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Fiscal.ai
Wix is currently trading at its cheapest FCF multiple ever as a public company.
EV/FCF: 11.5x
$WIX https://t.co/wC5pmuHBwj
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Wix is currently trading at its cheapest FCF multiple ever as a public company.
EV/FCF: 11.5x
$WIX https://t.co/wC5pmuHBwj
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Fiscal.ai
Zoom is now buying back more stock than it ever has.
$1.65B in buybacks over the last 12 months.
That's equal to 6.9% of their current market cap.
$ZM https://t.co/fUfLeF2tF9
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Zoom is now buying back more stock than it ever has.
$1.65B in buybacks over the last 12 months.
That's equal to 6.9% of their current market cap.
$ZM https://t.co/fUfLeF2tF9
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App Economy Insights
$ZM Zoom Q3 FY26 (October quarter).
• Customers > $100K TTM +9% Y/Y to 4,364.
• Revenue +4% Y/Y to $1.2B ($20M beat).
• Non-GAAP EPS $1.52 ($0.08 beat).
• Investment gains likely from Anthropic.
• FY26 FCF outlook ~$1.9B ($110M raise). https://t.co/MTAinso4Jo
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$ZM Zoom Q3 FY26 (October quarter).
• Customers > $100K TTM +9% Y/Y to 4,364.
• Revenue +4% Y/Y to $1.2B ($20M beat).
• Non-GAAP EPS $1.52 ($0.08 beat).
• Investment gains likely from Anthropic.
• FY26 FCF outlook ~$1.9B ($110M raise). https://t.co/MTAinso4Jo
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WealthyReadings
RT @WealthyReadings: 🚨BREAKING: The $610 Billion AI Ponzi Scheme Is Not A Ponzi Scheme
Here’s why $NVDA isn’t the disaster the algorithms - and the bears, want you to think it is. Far from it.
Shanaka’s argument claims that Nvidia’s rising inventory, receivables, and DSO suggest demand is slowing and the company is pushing more product than customers can absorb, in terms of need and payment.
In brief: no more demand nor cash to pay for their GPUs.
1. Rising Inventory ≠ Red Flag
Shanaka says rising inventory is evidence of weak demand, but ignores $NVDA pricing - and many other factors we'll talk about.
When unit prices double or triple, the same volume of hardware shows up as a larger dollar value in inventories.
You'll have more bananas for $1M that airplanes, right? Just like you'll have more H100 than GB200.
When we normalize inventory by revenue - or by units shipped, the trend is stable, suggesting this is a pricing effect, not a demand problem and rising inventory in volume.
This can also be illustrated with accounts receivable per revenue, which make the same point: when product prices increase, dollar-denominated metrics rise, so metrics taken individually may look bad but within context, the story looks normal.
That being said, many could point that even then, inventory is rising. To which we need to add context, something algorythms are incapable of.
2. Higher DSO & Supply Chain Constraints
DSO - which represents the time before being paid, rising slightly is consistent with real-world constraints.
$NVDA doesn’t just ship GPUs anymore; they ship racks, custom configurations, integrated systems… These use third-party components, which require more coordination, harder logistics, and can temporarily increase time before revenue recognition and therefore inventory.
Add to this the fact that foundries, as proven many times these quarters during $TSM & co earnings, run at full capacity, and you get even more delays.
More customization + constrained supply chains = longer installation cycles before revenue can be recognized and rising inventories until then.
This is an operational bottleneck, not a credit problem.
A move from 46 to 53 days is marginal especially considering this value has been roughly stable for three quarters.
3. Circular Economy
As for the claims about a circular economy and the same dollars being used across multiple companies, I have no counters but this: circular economies are normal, that’s how economies work.
It only becomes a problem if AI services do not generate enough cash to honor commitments.
Because that’s what those are: commitments, not booked revenues. If those commitments can be honored, then what is the problem?
4. Algorithms Don’t Understand Context
Shanaka claims that this was thankfully found by algorithm - and I can agree with him based on the market's behaviour and violence. But he forgets that algorythm are built to find fraud in 99% of cases.
