While reviewing earnings reports last week at my mountain cabin, I couldn't help but chuckle at the stream of "Google is dead" headlines flooding my inbox.
Having covered technological disruptions since the early days of the personal computer revolution, I've learned that paradigm shifts rarely happen overnight. The reality is far more nuanced.
The bears would have you believe that Google (GOOGL) is about to become the next Yahoo!, destined for the tech graveyard as AI chatbots eat its lunch.
After decades of watching tech giants rise and fall, I've developed a nose for distinguishing between genuine disruption and market hysteria. This feels a lot like the latter.
Let me share something that might surprise you: Google's search business is still growing even as ChatGPT and its AI cousins grab headlines.
We're looking at a $2.25 trillion behemoth with $95.66 billion in cash, trading at a better valuation than its big tech peers. Now THAT's what I call a disconnect between perception and reality.
Here's why the Google-is-dead crowd has it all wrong.
For one, Google isn't sitting on its hands. I've analyzed enough tech transitions to know the difference between a company adapting and one in denial.
After a brief deer-in-headlights moment when ChatGPT launched, they've gone full throttle into AI. The difference? Google can actually afford the AI arms race.
While OpenAI burns through cash faster than a Silicon Valley startup during the dot-com boom, Google generates enough free cash flow from its search business to fund its AI future.
It's like having a money printer to fund your R&D - something I wish every promising tech company had back when I was analyzing startups in the '80s.
But here's the kicker that most people miss: Google has THREE aces up its sleeve that nobody else can match.
First, they have an ecosystem that would make any tech company envious.
Google is on virtually every smartphone worldwide. They've got 8.5 billion daily searches, millions of YouTube uploads, and more data points than there are stars in the Milky Way.
Second, they have data quality that puts everyone else to shame.
While OpenAI is scrambling to buy training data (word is they're running out of public data to train on), Google's got a fresh firehose of high-quality, real-world information flowing in daily.
Third, they have cash flow that won't quit.
With a $95.66 billion war chest and money-printing core business, Google can outspend and outlast virtually any competitor.
Speaking of money, let's talk valuation.
Google's enterprise value sits at $2.18T, but here's what makes it interesting - it's actually cheaper than Microsoft on an EV/EBITDA basis.
The company's been buying back shares like they're going out of style, reducing the share count by 10% in just five years. That's a sneaky 2% annual return right there, before we even talk about price appreciation.
Sure, there are risks. New players like Perplexity are popping up faster than NFT projects in a bull market.
But having witnessed multiple tech cycles, I can tell you that unseating an incumbent with Google's advantages is about as easy as climbing Mount Everest in flip-flops.
Don't get me wrong - Google needs to execute.
Their CAPEX spending shows they're serious, but it's still below Meta (META) and Microsoft (MSFT) as a percentage of revenue. That might need to change.
But with search revenues still growing and AI integration accelerating, Google looks more like a phoenix than a dinosaur.
The bottom line? Google is a buy on dips. The death of search has been greatly exaggerated, and the company's positioning in AI is far stronger than the market realizes.
Where will Google be in five years? Nobody knows for sure, but I've got a strong hunch those AI-powered searches will be making us all look smarter while making Google shareholders richer.
Now, if you'll excuse me, I need to go check if my AI assistant can help analyze these quarterly earnings faster than I can. Some disruptions are worth embracing.
One of my hedge fund buddies called me last week about Super Micro Computer (SMCI), sounding more worried than I've heard him since the 2008 crash.
"What's the real story behind these missing financials?" he demanded.
I had to smile. After decades of watching companies navigate regulatory waters, I've learned that sometimes the best opportunities come disguised as paperwork problems.
And SMCI's numbers tell a fascinating story.
Let's dive into what matters. Their preliminary Q2 FY25 earnings show revenue of $5.6-5.7 billion – a staggering 54% growth from last year.
Yes, gross margins have dipped to 11.8-11.9% from 13.3%, but here's what the market is missing: their capacity utilization is only 55% in the US, 60% in Taiwan, and a mere 1% in Malaysia.
That's not a company struggling to meet demand – that's a coiled spring waiting to launch.
The regulatory drama? They finally found a new auditor, confirmed no restatement of prior financials is needed, and committed to filing their delayed reports – the FY24 10-K and Q1/Q2 FY25 10-Qs – by February 25, 2025.
The market's initial relief sent the stock up 6% in mid-February, but there's more upside here.
Follow the technology trail. SMCI just started full-scale production of NVIDIA's (NVDA) Blackwell Rack-Scale Solutions for the HGX B200 system. Their servers are already certified for NVIDIA H100 and H200 GPUs.
But here's the game-changer: liquid cooling technology.
With over 30% of new data centers expected to adopt liquid-cooling systems in the next twelve months, SMCI is positioned perfectly.
They've smartly raised $700 million through 2.25% convertible senior notes due 2028 to expand these capabilities.
I've seen plenty of tech companies come and go in my years covering the market, but SMCI's approach to the AI infrastructure challenge is different.
When you're running advanced AI workloads, traditional air cooling is like trying to cool a blast furnace with a desk fan. Their direct-liquid cooling technology gives them a significant edge as data centers struggle with power density challenges.
The numbers tell the story: while traditional air-cooled data centers typically support 15-20 kW per rack, liquid cooling can handle upwards of 100 kW.
That's the difference between hosting basic enterprise applications and running complex AI workloads.
Despite this technology, the valuation disconnect is striking.
SMCI trades at 12x forward earnings while NVIDIA commands 30x and Advanced Micro Devices (AMD) sits at 18x.
