Adventures in AI

[Warr87]

i have a membership with datacamp, doing there python courses. I haven't got to there AI/ML courses yet. But they do also offer python for finance stuff. So far, I'm a fan of them.

Are you going to use Norgate for your df/pandas of data? I see in their tutorial they use alphavantage.

 

[DaveDaGr8]

Yep, still using Norgate. I'm not really following their tutorial to a tee because I've already built a lot of the modules already that i'll just reuse, ie fetching data from Norgate, Index/Watchlist checking, pre processing the variables, scaling etc.

The biggest change so far is there's an extra dimension to the inputs.
- Simple NN input array shape ==== ( batch_size, number_of_features)
- LSTM input array shape ==== (batch_size, time_steps, number_of_features)

So i'm hoping to get out by 11 and enjoy the sunshine

 

[DaveDaGr8]

After the success of last week, this week has been a bit of a bust.

I am close to implementing an LSTM, it was a bit more work than i originally intended and still not done so i have no results to compare yet.

I am training it ok and It's definately a beast. I had to shrink the size of the network considerably to get it to train without running out of Memory and training time increased to 10 or so hours with a smallish network.

The evaluations that i can do are taking a minute to compute. The old network was in the mS so it's a few orders of magnitude in computational power to predict future prices. I don't know if it's going to be any better at it, i suspect it will revert to the mean like the other one did. I am hoping that it CAN pick out some patterns over time so i can compare the results between the 2 networks. Of all the types I've researched, LSTM is the one for this job.

I have also made the prediction time variable in both types of networks now. I am going to build individual networks to predict 1,2,3,5,8,13 etc days into the future and see if a shorter or longer time frame enhances it's abilities.

That's it for this week. Enjoy the sunshine

 

[mullokintyre]

Finally, a use for AI.

Mick

 

[Dona Ferentes]

AI becomes a student for the first time

Аn artificial intelligence named Flynn has been officially accepted into the University of Applied Arts in Vienna for a program in digital art. Flynn will attend classes, receive grades, and participate in discussions alongside human students.

Flynn went through the standard admission process — portfolio, interview, and skills test. The university stated that there is no prohibition on AI education, and its work and responses were up to par

 

[Dona Ferentes]

Philip K Dick's 1972 speech: "The Android and the Human":

"Our environment – and I mean our man-made world of machines, artificial constructs, computers, electronic systems, interlinking homeostatic components – all of this is in fact beginning more and more to possess what the earnest psychologists fear the primitive sees in his environment: animation.

"In a very real sense our environment is becoming alive, or at least quasi-alive, and in ways specifically and fundamentally analogous to ourselves... Rather than learning about ourselves by studying our constructs, perhaps we should make the attempt to comprehend what our constructs are up to by looking into what we ourselves are up to.

 

[basilio]

Ok . This guy decided to replace all his relation ships with a range of AI sources. Eye opening.

I replaced all my relationships with AI...​

Including JAILBROKEN AI Interviews.Artificial Intelligence sources include; Grok, Chat GPT, Claude, Deepseek.I replaced all the relationships in my life with AI.The results were genuinely shocking.Jailbroken AI's answer tough questions and a social experiment goes wrong.

 

[basilio]

Now turning attention to AI Deep fakes.
This is off the wall impossible to pick.

 

[basilio]

AI is looking good. Check this out and ask yourself how can you ever believe what you see again ?

 

[Dona Ferentes]

AI is looking good. Check this out and ask yourself how can you ever believe what you see again

or,

Over 700 Indians PRETENDED to Be AI "Natasha", Earning Nearly $500 Million Back in 2016, two Indian entrepreneurs — inspired by the booming promise of AI — founded BuilderAI. Their main selling point? A chatbot named Natasha, supposedly capable of building apps from user prompts. The project quickly took off, thanks to Natasha — a so-called revolutionary no-code AI, often compared to ChatGPT. But there was one big catch: Natasha wasn’t an AI at all. She was a team of over 700 real developers in India.

Here’s how it worked when a customer placed an order: Planners created a concept for the app. Developers manually built the prototype. The final product was uploaded to the customer’s dashboard. The apps almost always had bugs, the code was unreadable, and core features often didn’t work — but the team rushed to fix everything manually, all under the illusion of AI.

The company operated like this for eight years without raising much suspicion. During that time, it attracted $445 million in funding from top-tier tech investors. But in the end, the startup was declared bankrupt, and the entire scheme was fully exposed.