But $NVDA is the 1%.
When revenue grows 60–80% YoY, it's normal for inventories, receivables, and payables to grow at least comparably in dollar terms. Maybe even slightly higher when added real-world constraints.
What matters is whether these metrics grow disproportionately relative to revenue.
And once normalized, $NVDA ratios are stable, which is consistent with a rapid ongoing expansion, not accounting games or demand collapse.
That being said, everything isn’t necessarily perfect. But again: algorithms are configured to gauge 99% of the market, so of course the 1% will raise red flags.
Add some organic grey cells, context and reality, and the picture is very different, even if the stock continues to fall.
The market is about emotions, not rationality. And X is great at sharing emotions, less for rationality.
Conclusion.
I might be proven wrong in time and $NVDA might be an accounting fraud. I personally continue to believe in the AI revolution, have my own concerns a[...]
RT @WealthyReadings: 🚨BREAKING: The $610 Billion AI Ponzi Scheme Is Not A Ponzi Scheme
Here’s why $NVDA isn’t the disaster the algorithms - and the bears, want you to think it is. Far from it.
Shanaka’s argument claims that Nvidia’s rising inventory, receivables, and DSO suggest demand is slowing and the company is pushing more product than customers can absorb, in terms of need and payment.
In brief: no more demand nor cash to pay for their GPUs.
1. Rising Inventory ≠ Red Flag
Shanaka says rising inventory is evidence of weak demand, but ignores $NVDA pricing - and many other factors we'll talk about.
When unit prices double or triple, the same volume of hardware shows up as a larger dollar value in inventories.
You'll have more bananas for $1M that airplanes, right? Just like you'll have more H100 than GB200.
When we normalize inventory by revenue - or by units shipped, the trend is stable, suggesting this is a pricing effect, not a demand problem and rising inventory in volume.
This can also be illustrated with accounts receivable per revenue, which make the same point: when product prices increase, dollar-denominated metrics rise, so metrics taken individually may look bad but within context, the story looks normal.
That being said, many could point that even then, inventory is rising. To which we need to add context, something algorythms are incapable of.
2. Higher DSO & Supply Chain Constraints
DSO - which represents the time before being paid, rising slightly is consistent with real-world constraints.
$NVDA doesn’t just ship GPUs anymore; they ship racks, custom configurations, integrated systems… These use third-party components, which require more coordination, harder logistics, and can temporarily increase time before revenue recognition and therefore inventory.
Add to this the fact that foundries, as proven many times these quarters during $TSM & co earnings, run at full capacity, and you get even more delays.
More customization + constrained supply chains = longer installation cycles before revenue can be recognized and rising inventories until then.
This is an operational bottleneck, not a credit problem.
A move from 46 to 53 days is marginal especially considering this value has been roughly stable for three quarters.
3. Circular Economy
As for the claims about a circular economy and the same dollars being used across multiple companies, I have no counters but this: circular economies are normal, that’s how economies work.
It only becomes a problem if AI services do not generate enough cash to honor commitments.
Because that’s what those are: commitments, not booked revenues. If those commitments can be honored, then what is the problem?
4. Algorithms Don’t Understand Context
Shanaka claims that this was thankfully found by algorithm - and I can agree with him based on the market's behaviour and violence. But he forgets that algorythm are built to find fraud in 99% of cases.
But $NVDA is the 1%.
When revenue grows 60–80% YoY, it's normal for inventories, receivables, and payables to grow at least comparably in dollar terms. Maybe even slightly higher when added real-world constraints.
What matters is whether these metrics grow disproportionately relative to revenue.
And once normalized, $NVDA ratios are stable, which is consistent with a rapid ongoing expansion, not accounting games or demand collapse.
That being said, everything isn’t necessarily perfect. But again: algorithms are configured to gauge 99% of the market, so of course the 1% will raise red flags.
Add some organic grey cells, context and reality, and the picture is very different, even if the stock continues to fall.