Yes, management revised down their FY25 revenue guidance from $26-30 billion to $23.5-25 billion, but they're still targeting $40 billion by FY26.
Having watched tech cycles for decades, I know ambitious targets when I see them – and these are actually achievable.
Why? The shift from AI training to inference workloads changes everything.
While training demands massive computing power, inference needs efficient, scalable solutions – exactly what SMCI provides.
Management projects 65% annual revenue growth through FY26, moderating to 30% as the market matures, then settling around 10% long-term.
Margins might only see 10 basis points of expansion due to competition, but the volume growth more than compensates.
For those wondering about timing, here's my take: The Special Committee found no accounting fraud or inappropriate revenue recognition.
This isn't an Enron with imaginary Nigerian barges and "special purpose entities" – it's just a filing delay from a company that actually makes real products that actually work.
Once SMCI meets the Nasdaq deadline – and my sources suggest they will – we would most likely see a significant re-rating of the stock.
So, I recommend that you keep a close watch of the next few weeks. In this market, companies solving real AI infrastructure problems don't stay discounted for long.
Just keep your position size reasonable – even the best tech plays require disciplined risk management.
As for me, I'll be tracking this one closely and will alert you the moment I see a clear entry point.
After all, in tech, today's paperwork problem often becomes tomorrow's profit engine. Just ask my old friend who panic-sold Microsoft (MSFT) in 1987 over their messy IPO filing.
You know you're getting old when you can remember Advanced Micro Devices (AMD) back when they were the scrappy underdog making Intel-compatible chips in Austin, Texas.
Back in my early trading days in the '80s, I watched AMD engineers reverse-engineer Intel's (INTC) latest processors with the dedication of medieval monks copying manuscripts.
Fast forward to today, and AMD's stock just got the kind of beating usually reserved for tech companies that forget to mention "AI" in their earnings calls.
We're talking about a drop from $227 to around $107 - a painful 53% decline that's enough to make even the most hardened tech trader wince.
The million-dollar question floating around my Lake Tahoe office this week: “Has the market lost its mind, or is this the kind of opportunity that makes careers?”
Let me break this down for you, and trust me, it gets interesting.
First, let's address the elephant in the server room - why was AMD trading at $227 in the first place? Simple: AI fever.
The same fever that had people buying pet rocks in the '70s and crypto tokens named after dogs in 2021. Expectations got so far ahead of reality that they were practically in a different zip code.
But here's where it gets juicy - AMD's data center revenue just surged 69% year-over-year to $3.9 billion in Q4. That's not a typo, and it's definitely not the kind of number you see from a company that's supposedly lost its mojo.
The division now accounts for 50% of 2024 sales, up from about as much as a rounding error a few years ago.
Speaking of numbers that make you do a double-take, AMD's forward P/E ratio has crashed from the nosebleed level of 40-50 last year to below 18 now.
The last time I saw a multiple compression this dramatic, I was watching the air leave my daughter’s birthday bouncy castle.
Still, here's something the doom-and-gloom crowd isn't telling you: AMD's pulling forward production of their MI350 series to mid-2025 due to strong customer demand.
When a company accelerates production in this environment, it's like seeing a restaurant with a line around the block - something good is cooking inside.
Sure, AMD's got challenges. Their AI GPU sales expectations for 2025 got trimmed back faster than my hedge during spring cleaning. The software side needs work - they're playing catch-up to NVIDIA (NVDA) in the AI space like I used to chase after my kids at Disneyland.
But here's the kicker: AMD's total data center sales could still hit $15-16 billion this year.
The client segment isn't exactly sitting on its hands either, posting a 52% year-over-year growth rate. We're looking at potential sales of $32-33 billion this year, possibly ramping up to $40-42 billion in 2026.
Now, am I saying AMD is risk-free? About as much as my morning coffee is calorie-free.
Obviously, they're facing serious competition from NVIDIA in AI and need to keep Intel at bay in traditional computing.
But at these prices? It's like finding a Ferrari with a Honda Civic price tag just because it needs new tires.
Looking ahead to 2030, I can see AMD's stock hitting $500 or higher. That's not just optimism talking - that's looking at the numbers and seeing a company trading at a modest 25-27 forward P/E multiple with substantial growth ahead.
For those tracking this stock, AMD reported in-line EPS of $1.09 on $7.7 billion in sales - a 24% year-over-year increase that beat expectations by $170 million.
Q1 guidance came in at $7.1 billion, above the Street's $6.99 billion estimate. Those aren't the numbers of a company in trouble; they're the numbers of a company in transition.
Is AMD oversold? The technicals certainly suggest so. The stock is about 33% below its 200-day moving average, which in technical analysis terms is like finding yourself in Death Valley when you meant to drive to San Francisco.
The RSI has stopped making new lows relative to the stock price - often a sign that the smart money is quietly accumulating positions.
The bottom line? AMD at $107 looks about as overvalued as a snow shovel in July. Sure, there might be more volatility ahead - this is tech, after all, not a savings bond.
But for those willing to look past the next quarter or two, AMD could be setting up for one of those moves that people talk about at investment conferences for years to come.
As for me, I'm heading back to Lake Tahoe this weekend. There's something about the clear mountain air that helps put market volatility in perspective.
That, and I hear there's a tech conference in Reno where a certain CPU maker might be making some interesting announcements.
Remember, in Silicon Valley, today's underdog is tomorrow's top dog. Just ask the folks who sold their AMD shares in 2015 for $2.
