes this move with Uber so telling. No one said the race would be tidy.

Companies bump elbows and occasionally whisper sweet nothings into a promising startup’s ear. NVIDIA wouldn’t bother unless it foresaw future profits.

Uber wouldn’t team up unless it sensed this tech could improve its bottom line.

I’ve seen this before. Early believers in a small disruptor can reap big rewards while skeptics mutter into their coffee.

This could be another one of those moments. For those who like to keep their fingers on the pulse, NVIDIA's position is so dominant that I'd still call it a buy.

AMD is worth picking up too, considering it's stubbornly carving out territory. Intel might need to show us more before getting a seat at the table.

TSMC is the machine that keeps the AI train rolling, making it a buy for anyone with a penchant for stable back-end players. Alphabet and Microsoft, both deeply committed to AI research and application, look like buys as well.

Uber, for all its ambition, stays at hold. I love the swagger, but let's see if it can turn these investments into real profits.

I’ve seen this story before. Early believers in small disruptors can reap big rewards while skeptics mutter into their coffee.

Sometimes the tastiest morsels come from watching which small fish the giants eye for dinner. And this little AI startup? It just might be the plankton that feeds the whales.

AI: The New Detective on the Block - How Artificial Intelligence is Cracking Unsolved Crimes

Mad Hedge AI

For decades, unsolved crimes have cast long shadows, leaving families and communities grappling with unanswered questions and a lingering sense of injustice. But a new era of crime-solving is dawning, one where artificial intelligence (AI) is stepping in to assist investigators in cracking cold cases that have baffled detectives for years.

From analyzing vast troves of data to identifying patterns invisible to the human eye, AI is proving to be a game-changer in the pursuit of justice. This technology is not just a futuristic fantasy; it's being deployed right now, helping to bring closure to victims' families and hold perpetrators accountable.

The AI Detective's Toolkit

AI's role in crime-solving is multifaceted, with algorithms and machine learning models being utilized in a variety of ways:

Facial Recognition: AI-powered facial recognition systems can analyze images and videos from crime scenes, comparing them to vast databases of faces to identify potential suspects. This technology has already been instrumental in solving cases where the only evidence was a grainy photograph or a fleeting glimpse of the perpetrator.
DNA Analysis: Traditional DNA analysis can be time-consuming and complex. AI algorithms can expedite this process, quickly identifying matches and predicting potential familial relationships, leading to breakthroughs in cases where DNA evidence is limited or degraded.
Predictive Policing: By analyzing crime data, AI can identify patterns and predict where future crimes are likely to occur. This allows law enforcement to allocate resources more effectively and potentially prevent crimes before they happen. While this application of AI raises ethical concerns about bias and profiling, its proponents argue that it can be a valuable tool for proactive policing.
Natural Language Processing (NLP): NLP algorithms can analyze vast quantities of text data, such as police reports, witness statements, and social media posts, to identify connections and extract crucial information that might have been overlooked by human investigators.
Pattern Identification: AI excels at identifying patterns and anomalies that humans might miss. By analyzing crime scene data, phone records, and other digital footprints, AI can uncover hidden connections and generate new leads.

Real-World Success Stories

The impact of AI in solving unsolved crimes is not just theoretical; it's already making a tangible difference:

The Case of the Golden State Killer: One of the most notorious cold cases in American history, the Golden State Killer case, was finally cracked in 2018 with the help of AI. Investigators used a genealogy website to compare DNA from crime scenes to publicly available genetic data, ultimately leading them to Joseph James DeAngelo, a former police officer who was convicted of 13 murders and dozens of rapes.
Identifying Child Predators: AI is being used to analyze online activity and identify individuals who are sharing or seeking child sexual abuse material. This technology can detect patterns of behavior and language that indicate predatory intent, helping law enforcement apprehend offenders and protect vulnerable children.
Cold Case Units: Law enforcement agencies across the country are establishing dedicated cold case units that utilize AI tools to re-examine old evidence and generate new leads. These units are bringing closure to families who have waited years for answers and ensuring that justice is served, no matter how much time has passed.