The market is about emotions, not rationality. And X is great at sharing emotions, less for rationality.
Conclusion.
I might be proven wrong in time and $NVDA might be an accounting fraud. I personally continue to believe in the AI revolution, have my own concerns a[...]
Offshore
WealthyReadings RT @WealthyReadings: 🚨BREAKING: The $610 Billion AI Ponzi Scheme Is Not A Ponzi Scheme Here’s why $NVDA isn’t the disaster the algorithms - and the bears, want you to think it is. Far from it. Shanaka’s argument claims that Nvidia’s rising…
bout the circular economy but did not find any indications that AI won't yield cash flow and that commitments can't be honored as of today.
I continue to be bullish. And shared all my moves and reasoning with subscribers yesterday.
The future is bright for those with a system.
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I continue to be bullish. And shared all my moves and reasoning with subscribers yesterday.
The future is bright for those with a system.
BREAKING: The $610 Billion AI Ponzi Scheme Just Collapsed
Last night at 4pm EST, something unprecedented happened. Nvidia stock rallied 5% on earnings, then crashed into negative territory within 18 hours. Wall Street algorithms detected what humans couldn’t: the numbers don’t add up.
Here’s what they found.
Nvidia reported $33.4 billion in unpaid bills, up 89% in one year. Customers who bought chips haven’t paid for them yet. The average wait time for payment stretched from 46 days to 53 days. That extra week represents $10.4 billion that may never arrive.
Meanwhile, Nvidia stockpiled $19.8 billion in unsold chips, up 32% in three months. But management claims demand is insane and supply is constrained. Both cannot be true. Either customers aren’t buying or they’re buying without cash.
The cash flow tells the real story. Nvidia generated $14.5 billion in actual cash but reported $19.3 billion in profit. The gap is $4.8 billion. Healthy chip companies like TSMC and AMD convert over 95% of profits to cash. Nvidia converts 75%. That’s distress level.
Here’s where it gets criminal.
Nvidia gave $2 billion to xAI. xAI borrowed $12.5 billion to buy Nvidia chips. Microsoft gave OpenAI $13 billion. OpenAI committed $50 billion to buy Microsoft cloud. Microsoft ordered $100 billion in Nvidia chips for that cloud. Oracle gave OpenAI $300 billion in cloud credits. OpenAI ordered Nvidia chips for Oracle data centers.
The same dollars circle through different companies and get counted as revenue multiple times. Nvidia books sales, but nobody actually pays. The bills age. The inventory piles up. The cash never comes.
AI company CEOs admitted it themselves last week. Airbnb’s CEO called it vibe revenue. OpenAI burns $9.3 billion per year but makes $3.7 billion. That’s a $5.6 billion annual loss. The $157 billion valuation requires $3.1 trillion in future profits that MIT research shows 95% of AI projects will never generate.
Peter Thiel sold $100 million in Nvidia on November 9. SoftBank dumped $5.8 billion on November 11. Michael Burry bought put options betting Nvidia crashes to $140 by March 2026.
Bitcoin, which tracks AI speculation, dropped from $126,000 in October to $89,567 today. That’s a 29% crash. AI startups hold $26.8 billion in Bitcoin as collateral for loans. When Nvidia falls another 40%, those loans default, forcing $23 billion in Bitcoin sales, crashing crypto to $52,000.
The timeline is now certain. February 2026, Nvidia reports fourth quarter and reveals how many bills aged past 60 days. March 2026, credit agencies downgrade. April 2026, the first restatement. The fraud that took 18 months to build unwinds in 90 days.
Fair value for Nvidia: $71 per share. Current price: $186. The math is simple.
This is the fastest moving financial fraud in history because algorithms detected it in real time. Human investors are 90 days behind.
Read the full data driven deep dive article here - https://t.co/sDEf5Mdrtc tweet
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WealthyReadings
RT @WealthyReadings: $NBIS is one of the most interesting AI infrastructure plays on the market, involved across multiple verticals.