Be on the lookout for developments - this semiconductor story has more chapters ahead.
https://www.madhedgefundtrader.com/wp-content/uploads/2025/02/Screenshot-2025-02-19-165010.png672673Douglas Davenporthttps://madhedgefundtrader.com/wp-content/uploads/2019/05/cropped-mad-hedge-logo-transparent-192x192_f9578834168ba24df3eb53916a12c882.pngDouglas Davenport2025-02-19 16:51:382025-02-19 16:51:38SNOW SHOVELS IN JULY
Deepseek, a Chinese artificial intelligence (AI) company, has recently made headlines with its innovative AI model. The company claims that its model is more efficient and less expensive to train than its competitors. This has sent shockwaves through the stock market, with investors reacting to the potential implications of Deepseek's technology.
In this article, we will take a closer look at the effects of Deepseek's emergence on the stock market and its competitors. We will also discuss the potential implications of Deepseek's technology for the future of AI.
Short-Term Effects
The immediate effect of Deepseek's announcement was a sharp decline in the stock prices of major AI players. Nvidia, the leading provider of chips for AI training, saw its stock price fall by more than 10% in a single day. Other AI companies, such as Google and Microsoft, also saw their stock prices decline.
The market reacted to the possibility that Deepseek's technology could reduce the demand for Nvidia's products. If Deepseek's model is truly more efficient and less expensive to train, then companies may be less likely to purchase Nvidia's chips.
However, it is important to note that the stock market is often volatile in the short term. The decline in stock prices may simply be a reaction to the uncertainty surrounding Deepseek's technology. It is possible that the stock prices of AI companies will recover in the long run.
Long-Term Effects
The long-term effects of Deepseek's emergence are still uncertain. Some analysts believe that Deepseek's technology could lead to a more competitive AI market. This could benefit consumers in the long run, as companies would be forced to lower their prices and improve their products in order to compete.
Others are concerned that Deepseek's technology could give Chinese companies an advantage in the AI race. This could have implications for national security, as AI is becoming increasingly important in areas such as defense and surveillance.
It is also possible that Deepseek's technology could lead to the development of new AI applications. If Deepseek's model is truly more efficient and less expensive to train, then it could be used to develop AI models for a wider range of applications. This could lead to the development of new products and services that are powered by AI.
Deepseek's Competitors
Deepseek's emergence has put pressure on its competitors to innovate. Companies such as Google and Microsoft are now investing heavily in AI research and development in order to compete with Deepseek.
It is possible that Deepseek's competitors will be able to develop their own technologies that are as efficient and inexpensive as Deepseek's. However, it is also possible that Deepseek will be able to maintain its lead in the AI market.
The Future of AI
The emergence of Deepseek is a sign that the AI market is becoming increasingly competitive. This is good news for consumers, as it could lead to lower prices and better products.
However, it is also important to be aware of the potential risks of AI. AI is a powerful technology that could be used for both good and bad purposes. It is important to ensure that AI is developed and used in a responsible manner.
Conclusion
Deepseek's appearance on the scene has sent shockwaves through the stock market. The company's innovative AI model has the potential to disrupt the AI market. However, the long-term effects of Deepseek's emergence are still uncertain.
It is important to keep an eye on Deepseek and its competitors in the years to come. The future of AI is likely to be shaped by the companies that are able to develop the most innovative and efficient AI technologies.
Additional Points
It is important to note that Deepseek is a relatively new company. It remains to be seen whether the company will be able to maintain its lead in the AI market.
Deepseek's technology is still under development. It is possible that the company will make further improvements to its model in the future.
The AI market is constantly evolving. It is possible that new AI technologies will emerge in the future that are even more efficient and inexpensive than Deepseek's.
https://madhedgefundtrader.com/wp-content/uploads/2019/05/cropped-mad-hedge-logo-transparent-192x192_f9578834168ba24df3eb53916a12c882.png00Douglas Davenporthttps://madhedgefundtrader.com/wp-content/uploads/2019/05/cropped-mad-hedge-logo-transparent-192x192_f9578834168ba24df3eb53916a12c882.pngDouglas Davenport2025-02-14 16:39:342025-02-14 16:39:34Deepseek's Impact on the Stock Market and its Competitors
During my years covering the Japanese financial markets in the 1970s, I witnessed firsthand how technological breakthroughs could reshape entire economies.
Back then, it was the revolution in consumer electronics that turned companies like Sony (SONY) from small radio makers into global powerhouses.
Today, we're seeing something far more dramatic with artificial intelligence.
OpenAI is reportedly closing in on a $40 billion funding round led by SoftBank (SFTBY) at a mind-bending $300 billion valuation.
Having interviewed countless Asian tech pioneers over the decades, I've developed a nose for spotting the difference between genuine innovation and market hype. This one has elements of both.
To understand the scale, let’s put the numbers in perspective. Since October 2024, OpenAI has been adding about $1.1 billion in value every single day.
When I was covering the early days of Japan's tech boom, we thought NEC's $100 million monthly valuation growth was astronomical. OpenAI is doing that before lunch.
The company projects $3.7 billion in revenue for 2024, climbing to $11.6 billion by the end of 2025—a 213% growth rate that would make even the most aggressive Japanese keiretsu blush.
At a 25.8x forward revenue multiple, they're pricing this thing like it’s the next Toyota (TM), Sony, and Nintendo (NTDOY) combined.
Speaking of Japan, SoftBank’s involvement is worth noting. As the lead investor in this round, SoftBank is betting big yet again.
Masayoshi Son has built his empire on moonshot investments, but his track record is mixed. I remember when he was just starting out in software distribution—his ambition hasn’t wavered, though the scale of his bets has certainly grown.