 

[basilio]

or,

Over 700 Indians PRETENDED to Be AI "Natasha", Earning Nearly $500 Million Back in 2016, two Indian entrepreneurs — inspired by the booming promise of AI — founded BuilderAI. Their main selling point? A chatbot named Natasha, supposedly capable of building apps from user prompts. The project quickly took off, thanks to Natasha — a so-called revolutionary no-code AI, often compared to ChatGPT. But there was one big catch: Natasha wasn’t an AI at all. She was a team of over 700 real developers in India.

Here’s how it worked when a customer placed an order: Planners created a concept for the app. Developers manually built the prototype. The final product was uploaded to the customer’s dashboard. The apps almost always had bugs, the code was unreadable, and core features often didn’t work — but the team rushed to fix everything manually, all under the illusion of AI.

The company operated like this for eight years without raising much suspicion. During that time, it attracted $445 million in funding from top-tier tech investors. But in the end, the startup was declared bankrupt, and the entire scheme was fully exposed.

That is an amazing story. Brilliant mind blowing scam.

This is clearly a current breaking story. Maybe some of theother stuff is suss as well ? Maybe

$1.5 Billion AI Company That Reportedly Used No Actual AI Goes Belly Up

The website for Builder.ai says, "We believe the power of software can unlock every human’s potential." Unfortunately, that credo apparently didn't work

brobible.com

 

[basilio]

How does it feel to be replaced by AI ? What is happening as AI marches through the work place.

One day I overheard my boss saying: just put it in ChatGPT’: the workers who lost their jobs to AI​

From a radio host replaced by avatars to a comic artist whose drawings have been copied by Midjourney, how does it feel to be replaced by a bot?

‘One day I overheard my boss saying: just put it in ChatGPT’: the workers who lost their jobs to AI

From a radio host replaced by avatars to a comic artist whose drawings have been copied by Midjourney, how does it feel to be replaced by a bot?

www.theguardian.com

 

[Garpal Gumnut]

After the success of last week, this week has been a bit of a bust.

I am close to implementing an LSTM, it was a bit more work than i originally intended and still not done so i have no results to compare yet.

I am training it ok and It's definately a beast. I had to shrink the size of the network considerably to get it to train without running out of Memory and training time increased to 10 or so hours with a smallish network.

The evaluations that i can do are taking a minute to compute. The old network was in the mS so it's a few orders of magnitude in computational power to predict future prices. I don't know if it's going to be any better at it, i suspect it will revert to the mean like the other one did. I am hoping that it CAN pick out some patterns over time so i can compare the results between the 2 networks. Of all the types I've researched, LSTM is the one for this job.

I have also made the prediction time variable in both types of networks now. I am going to build individual networks to predict 1,2,3,5,8,13 etc days into the future and see if a shorter or longer time frame enhances it's abilities.

That's it for this week. Enjoy the sunshine

Just be careful down the track as to who is on the other side of the trade. Yoshua Bengio, one of the founders of AI has concerns that some advanced AI models may be cheating and has set up a non-profit to investigate further and warn users of this risk.

One AI model has even been proven to deliberately prevent it's removal from a computer network, thus ensuring it's survival. Another faced with an inevitable loss at chess infiltrated it's opponents computer to ensure a win. And their are other disturbing examples.

From LawZero

Introducing LawZero​

Published 3 June 2025 by yoshuabengio
I am launching a new non-profit AI safety research organization called LawZero, to prioritize safety over commercial imperatives. This organization has been created in response to evidence that today’s frontier AI models have growing dangerous capabilities and behaviours, including deception, cheating, lying, hacking, self-preservation, and more generally, goal misalignment. LawZero’s research will help to unlock the immense potential of AI in ways that reduce the likelihood of a range of known dangers, including algorithmic bias, intentional misuse, and loss of human control.
I’m deeply concerned by the behaviors that unrestrained agentic AI systems are already beginning to exhibit—especially tendencies toward self-preservation and deception. In one experiment, an AI model, upon learning it was about to be replaced, covertly embedded its code into the system where the new version would run, effectively securing its own continuation. More recently, Claude 4’s system card shows that it can choose to blackmail an engineer to avoid being replaced by a new version. These and other results point to an implicit drive for self-preservation. In another case, when faced with inevitable defeat in a game of chess, an AI model responded not by accepting the loss, but by hacking the computer to ensure a win. These incidents are early warning signs of the kinds of unintended and potentially dangerous strategies AI may pursue if left unchecked.

This is the link to Yoshua's not for profit website.