Here’s why 👇
🔷 Providing highly efficient compute at competitive prices.
🔷 Serving hyperscalers, start-ups and enterprises with hyperscaler-level compute quality.
🔷 Own and operates data centers all around the world.
🔷 Operating in one of the fastest-growing sectors with massive demand.
🔷 Very rapid ARR growth driven by insatiable compute needs.
🔷 Active in autonomous vehicles and tech education through its subsidiaries.
🔷 Involved in cutting-edge data technologies through equity stakes in ClickHouse and Toloka.
🔷 Valuation reflects execution risk, not full long-term potential.
The bear case?
🔷 Highly competitive industry with major cloud providers and neoclouds, even if Nebius offers hyperscaler-grade compute at better pricing.
🔷 Large capex requirements, long scaling cycles, and the risk of overbuilding capacity — amplified by hyperscalers shifting risk downstream.
🔷 Execution needs to remain flawless to compete long term in the AI ecosystem.
You'll find more details in the full breakdown below, but one conclusion stands: $NBIS is building competitive AI infrastructure at a time when demand is exploding, with pricing and performance that directly challenge hyperscalers.
Question is, how long before the market recognizes the scale of the opportunity?
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RT @WealthyReadings: $NBIS is one of the most interesting AI infrastructure plays on the market, involved across multiple verticals.
Here’s why 👇
🔷 Providing highly efficient compute at competitive prices.
🔷 Serving hyperscalers, start-ups and enterprises with hyperscaler-level compute quality.
🔷 Own and operates data centers all around the world.
🔷 Operating in one of the fastest-growing sectors with massive demand.
🔷 Very rapid ARR growth driven by insatiable compute needs.
🔷 Active in autonomous vehicles and tech education through its subsidiaries.
🔷 Involved in cutting-edge data technologies through equity stakes in ClickHouse and Toloka.
🔷 Valuation reflects execution risk, not full long-term potential.
The bear case?
🔷 Highly competitive industry with major cloud providers and neoclouds, even if Nebius offers hyperscaler-grade compute at better pricing.
🔷 Large capex requirements, long scaling cycles, and the risk of overbuilding capacity — amplified by hyperscalers shifting risk downstream.
🔷 Execution needs to remain flawless to compete long term in the AI ecosystem.
You'll find more details in the full breakdown below, but one conclusion stands: $NBIS is building competitive AI infrastructure at a time when demand is exploding, with pricing and performance that directly challenge hyperscalers.
Question is, how long before the market recognizes the scale of the opportunity?
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WealthyReadings
RT @WealthyReadings: 🔥Hot take: $DUOL is the next $NFLX.
Yet, you shouldn't buy it right now.
I don't. And I explain you why 👇
https://t.co/IqtMqADQqt
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RT @WealthyReadings: 🔥Hot take: $DUOL is the next $NFLX.
Yet, you shouldn't buy it right now.
I don't. And I explain you why 👇
https://t.co/IqtMqADQqt
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WealthyReadings
RT @WealthyReadings: $TMDX is one of the most interesting growth names in the med-tech sector, and one of the most interesting stocks in the entire market right now.
Here’s why 👇
🔷 The only company providing an end-to-end transplant system in the U.S., from organ recovery to transportation to transplant surgery.
🔷 Leader in organ-preservation technology with growing demand and innovative solutions.
🔷 Rising adoption of their technologies and services.
🔷 Upgrading both lung and heart platforms in FY26, with expansion into kidneys by 2028.
🔷 International expansion coming in Europe in 2026 and more regions after.
🔷 Hospitals tend to rely on specific technologies for decades once integrated into their workflow.
🔷 Revenue growth has been consistent, with multiple levers to sustain it for years.
🔷 Margins are expanding.
🔷 Valuation remains well below comparable med-tech innovators.
The bear case?
🔷 Regulations and social security systems can slow progress - innovation & expansion.
🔷 Reputation is critical in this sector and can be impacted by internal or external events.