A substantial portion of this funding will go toward Project Stargate, OpenAI’s massive initiative to build AI-optimized data centers across the US.
Controlling your own hardware destiny is critical, as I saw during Japan’s infrastructure boom in the ’70s, but it’s also fraught with risk. Stargate could give OpenAI a crucial edge, but it will burn through cash at an unprecedented rate.
The competitive landscape feels eerily familiar. An open-source competitor called DeepSeek has launched its r1 model, reportedly matching OpenAI’s capabilities at a fraction of the cost.
Watching this unfold is like reliving the rise of Linux (LLNXF) during the early PC wars. The key question is whether OpenAI can sustain its edge as the technology becomes more commoditized.
For those looking to play the AI boom through public markets, there are clear winners emerging alongside OpenAI.
Microsoft (MSFT), trading at roughly $412.22, remains OpenAI’s sugar daddy. It has poured over $13 billion into the company, securing first dibs on technology while monetizing infrastructure through Azure. It’s a dream setup, and I’ve been long on MSFT since the partnership was announced. I see no reason to change that position.
NVIDIA (NVDA), sitting at $133.57, has become the Intel (INTC) of the AI age. They’re selling the picks and shovels for this gold rush, and demand for their chips is so fierce that companies are reportedly paying premiums to secure supply. NVIDIA has been one of my favorite long-term holdings for years, and it keeps proving its worth.
Alphabet (GOOGL), trading at $186.47, is playing catch-up in the AI race. While they have the talent and the data to compete, their cloud business still lags behind Microsoft. Watching their quarterly cloud revenue growth will be key to assessing their progress.
Amazon (AMZN) at $233.14 is the sleeping giant. They’ve been quietly building AI infrastructure and leveraging their retail operation as a testing ground for AI applications. This dual strategy could make them the dark horse in this race.
Then there’s Taiwan Semiconductor (TSM) at $207.95. Having covered Asian tech markets for decades, I know TSMC’s manufacturing prowess is nearly impossible to replicate. They’re supplying the chips that power this revolution, making them one of the industry's kingmakers.
IBM (IBM) might seem old school at $249.27, but they’ve got deep enterprise relationships and a solid AI strategy. Sometimes the tortoise does beat the hare, especially when enterprise clients value reliability over hype.
AMD (AMD), trading at $110.48, is NVIDIA’s closest competitor in AI chips. My sources at AMD are bullish on their next-generation processors, and while they’ve yet to dethrone NVIDIA, they remain a strong contender.
Finally, there’s Baidu (BIDU) at $93.85, China’s AI leader. With geopolitical tensions mounting, however, I’m hesitant to bet heavily on Chinese tech right now. I’d rather stick with US and Taiwan-based players for more stability.
OpenAI’s $300 billion valuation might seem insane, but as I often say, the market can stay irrational longer than you can stay solvent. I’ve seen bubbles in Japanese real estate during the ’80s and the dot-com boom in the ’90s. The key is finding the companies that will survive when the music stops.
We can all agree that the AI revolution is real, but at these valuations, selectivity is crucial.
So, I’m holding my positions in Microsoft and NVIDIA while keeping some dry powder for better entry points in speculative names. When the market gives you a chance to buy these leaders at a discount, you need to be ready.
As for OpenAI’s valuation? Let’s just say I’ve seen this movie before in different languages. The technology is revolutionary, but the market’s enthusiasm feels like it’s running a few quarters ahead of reality.
And if history has taught me anything, it’s that selling shovels—like NVIDIA—tends to be a safer bet than digging for gold. Jensen Huang figured that out a long time ago.
I’ll be watching closely, hoping this doesn’t end like some of the high-flying Japanese tech stocks of the late ’80s. Those stories didn’t end well. But maybe, just maybe, this time it’s different.
Last weekend, I watched my daughter absolutely demolish me in a game of Go on her smartphone.
As I nursed my wounded pride with a cup of coffee, I couldn't help but smile - not because I lost, but because I remembered something remarkable that happened in 2016 that changed everything we thought we knew about artificial intelligence.
You see, back then, Google's (GOOG) AlphaGo made what became known as "move 37" against Go champion Lee Sedol. It was a move so bizarre, so seemingly nonsensical, that human experts thought it was a glitch.
Turns out, it was pure genius. That single move didn't just win a game - it showed us that AI could think in ways humans never imagined.
Fast forward to today, and I'm seeing something equally revolutionary happening in the AI space.
Just like AlphaGo's famous move, we're witnessing what I call the "chain-of-thought revolution," and it's about to reshape everything we know about AI investing.
Speaking of investing, Palantir (PLTR) has once again caught my attention lately, and not just because it's up 300% in 2024. There's something much bigger brewing here, and it reminds me of that pivotal AlphaGo moment.
Let me break down why this matters.
Remember how the later version of AlphaGo, called AlphaGo Zero, absolutely crushed its predecessor? Here's the kicker - it did it by completely ignoring human knowledge.
That means pure machine learning, no human training wheels needed. This isn't just some tech trivia - it's a blueprint for what's happening right now in the AI industry.
Through recent breakthroughs in what's called "chain-of-thought" processing and reinforcement learning (RL), we're seeing AI models that can actually improve themselves.
Think of it like a digital version of compound interest, but for intelligence. OpenAI's "o" series and DeepSeek-R1 are already showing us glimpses of this future.
Why is this important to us? Because we're approaching what AI researchers call a "hard takeoff" – a moment when AI capabilities could improve exponentially.
And just like buying Amazon (AMZN) in the early days of e-commerce, positioning yourself correctly now could be life-changing.