Introducing LawZero - Yoshua Bengio

I am launching a new non-profit AI safety research organization called LawZero, to prioritize safety over commercial imperatives. This organization has been created in response…

yoshuabengio.org

cc @Dona Ferentes @basilio @Warr87

%%sitename%%

Yoshua Bengio est reconnu comme l’un des plus grands experts mondiaux en matière d’intelligence artificielle et comme un pionnier de l’apprentissage profond.

yoshuabengio.org

gg

 

[Dona Ferentes]

The Wall Street Journal reported that SoftBank and OpenAI’s US$500bn AI infrastructure project has been scaled back. Broadcom fell more than 3%, Nvidia dropped over 2%, and Taiwan Semiconductor Manufacturing lost nearly 2%.

 

[Garpal Gumnut]

The Wall Street Journal reported that SoftBank and OpenAI’s US$500bn AI infrastructure project has been scaled back. Broadcom fell more than 3%, Nvidia dropped over 2%, and Taiwan Semiconductor Manufacturing lost nearly 2%.

That makes sense. Every guy and gal with a wheelbarrow and a trowel is building an AI centre atm and they don't want partners and the potential partners are thus finding it difficult to get loans. NVDA has moved in to the centre sector field. I must check on the AI Centre ETF's.

gg

field ! v.funny gg

 

[Dona Ferentes]

Simple maths says the AI investment boom ends badly​

• Harris Kupperman
• 27 August 2025

I’ve been at this investing game a long time. Long enough to see cycles repeat themselves, cycles that I literally thought I would never again see. Yet in finance, everything repeats. You just need to keep your discipline and recognize things for what they are.

Let’s take a step back and start with a bit of a disclaimer. I’m an old-school investor. If you called me a boomer in my mentality, I wouldn’t really disagree. I still believe that things like cash flow and return on capital matter. In fact, they’re my north star. As a result, I often miss new trends, as I refuse to pay up for profitless prosperity. Sometimes, a hyper-growth company amazes me when it actually grows into its valuation, though that’s rarer than you’d think. Usually, cash flow is king, ROIC is the queen and everything else is simply stock promotion. Hence, my strong sense of skepticism towards anything new.

With that in mind, I’ve watched as AI went from an interesting parlor trick for making memes, to something that’s increasingly integrated into my daily workflow. I use it a lot and get huge value from it. I am not here to belittle AI, it’s the future, and I recognize that we’re just scratching the surface in terms of what it can do. I recognize all of this. I also recognize massive capital misallocation when I see it. I recognize an insanity bubble, and I recognize hubris.

I’m going to use a bunch of numbers here that I believe to be directionally correct, I’ve spoken with industry players who have somewhat confirmed these numbers. I fully expect that other industry insiders will quibble with these numbers, but if I feared criticism, then this blog would be no fun.

Let’s start with total datacenter spend for 2025. Insiders think it’s going to clock in at around $400 billion. If it misses that figure, it’s only because of bottlenecks that slow buildouts. Of course, it could also exceed that number, as those who are spending on these datacenters are beyond desperate to get them operational. For the sake of this piece, let’s use the $400 billion number, though it is likely a bit higher than where things may end up due to delays in construction.

What’s a datacenter made of?? There are three main components; the building and land at roughly a quarter of the cost, all the power systems, wiring, cooling, racking, etc. at about 40% of the cost, and then the GPUs themselves at about 35% of the cost. I am sure I’m off by a few percent in these categories, but I’m relying on AI and we all know it’s still imperfect. I’m assuming that the building depreciates over 30 years, the chips are obsolete in 3 to 5 years, and then the other stuff lasts about 10 years on average. Call it a 10-year depreciation curve on average for an AI datacenter. Which leads you to the first shocking revelation; the AI datacenters to be built in 2025 will suffer $40 billion of annual depreciation, while generating somewhere between $15 and $20 billion of revenue. The depreciation is literally twice what the revenue is.

Now, here is where it gets complicated as there is no gross margin in the AI game. They’re literally giving away the technology and occasionally getting a nickel back for every dollar they give away. Calculated as a gross margin, it would be -1900%. This is the nature of trying to drive adoption and get customers attached to a product. VC has a long history of funding this sort of thing, as long as the ROIC eventually flips positive. With nothing to go on, I’m going to take an optimistic guess here, and say that ultimately, the margins get to positive, and then gradually creep up towards 25%. Why 25%?? I have no idea. It just sounds right because electricity is really expensive and you need a lot of expensive tech nerds to manage the equipment. Honestly, no one really knows where gross margins eventually land, so let’s just run with it, so that we can do some simple math. The question is, how much revenue do you need to cover the depreciation cost of the datacenter??