You'll find more details in the full breakdown below, but one conclusion stands: $TMDX is one of the most innovative and transformative companies in med-tech, offering a service and product no one else does.
Question is, how long before the market finally prices in the growth story and the importance of their service and hardware in the future of transplants?
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RT @WealthyReadings: $TMDX is one of the most interesting growth names in the med-tech sector, and one of the most interesting stocks in the entire market right now.
Here’s why 👇
🔷 The only company providing an end-to-end transplant system in the U.S., from organ recovery to transportation to transplant surgery.
🔷 Leader in organ-preservation technology with growing demand and innovative solutions.
🔷 Rising adoption of their technologies and services.
🔷 Upgrading both lung and heart platforms in FY26, with expansion into kidneys by 2028.
🔷 International expansion coming in Europe in 2026 and more regions after.
🔷 Hospitals tend to rely on specific technologies for decades once integrated into their workflow.
🔷 Revenue growth has been consistent, with multiple levers to sustain it for years.
🔷 Margins are expanding.
🔷 Valuation remains well below comparable med-tech innovators.
The bear case?
🔷 Regulations and social security systems can slow progress - innovation & expansion.
🔷 Reputation is critical in this sector and can be impacted by internal or external events.
You'll find more details in the full breakdown below, but one conclusion stands: $TMDX is one of the most innovative and transformative companies in med-tech, offering a service and product no one else does.
Question is, how long before the market finally prices in the growth story and the importance of their service and hardware in the future of transplants?
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AkhenOsiris
Anthropic:
The new model, Claude Opus 4.5, scored higher on Anthropic's most challenging internal engineering assessment than any human job candidate in the company's history, according to materials reviewed by VentureBeat.
At the highest effort level, Opus 4.5 exceeds Sonnet 4.5 performance by 4.3 percentage points while still using 48% fewer tokens.
Enterprise customers provided early validation of the efficiency claims. "Opus 4.5 beats Sonnet 4.5 and competition on our internal benchmarks, using fewer tokens to solve the same problems," said Michele Catasta, president of Replit, a cloud-based coding platform, in a statement sent to VentureBeat. "At scale, that efficiency compounds."
GitHub's chief product officer, Mario Rodriguez, said early testing shows Opus 4.5 "surpasses internal coding benchmarks while cutting token usage in half, and is especially well-suited for tasks like code migration and code refactoring."
Fundamental Research Labs, a financial modeling firm, reported that "accuracy on our internal evals improved 20%, efficiency rose 15%, and complex tasks that once seemed out of reach became achievable," according to co-founder Nico Christie.
The pricing reduction for Opus 4.5 could pressure margins while potentially expanding the addressable market. "I'm expecting to see a lot of startups start to incorporate this into their products much more and feature it prominently," Albert said.
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Anthropic:
The new model, Claude Opus 4.5, scored higher on Anthropic's most challenging internal engineering assessment than any human job candidate in the company's history, according to materials reviewed by VentureBeat.
At the highest effort level, Opus 4.5 exceeds Sonnet 4.5 performance by 4.3 percentage points while still using 48% fewer tokens.
Enterprise customers provided early validation of the efficiency claims. "Opus 4.5 beats Sonnet 4.5 and competition on our internal benchmarks, using fewer tokens to solve the same problems," said Michele Catasta, president of Replit, a cloud-based coding platform, in a statement sent to VentureBeat. "At scale, that efficiency compounds."
GitHub's chief product officer, Mario Rodriguez, said early testing shows Opus 4.5 "surpasses internal coding benchmarks while cutting token usage in half, and is especially well-suited for tasks like code migration and code refactoring."
Fundamental Research Labs, a financial modeling firm, reported that "accuracy on our internal evals improved 20%, efficiency rose 15%, and complex tasks that once seemed out of reach became achievable," according to co-founder Nico Christie.
The pricing reduction for Opus 4.5 could pressure margins while potentially expanding the addressable market. "I'm expecting to see a lot of startups start to incorporate this into their products much more and feature it prominently," Albert said.
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