This brings us back to Palantir, which reported Q4 revenue of $828 million, up 36% year-over-year.
Their U.S. commercial revenue jumped 54%, and government revenue grew 45%.
But here's what really got my attention - they achieved this with a 45% adjusted operating margin. Now, that's the kind of margin most software companies only dream about.
The company is projecting $3.75 billion in revenue for 2025, representing 31% annual growth.
Sure, at $236 billion market cap and a P/E of 525, it looks expensive. But so did Microsoft (MSFT) when it first started dominating the PC market.
Here's why I think Palantir is uniquely positioned.
First, they've built what I call the "infrastructure for intelligence" - systems that can deploy these new self-improving AI models securely and at scale. It's like owning the railroad tracks during the steam engine revolution.
Second, their government contracts provide stable cash flow while their commercial business offers explosive growth potential. It's a rare combination that reminds me of early AWS.
Third, and most importantly, they're perfectly positioned to benefit from the chain-of-thought revolution. While others are still figuring out how to make AI work in the real world, Palantir already has the plumbing in place.
Now, let's talk risks because I've been around long enough to know nothing is a sure bet.
Competition is fierce - Microsoft, Google, and an army of well-funded startups are all fighting for a piece of the pie. The valuation is steep, and any slowdown in growth could hit the stock hard.
But here's what keeps me bullish: Unlike companies building the AI models themselves (which become commoditized quickly), Palantir operates on the application layer.
They're not selling picks and shovels during the gold rush - they're building the entire mining infrastructure.
Think about it this way: When I was learning to code in the early days of the internet, we were writing basic HTML.
Today, my kid who beat me at Go is creating AI agents that can write their own code. That's the kind of exponential progress we're seeing, and Palantir is right at the center of it.
At its current valuation, Palantir might look scary. But remember what happened when we doubted Tesla's (TSLA) valuation? Sometimes, the market prices in the future before most investors can see it.
Just like AlphaGo's "move 37" seemed crazy until it proved brilliant, investing in Palantir at these levels might seem nuts to some.
But when you understand the technological revolution happening under the surface - this chain-of-thought AI breakthrough combined with reinforcement learning - the potential becomes clear.
Just like in Go, sometimes the winning move in investing isn't the obvious one.
And right now, while others are still learning the rules of the AI game, I'm putting my money where my mouth is and making my move with Palantir on dips.
Last weekend, while cleaning up my home office, I came across an old Intel 486 processor I'd kept as a memento from my first custom-built PC.
Funny how things change - that chip had about 1.2 million transistors. Today's AI accelerator chips? We're talking billions.
This old relic got me thinking about Broadcom (AVGO) and the recent market hysteria over AI chip competition.
Speaking of hysteria, let me tell you about the market's latest panic attack. When news broke about DeepSeek's supposedly cheaper-to-train Chinese language model, investors acted like someone had just announced the death of AI.
Over $1 trillion in market cap vanished faster than a plate of cookies at a board meeting.
And here's the kicker: DeepSeek reportedly spent just $5.6M on training compared to Google (GOOG) DeepMind's Gemini at $191M and OpenAI's GPT-4 at $78M.
Broadcom took a nasty hit in this selloff, dropping 17.3% at its worst.
That's quite a haircut for a company that just reported AI-related revenues of $12.2B - a whopping 41% of their semiconductor business in FY2024. For more context, that's up 21 percentage points year-over-year.
But here's where it gets curious. While having lunch with a semiconductor industry veteran the other day, he couldn't stop talking about Broadcom's custom ASIC business.
These aren't your garden-variety chips - they're custom-designed AI accelerators for the likes of Google, Meta (META), and Amazon (AMZN). And guess what? All these companies are ramping up their AI spending, not cutting back.
The numbers tell an intriguing story. Taiwan Semiconductor Manufacturing Company (TSM), the world's leading chip manufacturer, reports that their advanced 3nm and 5nm chips now represent 60% of revenue, up 8 points quarter-over-quarter and 10 points year-over-year.
That's not the trajectory of a dying industry - that's a growth story with legs.
Want to talk about margins? NVIDIA (NVDA) has been enjoying gross margins of 75% in their latest quarter, up from 61.2% in FY2019, though down a bit from their peak of 78.4%.
When you're making margins like that, you're practically printing money. No wonder hyperscalers are looking at custom ASICs as an alternative - and that's where Broadcom shines.
Looking ahead, analysts expect Broadcom to grow revenue and earnings at a CAGR of 16.3% and 23.1% through FY2027.
That's not just impressive - it's an acceleration from their already robust historical growth of 17.9% and 18% between FY2019 and FY2024.
The stock isn't exactly cheap at 34.85x forward earnings, up from its 5-year mean of 20.11x.
But in the context of the sector, with a forward PEG ratio of 1.69x compared to the sector median of 1.82x, it's still digestible.
NVIDIA, by comparison, trades at 40.66x forward earnings with a PEG ratio of 1.07x.
Yes, the dividend yield has dropped to 1.07% from its 5-year average of 2.76%, but that's what happens when your stock becomes a market darling.
Short sellers seem to agree - they've reduced their bets against Broadcom by 7.9% compared to last year.
Here's my bottom line: The market's reaction to DeepSeek looks like a classic case of throwing the baby out with the bathwater.
Broadcom isn't just riding the AI wave - they're helping build the surfboard. Their custom ASIC business is perfectly positioned as tech giants look to optimize their AI infrastructure costs.
That old 486 processor sitting on my desk reminds me of an important lesson: in tech, it's not about where we've been, but where we're going.