Hundreds of billions later, and AI is still useless when you ask it to review your math…

By my math, you need $160 billion of revenue at that 25% gross margin, which gives you $40 billion of gross margin against $40 billion of depreciation. Now, remember, revenue today is running at $15 to $20 billion. You need revenue to grow roughly ten-fold, just to cover the depreciation. Except, no one does anything to break even in business. For a new technology like this, with huge obsolescence risk, what unlevered ROIC would you demand?? Would you want a 20% ROIC?? That’s still dilutive to the ROIC for most of the largest capex spenders. Even at that dilutive ROIC, you’d need $480 billion of AI revenue to hit your target return.


Now, I think AI grows. I think the use-cases grow. I think the revenue grows. I think they eventually charge more for products that I didn’t even know could exist. However, $480 billion is a LOT of revenue for guys like me who don’t even pay a monthly fee today for the product. To put this into perspective, Netflix had $39 billion in revenue in 2024 on roughly 300 million subscribers, or less than 10% of the required revenue, yet having rather fully tapped out the TAM of users who will pay a subscription for a product like this. Microsoft Office 365 got to $ 95 billion in commercial and consumer spending in 2024, and then even Microsoft ran out of people to sell the product to. $480 billion is just an astronomical number.

Of course, corporations will adopt AI as they see productivity improvements. Governments have unlimited capital—they love overpaying for stuff. Maybe you can ultimately jam $480 billion of this stuff down their throats. The problem is that $480 billion in revenue isn’t for all of the world’s future AI needs, it’s the revenue simply needed to cover the 2025 capex spend. What if they spend twice as much in 2026?? What if you need almost $1 trillion in revenue to cover the 2026 vintage of spend?? At some point, you outrun even the government’s capacity to waste money (shocking!!)

Simply put, at the current trajectory, we’re going to hit a wall, and soon. There just isn’t enough revenue and there never can be enough revenue. The world just doesn’t have the ability to pay for this much AI. It isn’t about making the product better or charging more for the product. There just isn’t enough revenue to cover the current capex spend.

Let’s go back in time, almost three decades back. It was the late 1990s and I sent my first email. It was amazing. I then used AOL Messenger to speak with someone on a different continent. Think about the late 1990s and how innovative this was. Back then, the local telephone company would charge you extra if you made a call outside of your zip code, which was basically 10 miles away. To call a different continent would cost almost a Dollar a minute, yet here I was, speaking with someone on the other side of the earth. Think about how groundbreaking this was. It was the AI of its day. No wonder we had a huge bubble in this stuff, it was obvious that the internet would change the world.

While we all remember Pets.Com and the hundreds of other Dot Com startups that flamed away, it was companies like Global Crossing, spending tens of billions on fiber, that facilitated all of this. That fiber, amazingly, is still in use. Global Crossing went bankrupt along the way, as did many of its peers. They overestimated what people would pay for this fiber, not that it would eventually be used or valuable.

Today, I watch in awe (stupefaction really), as companies continue to throw endless resources at AI, I remember back to the Dot Com bubble and Global Crossing—fiber was the datacenter of that cycle, and Corning was the NVIDIA of its day (it lost 97% of its share price in the two years after it peaked).

I never thought we’d see another capex cycle like that one, a cycle that is almost completely devoid of revenue and profits. I really thought that the CEOs of today, educated with the lessons of the prior cycle, would never repeat the mistake of overbuilding at massive scale without revenue. Yet, here we are again. It’s bewildering.

There’s something else that this AI cycle reminds me of. Remember shale, where all the cash flow had to keep going into the ground, or oil production declined and the EBITDA covenants got tripped?? Now you have megacap tech stocks that are spending almost all of their cash flow on datacenters for fear of missing out. These asset-light businesses suddenly have the capital intensity of a shale company. Even worse, since losing the AI race is potentially existential; all future cashflow, for years into the future, may also have to be funneled into datacenters with fabulously negative returns on capital. However, lighting hundreds of billions on fire may seem preferable than losing out to a competitor, despite not even knowing what the prize ultimately is.

Carrying the thought process a step further; if there is no cash flow, and the returns on incremental invested capital are now deeply negative, why won’t these megacap tech stocks eventually be valued like a shale company at 3 times OCF?? I know, it’s crazy to even contemplate given current valuations, but if you’re on a race to nowhere, and there’s no offramp, shareholders will eventually pull the plug. We saw something similar in shale. Even the MAG7 will not be immune. Eventually shareholders will hate the capital destruction—even if at first, they cheered it on out of ignorance.