The Ethical Considerations

While the potential benefits of AI in crime-solving are undeniable, it's crucial to address the ethical considerations surrounding its use:

Bias and Discrimination: AI algorithms are trained on data, and if that data reflects existing biases, the algorithms can perpetuate those biases in their predictions and analyses. This can lead to unfair targeting of certain groups, particularly minorities who are already disproportionately represented in the criminal justice system.
Privacy Concerns: The use of facial recognition and other AI-powered surveillance technologies raises serious concerns about privacy and civil liberties. Striking the right balance between public safety and individual rights is a complex challenge that requires careful consideration.
Transparency and Accountability: It's important for AI algorithms used in crime-solving to be transparent and explainable. This allows investigators to understand how the AI arrived at its conclusions and ensures that the technology is not being used in a biased or discriminatory manner.

The Future of AI in Law Enforcement

The use of AI in law enforcement is still in its early stages, but its potential is vast. As technology continues to evolve, we can expect to see even more innovative applications of AI in the fight against crime:

Crime Scene Reconstruction: AI could be used to create virtual crime scenes, allowing investigators to explore different scenarios and test hypotheses in a safe and controlled environment.
Enhanced Interrogation Techniques: AI could analyze micro-expressions and other nonverbal cues during interrogations, helping investigators detect deception and extract more accurate information.
Personalized Risk Assessments: AI could be used to assess the risk of recidivism for individuals who have been convicted of crimes, helping to inform sentencing decisions and rehabilitation programs.

Conclusion

AI is transforming the landscape of crime-solving, offering new tools and capabilities that were once unimaginable. While it's essential to address the ethical concerns and ensure responsible use, the potential of AI to bring justice and closure to victims and their families is undeniable. As technology continues to advance, we can expect AI to play an even greater role in the pursuit of justice, ensuring that no crime goes unsolved and no perpetrator escapes accountability.

NVIDIA'S NEW NIGHTMARE IS SMALLER THAN THIS PERIOD.

Mad Hedge AI

(AMD), (NVDA), (GOOG), (MSFT), (META), (AMZN)

I was at MIT last month, watching a tiny roundworm wiggle around in a petri dish, when one of the researchers dropped a bombshell: "This creature," he said, pointing to something smaller than a period, "just helped us raise $250 million from AMD."

Welcome to the strange world of Liquid AI, where a worm with 302 neurons is teaching Silicon Valley how to build better artificial brains.

And by better, I mean cheaper, faster, and far more efficient.

Here's why I’m thrilled about this discovery: the AI industry has a dirty little secret. These massive language models everyone's obsessing over? They're burning through cash faster than a Tesla (TSLA) on ludicrous mode.

GPT-4 reportedly cost over $100 million just to train. The electricity bill alone would make your eyes water.

But Liquid AI has found a different path. By studying how that little worm processes information, they've created neural networks that slash computing costs by 90% while matching the performance of the big boys.

No wonder AMD (AMD) just wrote them a check that pushed their valuation north of $2 billion.

For AMD, this isn't just another tech bet. They're gunning for Nvidia (NVDA), who currently owns 80% of the AI chip market.

Last week, I spoke with some engineers at AMD who couldn't stop grinning about their new processors. They're talking about 5-10x better performance per watt compared to current GPUs.

With Nvidia reporting $18.1 billion in data center revenue for Q4 2023, you can see why AMD is excited.

The timing couldn't be perfect. Companies are getting tired of building massive GPU farms that consume enough power to light up Las Vegas.

Google's (GOOG) DeepMind is sweating bullets. Microsoft (MSFT) is scrambling to get this technology into Azure.

Intel (INTC) is still trying to figure out which end is up. And Meta (META)? They're too busy building digital playgrounds to notice.

Even Amazon's (AMZN) AWS, which powers about 40% of the world's large language models, sees the writing on the wall.

They've already poured $100 million into AI optimization research. When Amazon starts writing checks that big, you know something's coming.

Just yesterday, a hedge fund manager friend called me and asked if I'd lost my mind getting excited about a worm-inspired AI company.

I reminded him that in 2002, Sydney Brenner won the Nobel Prize for mapping that same worm's nervous system.

Back then, nobody imagined this research would spawn a multi-billion dollar AI company. But that's how innovation works - the biggest breakthroughs often come from the smallest places.

For those looking into the AI sector, the signals are clear. While Liquid AI remains private, keep your eyes on AMD, Microsoft, and Amazon.

They're positioning themselves for the next big shift in AI. When the efficiency revolution hits quarterly earnings reports, you'll want to be ahead of the curve.