And Broadcom? They're headed toward the next generation of AI chips, with volume shipments of 3nm ASICs scheduled for the second half of fiscal 2025.
For now, I'm calling this one a Buy on any pullbacks. Sometimes the market hands you a gift wrapped in panic - this might be one of those times.
The insurance industry is undergoing a seismic shift as artificial intelligence (AI) transforms the way insurers assess and manage risks associated with properties, particularly those vulnerable to natural disasters, climate change, and other hazards. From predictive analytics to real-time monitoring, AI is enabling insurers to make faster, more accurate, and data-driven decisions, helping them navigate an increasingly complex risk landscape.
The Growing Need for Advanced Risk Assessment
Climate Change and Natural Disasters
Climate change is driving a surge in the frequency and severity of natural disasters, including hurricanes, wildfires, floods, and earthquakes. According to the National Oceanic and Atmospheric Administration (NOAA), the number of billion-dollar weather and climate disasters in the U.S. has risen dramatically over the past few decades. This trend is global, leaving insurers grappling with higher claims and greater financial exposure.
Traditional risk assessment methods, which rely heavily on historical data, are struggling to keep pace with these evolving risks. AI, however, offers a solution by analyzing vast amounts of real-time data and identifying patterns that traditional methods might miss.
Urbanization and Property Density
Urbanization is compounding the problem, with more properties being built in disaster-prone areas. Coastal cities, for instance, face heightened risks from hurricanes and rising sea levels. The increasing density of properties in these regions means that a single catastrophic event can result in massive losses for insurers.
AI is helping insurers better understand these risks by integrating data from satellite imagery, weather forecasts, and building codes. This allows for more informed underwriting and pricing decisions, ensuring that insurers can manage their exposure effectively.
Regulatory and Consumer Pressures
Regulators and consumers are demanding greater transparency and accuracy in risk assessment. Insurers are under pressure to offer affordable yet comprehensive policies while maintaining financial stability to pay out claims. AI is helping insurers meet these demands by providing more precise risk assessments, enabling better pricing and underwriting decisions, and ensuring compliance with regulatory requirements.
How AI is Transforming Risk Assessment
Data Collection and Integration
AI excels at collecting and integrating data from diverse sources, including:
Satellite Imagery: AI analyzes satellite images to assess property conditions, identify hazards, and monitor changes over time, such as erosion or deforestation.
Weather Data: Real-time weather data from satellites, IoT devices, and weather stations helps insurers predict extreme weather events and their potential impact.
Social Media and News Feeds: AI scans social media and news articles to identify emerging risks like wildfires or civil unrest.
Building and Infrastructure Data: AI evaluates building materials, construction methods, and infrastructure to assess vulnerability to hazards.
Historical Claims Data: AI identifies patterns in past claims to predict future risks.
By integrating these data sources, AI provides a comprehensive and accurate risk assessment for each property.
Predictive Analytics
Predictive analytics is one of AI's most powerful tools. By analyzing historical data, AI can forecast the likelihood of future events and their potential impact. For example, AI can predict hurricane landfalls and estimate property damage based on factors like wind speed, storm surge, and building resilience. This allows insurers to adjust premiums, recommend mitigation measures, and prepare for potential claims.
AI is also being used to assess long-term climate risks, such as rising sea levels and changing precipitation patterns, helping insurers plan for future challenges.
Machine Learning and Risk Modeling
Machine learning algorithms analyze large datasets to identify complex relationships between variables, enabling the development of sophisticated risk models. These models consider factors like geographic location, building characteristics, and environmental conditions, and are continuously updated with new data.
For example, machine learning can identify properties at higher risk of water damage due to flooding or plumbing issues, allowing insurers to adjust premiums or recommend specific mitigation measures.
Real-Time Monitoring and Alerts
AI enables real-time monitoring of properties through IoT sensors that track conditions like temperature, humidity, and water levels. If a sensor detects a potential hazard, such as a sudden increase in water levels, the system can alert both the insurer and the property owner.
AI also assesses the impact of natural disasters as they unfold by analyzing data from social media, news feeds, and satellite imagery. This helps insurers prioritize claims and allocate resources more effectively.
Automated Underwriting and Pricing
AI automates underwriting and pricing by analyzing property data to determine appropriate premiums and coverage. It can also flag high-risk properties for further review, ensuring that underwriters focus on the most complex cases.
Customer Engagement and Risk Mitigation
AI-powered chatbots provide policyholders with personalized recommendations on reducing risks, such as maintaining properties or installing protective measures. AI also delivers real-time updates on emerging risks, such as approaching wildfires, helping policyholders take proactive steps to protect their properties.
Case Studies: AI in Action
Lemonade: AI-Powered Insurance
Lemonade, a tech-driven insurer, uses AI to assess risks and process claims in real-time. Its AI system analyzes property data to determine premiums and coverage, while its chatbot, Maya, engages with customers, answers questions, and even helps file claims.
Zurich Insurance: AI for Flood Risk Assessment
Zurich Insurance has developed an AI-powered flood risk assessment tool that uses satellite imagery, weather data, and machine learning to predict flooding likelihood and potential damage. The tool helps underwriters assess risks and provides policyholders with mitigation recommendations.
Allstate: AI for Wildfire Risk Assessment
Allstate's AI tool predicts wildfire risks by analyzing factors like temperature, humidity, wind speed, and vegetation density. It helps underwriters evaluate properties in wildfire-prone areas and provides real-time updates to policyholders.