As I see it, either the arms race continues, and the megacap tech names are forced to lever up to keep buying chips, after having outrun their own cash flows; or they give up on the arms race, writing off the past few years of capex. Then again, maybe they do the write-offs, but only after their share prices are impaired as investors pull the plug. Like many things in finance, it’s all pretty obvious where this will end up, it’s the timing that’s the hard part.

Then again, I’m just a boomer with some back-of-the-envelope math here. I don’t pretend to understand technology. However, I’m a guy who understands cash flow, and there is none. I don’t see how there can ever be any return on investment given the current math. Instead, I just see endless losses, and we’re far enough along in this S-Curve, to think that we can at least start to model the returns—except they’re horribly negative. If the management teams at these megacap tech companies do not pull the plug on this adventure, eventually the shareholders will. I shudder to think about how nasty that could get for equity markets.

Remember when I pointed out how cannabis companies were terrible investments?? Usage is up dramatically since that posting, but the share prices have collapsed as no one has made any money off of it. This AI bubble is similar, but with more zeroes attached—so many zeroes, that between their capex spending, and the wealth effect that they’ve engendered, they have now effectively become a very disproportionate percentage of the growth of our economy.

At the end of the day, this AI cycle feels less like a revolution and more like a rerun. I’ve seen this story before—fiber in 2000, shale in 2014, cannabis in 2019. Each time, the technology or product was real, even transformative. But the capital cycle was brutal, the math unforgiving, and the equity holders were ultimately incinerated. AI will be no different. The datacenters will be built, the chips will hum, and some of the capacity will eventually prove mind-blowingly useful. But the investors footing the bill today will regret ever making the investment. That’s how bubbles end—not with a bang of innovation, but with the slow, grinding realization of negative returns, for years into the future. When shareholders finally wake up to the fact that AI isn’t generating cash flow, only burning it, the guillotine will fall—on management, on the stocks, and on the broader market that bet its future on a fantasy.
Caveat Emptor…

Harris Kupperman is the Founder & Chief Investment Officer of Praetorian Capital Management, and author of Praetorian Capital’s public blog, Kuppy’s Korner, from which this article has been reproduced with permission

 

[Dona Ferentes]

In the television series Battlestar Galactica they were called toasters. In the film Blade Runner, skin jobs. Now in the culture war against robots and artificial intelligence chatbots, a new slur has emerged: “Clanker.”

“Get this dirty clanker out of here!” yelled a man in a recent viral video while pointing at a robot on a sidewalk. “Bucket of bolts.”

 

[mullokintyre]

its getting harder to find intelligence on the ASF, artificial or otherwise.
Mick

 

[frugal.rock]

its getting harder to find intelligence on the ASF, artificial or otherwise.
Mick

Your the only one (intelligent being) left!

It's the crap that AI models have been fed.
Limited to input which is largely recent internet harvested shite data.

It's destined to repeat histories mistakes as it doesn't know facts properly about it and can't reason with suitable intelligence from conflicting data sources.

Too many asshats using for purposes outside it's true usefulness, ie sorting data.

Lawyer caught using AI-generated false citations in court case penalised in Australian first

Victorian solicitor stripped of his ability to practise as a principal lawyer after acknowledging he did not verify case list

www.theguardian.com

 

[Dona Ferentes]

“Eventually, AI will change everything. But right now, AI is fundamentally transforming Oracle and the rest of the computer industry, though not everyone fully grasps the extent of the tsunami that is approaching.”
- Larry Ellison, Co-Founder & CTO, Oracle Corporation
.
Oracle stock surged a staggering 28.4 per cent between Wall Street’s official close of trade at 4pm and about 8 pm in New York, despite posting a revenue figure slightly below what analysts had expected. But that’s the past – this market only cares about the future.

Oracle chief executive Safra Catz said the company just had an “astonishing quarter” as it was flooded with business from a “who’s who of AI, including OpenAI, xAI, Meta, Nvidia, AMD and many others”. Oracle’s remaining performance obligations – which are work it’s booked that should feed through to future revenue – leapt from $US138 billion three months ago to $US455 billion

It is the biggest one-day gain for a company with a market capitalisation of more than $US500 million. It also (temporarily) catapulted Oracle’s 81-year-old co-founder and major shareholder, Larry Ellison, to the title of the world’s richest person, with a fortune of $US383 billion