What fascinates me most isn't just the technology - it's the irony. In an industry obsessed with building bigger, more powerful systems, the solution to AI's biggest problems might come from studying one of the simplest nervous systems in nature.

And I know the tech bears keep telling us AI valuations are too high, the bubble's about to pop, head for the hills. But they're missing the point.

The rules have changed. This is not just about building bigger AI anymore - it's about building smarter AI. And sometimes, as our tiny worm friend shows us, smarter means smaller.

Just don't tell that to Nvidia yet. Let them figure it out when their next power bill arrives.

MOVE OVER, LOCKHEED

Mad Hedge AI

(MSFT), (AMZN), (GOOG), (PLTR), (LMT)

Here's something that would have sounded crazy a few years ago: the US government has suddenly developed an expensive taste for artificial intelligence.

Not just a casual interest, mind you, but the kind of enthusiasm usually reserved for Pentagon hardware projects.

In fact, the Department of Defense is positioning itself to become the most eager AI customer since ChatGPT hit the scene.

While traditional defense giants like Lockheed Martin (LMT) still dominate military hardware contracts, Microsoft (MSFT), Amazon (AMZN), and Google (GOOG) are already expanding their AI data centers.

Though if you ask them directly, they'll probably just call it "infrastructure investment." That sounds better in shareholder meetings.

But the real story here is how companies like Palantir (PLTR) might hit the jackpot.

They've spent years building technology that makes government agencies salivate, and now their patience might finally pay off. The shift is so significant that even traditional defense contractors are probably wishing they'd paid more attention during those early AI briefings.

Speaking of unexpected winners, let's also talk about Tesla (TSLA).

While most electric vehicle companies are bracing for a rough ride under potential policy changes, Tesla's sitting pretty.

It's not just because Elon Musk has friends in high places - though that certainly doesn't hurt. Tesla's built the kind of manufacturing scale that makes government incentives nice to have but not essential for survival.

Add in some potential tariffs that might keep Chinese EVs at bay, and suddenly Tesla's position looks even stronger.

Let's take a quick look at Tesla's autonomous driving program - because this is where things get really interesting.

Internal timelines are getting more aggressive, especially as they're neck-and-neck with Chinese companies in the self-driving race.

When I look at the numbers, I almost have to do a double-take: we're talking about a potential $40-$50 jump in share price and a market cap cruising past $1 trillion if they roll out full autonomy in 2025.

Everyone seems to buy the story - Tesla stock recently shot up 14.8% to $288.53, and I suspect that's just the beginning.

The FTC situation adds another layer to this already complex cake. Their commissioner, Lina Khan, who's been giving tech executives headaches, might be heading for the exit.

The Street's betting that her departure could spark the kind of merger activity we haven't seen since AOL thought buying Time Warner was a good idea.

Meanwhile, Apple (AAPL) presents its own peculiar puzzle. Despite all the tough talk about 60%+ tariffs on Chinese goods, Tim Cook's empire might get special treatment.

It makes sense when you think about it - having 80% of your iPhone production in China tends to focus the mind wonderfully.

As for Amazon, they’re sweating bullets. Their marketplace sellers are more tied to Chinese manufacturing than a factory in Shenzhen.

Which brings us to semiconductors, where the story gets even more complex. Remember the CHIPS Act? It was supposed to be America's answer to Asia's chip dominance.

Now it's caught in political crosswinds, with House Speaker Mike Johnson performing the kind of policy reversal that would make an Olympic gymnast proud - first suggesting repeal, then backing away faster than a cat from a cucumber.

Even Trump jumped into the fray on Joe Rogan's show, calling the chip deal "so bad" it made his trade war look subtle.

His solution? Classic Trump: slap tariffs on imported chips until companies build factories here. It's like using a sledgehammer to hang a picture - effective, maybe, but subtle it ain't.

For those trying to navigate this policy maze, the message is clear: government AI initiatives aren't just another Washington fad. They're reshaping the tech landscape more dramatically than any single company could.

Whether you're looking at cloud providers, chip makers, or AI specialists, understanding these policy shifts isn't optional - it's essential.

I've been watching tech trends since before the iPhone was a twinkle in Steve Jobs' eye, and I'll tell you this much: when government and technology interests align, fortunes are made.

The last time I saw this level of government interest was during the early days of the internet. Back then, everyone focused on the technology. The smart money focused on who was getting the contracts.

Some things never change.