Challenges and Ethical Considerations
Data Privacy and Security
The use of AI requires collecting and analyzing vast amounts of sensitive data. Insurers must implement robust data protection measures to safeguard this information and comply with privacy regulations.
Bias and Fairness
AI systems can produce biased results if trained on unrepresentative data. Insurers must ensure their AI models are trained on diverse datasets to avoid bias and ensure fairness.
Transparency and Explainability
The complexity of AI algorithms can make it difficult to explain how risk assessments are made. Insurers must prioritize transparency to build trust with regulators and policyholders.
Regulatory Compliance
AI-driven risk assessment must comply with regulations on data privacy, fairness, and transparency. Insurers must stay ahead of evolving regulatory requirements to avoid legal and reputational risks.
The Future of AI in Risk Assessment
Integration with IoT and Smart Homes
The integration of AI with IoT devices and smart home technology will enhance real-time risk monitoring. Smart sensors can detect leaks, smoke, or unusual activity, helping prevent damage and reduce claims.
AI-Driven Climate Risk Models
As climate change intensifies, insurers will rely on AI-driven climate risk models to assess long-term risks and develop strategies to mitigate them.
Collaboration with Governments and NGOs
Insurers are increasingly partnering with governments and NGOs to address climate risks. AI provides the data needed to develop effective policies and mitigation strategies.
Personalized Insurance Products
AI enables insurers to offer tailored policies based on specific property risks, such as flood or wildfire coverage, ensuring that policyholders receive the protection they need.
Conclusion
AI is revolutionizing the insurance industry by enabling more accurate, efficient, and scalable risk assessment. From predictive analytics to real-time monitoring, AI is helping insurers navigate the growing risks posed by climate change and natural disasters. While challenges remain, the potential benefits of AI are immense, promising a more resilient and sustainable future for the insurance industry. As AI technology continues to evolve, its role in risk assessment will only grow, reshaping the industry for years to come.
https://www.madhedgefundtrader.com/wp-content/uploads/2025/02/Screenshot-2025-02-03-164141.png587914Douglas Davenporthttps://madhedgefundtrader.com/wp-content/uploads/2019/05/cropped-mad-hedge-logo-transparent-192x192_f9578834168ba24df3eb53916a12c882.pngDouglas Davenport2025-02-03 16:47:562025-02-03 16:49:04How AI is Revolutionizing Risk Assessment in the Insurance Industry
Back in 1976, during one of my assignments, a senior Chinese economist offered me a cup of tea and shared something Deng Xiaoping had once told them—words I’ve never forgotten: "It doesn't matter if the cat is black or white, as long as it catches mice."
I found myself thinking about that conversation last week as I watched a small Chinese AI company, DeepSeek, snatch $1.2 trillion worth of mice right out from under Silicon Valley’s nose.
Earlier this month, a small Chinese AI lab called DeepSeek managed to vaporize $1.2 trillion in market value by doing something rather inconvenient: proving you don't need billions to build competitive AI models.
Their founder, Liam Wenfeng, probably wasn't trying to start a panic. He just wanted to show that his team could match OpenAI's capabilities at 5% of the cost.
The market reaction was swift and brutal. Nvidia (NVDA), everyone's favorite AI golden child, watched its stock plummet 17% in early trading.
The tremors hit the entire tech sector: Microsoft (MSFT) down 3.5%, Alphabet (GOOG) dropped 3%, and Amazon (AMZN) shed 2.4%.
Even Meta (META) took a 1.4% hit. Apple (AAPL), being Apple, somehow managed to gain 1.2%. There's always one kid in class who has to be different.
Let's talk about what DeepSeek actually did. Their R1 model, built for a mere $5.57 million using Nvidia's H800 chips, is matching OpenAI's GPT-4 in math, coding, and reasoning benchmarks.
They used pure reinforcement learning - basically letting the AI figure things out on its own rather than holding its hand through the process. And it worked.
The timing is almost comical. Just as OpenAI's Sam Altman was at the White House announcing the $500 billion Stargate Project, DeepSeek showed up with their bargain-basement solution that performs just as well.
Even Nvidia had to acknowledge the achievement, calling it an "excellent AI advancement." When your competitors start complimenting you, you know you've struck a nerve.
But here's what Wall Street might be missing: this isn't just about cost reduction.
DeepSeek released their model under an MIT license, meaning anyone can study, modify, and expand it. They're not just competing - they're changing the rules of the game.
What should we do? First, take a deep breath.
Despite this disruption, the fact remains that the Magnificent 7 and U.S. tech companies are playing a longer game, focused on artificial general intelligence with an ecosystem that DeepSeek "cannot come close to." This could actually increase demand for computing resources - cheaper AI often leads to more AI usage, not less.
The $2 trillion of capital expenditure expected over the next three years isn't going away. If anything, this development might accelerate it.
When technology gets cheaper, people tend to use more of it, not less. Just ask anyone who remembers when long-distance calls cost a fortune.
For investors, this looks more like a buying opportunity than a reason to panic. I've seen enough market disruptions to know that the initial reaction is usually overdone.
The AI infrastructure build-out is just getting started, and cheaper development costs could actually expand the market rather than shrink it.
Keep your eyes on DeepSeek, though. The tech giants will need to adapt - either by making their own processes more efficient or by finding new ways to add value. Competition has a funny way of improving everyone's game.
And somewhere in Beijing, I imagine there's a tech executive reciting that old proverb about cats and mice, knowing they just caught the biggest mouse of all - Wall Street's attention.
Some market lessons never get old, even if the cats keep changing their colors.
https://www.madhedgefundtrader.com/wp-content/uploads/2025/01/Screenshot-2025-01-31-163709.png591665Douglas Davenporthttps://madhedgefundtrader.com/wp-content/uploads/2019/05/cropped-mad-hedge-logo-transparent-192x192_f9578834168ba24df3eb53916a12c882.pngDouglas Davenport2025-01-31 16:38:012025-01-31 16:38:33BLACK CAT, WHITE CAT, RED STOCKS
At 5 AM, my phone lit up with texts from three hedge fund managers I know, all asking the same thing: "Is this Stargate thing for real?"
Honestly, I wasn’t even surprised. The messages rolled in just a day after Trump unveiled what could be the mother of all tech initiatives: a $500 billion AI infrastructure project dubbed "Stargate," with heavyweights like OpenAI's Sam Altman, SoftBank's (SFTBY) Masayoshi Son, and Oracle's (ORCL) Larry Ellison standing by his side.
But before we get carried away with the headlines, let's look at what really matters to us.
First, some context: The global AI infrastructure market was just $38.1 billion in 2023. That makes this initiative 13 times bigger than the entire current market.
If you're wondering why tech stocks popped on the news, there's your answer.
The semiconductor plays here are particularly compelling. NVIDIA (NVDA) is still trading at under 20X earnings despite 60% growth - a valuation that looks increasingly disconnected from reality given recent developments.
Morgan Stanley's latest channel checks show Blackwell chips are fully sold out for the next 12 months before production even begins, with "several billion dollars" in revenue expected in Q4 FY25 alone.
What's really getting my attention is the GB200 NVL72 system specifications.
It enables up to 72 GPUs to be connected via NVLink, acting as a single GPU with aggregate bandwidth of 259 terabytes per second - about 10 times higher than Hopper.
The implications for data center deployments are staggering.
Speaking of data centers, Oracle has already broken ground on their first Texas facility. It's a million square feet, and they're planning 20 more just like it.
Their stock jumped 8% on the announcement, but here's what most analysts missed: each facility requires approximately 1 gigawatt of power.
This is roughly equivalent to a mid-sized nuclear plant. That's not just a lot of power – that's "Back to the Future" DeLorean levels of energy consumption.
Looking at these numbers made me realize that the energy stocks might just be the sleeper opportunity here.
AI queries consume 3-36 times more energy than traditional searches, and current projections show AI consuming up to 19% of U.S. data center power by 2028.
This creates a compelling case for utilities positioned to serve this growing demand.
Constellation Energy (CEG) stands out in this space. They're already producing about 10% of the nation's emission-free energy, with CO2 emissions 4.5 times lower than NextEra (NEE).
Their recent 20-year Microsoft (MSFT) deal for data center operations is just the beginning. The $840 million government contract they just landed provides exactly the kind of revenue certainty I look for in utility plays.
Vistra Corp (VST) deserves more attention than it's getting. Their dominant position in the Electric Reliability Council of Texas (ERCOT) – where most of these new facilities will be built – puts them in prime position.
The ERCOT market is projected to see 5% annual demand growth through 2030. With their recent $6.8 billion Energy Harbor acquisition, they're now the second-largest nuclear operator in the country.
Meanwhile, Taiwan Semiconductor's (TSM) position here is crucial.
Reports project that we'll need 1.2 to 3.6 million additional wafers by 2030, requiring 3-18 new fabrication plants.
The strategic importance of this manufacturing capacity has already been seen - through Broadcom (AVGO), TSMC has secured manufacturing slots for OpenAI's first custom chip targeting 2026.
This semiconductor build-out is part of a larger global race for AI dominance. OpenAI's recent policy white paper estimates "$175 billion in global funds awaiting investment in AI projects."
Their warning is clear: if these funds don't land in U.S. projects, they'll flow to China-backed initiatives instead.
Now, let's talk about what could go wrong.
The infrastructure constraints are real - Texas's power grid can barely handle summer AC demand as it is.
Water usage for cooling these facilities is another major concern, especially given Texas's history with water scarcity.
We should also consider execution risk.
Trump's track record with big tech announcements is mixed - remember the 2017 Foxconn promise of a $10 billion Wisconsin factory that ended up as a scaled-down $672 million project?
This history of grand announcements versus actual delivery adds weight to current skepticism.
On top of these, Anthropic's CEO Dario Amodei called this plan "a bit chaotic" (tech exec speak for "What are they smoking?"), and Elon Musk took to X to throw shade at SoftBank's funding claims.
Still, the market seems to be ignoring these risks.
When I mentioned them to a tech CEO friend last night, he just shrugged and said "they'll figure it out." Maybe, but I'm watching the ERCOT capacity numbers like a hawk.
And before I forget, keep your eye on Broadcom too.
Their inference chip strategy, led by those Google (GOOG) TPU veterans, could be the dark horse here. While everyone's focused on training chips, the real volume play might be in inference.
For now, I'm holding steady with modest long positions in companies directly benefiting from this infrastructure buildout.
But in Texas, where everything is bigger, so are the opportunities—and the risks. The Volatility Index sitting at $12 tells me it's time to dig deeper.
https://madhedgefundtrader.com/wp-content/uploads/2019/05/cropped-mad-hedge-logo-transparent-192x192_f9578834168ba24df3eb53916a12c882.png00Douglas Davenporthttps://madhedgefundtrader.com/wp-content/uploads/2019/05/cropped-mad-hedge-logo-transparent-192x192_f9578834168ba24df3eb53916a12c882.pngDouglas Davenport2025-01-29 16:45:332025-01-29 16:45:33EVERYTHING IS BIGGER IN TEXAS
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