beerbaron

I think these examples are mistaking the trees from the forest. This is just a function on how LLM operates. Does not show lack of intelligence but rather how it see things (token).

AMAZING. This is a model on par with top end frontiers from december 2025. 1/15th of the price of opus. Imagine if China pulls out a better coding frontier model just before the 3 big IPOs, that would cratter valuations. How many over here know if there company allows dev. with chinese models? I

Does that Mythos thing remind you guys about when Japan was supposedly banning PS2 exports because it could be used in missile? Seems like a bit of marketing to me.

What is your issue with chinese models?

Well sentiment has little to do with investment in us assets. Based on economic accounting principles and the data shown in tbe graph, the link between increasing foreign investment and the trade deficit is direct and functional: they are two sides of the same coin. Fix the trade deficit and foreign asset will follow. Fixing trade deficit will not be done by tarifs alone. Fiscal policy and consumption reduction will do the heavy lifting.

The below is another threat to consider too. Not AI directly related... but will be a trend. Greenland was a breaking point, the decoupling will take many years but the will is now there. https://youtu.be/oZL-nRB23RI?si=cluwr16q6Hyv_lui

I think the AI folks are trying to sell a dream here. There are many layers of what we can call an app. I asked Gemini to give me a framework to help start the tough process. I think it can be refined by humans tough. 1. Structure of Data The Question: Does the company rely on data that anyone can scrape, or do they own a proprietary, "closed-loop" dataset that AI cannot access elsewhere? High Risk: The software processes public information or standard business documents (resumes, basic legal contracts, generic code). Low Risk: They have 10 years of private, industry-specific telemetry or "human-in-the-loop" feedback that makes their AI significantly smarter than a general model. 2. Complexity of Workflow The Question: Is the software a "system of record" (where data lives) or a "system of effort" (where work happens)? High Risk: The tool's main value is saving time on a single task (e.g., "AI copywriter" or "background remover"). These are features, not companies. Low Risk: The software is deeply integrated into a messy, multi-step business process involving multiple departments, permissions, and legacy hardware. 3. Output vs. Interface The Question: Does the user care about the software's buttons, or just the final result? High Risk: The UI is a complex dashboard that takes weeks to learn. An AI-native competitor could replace 50 buttons with one "chat" box or an agent that does the work in the background. Low Risk: The interface is the value (e.g., collaborative design tools like Figma or real-time communication tools) where human-to-human interaction is the point. 4. Revenue Model (Pricing) The Question: Do they charge "per seat"? High Risk: If a company charges per user, and AI allows 1 person to do the work of 10, the company’s revenue will drop by 90% even if the customer is happy. Low Risk: They use Value-Based Pricing (charging based on outcomes or usage) rather than the number of human logins. 5. Ecosystem & Integration The Question: How hard is it to "unplug" them? High Risk: It’s a browser extension or a standalone app with no deep integrations. Low Risk: They are the "Single Source of Truth." If turning off the software breaks the company’s payroll, taxes, or supply chain, they are safe (for now). Comparison: High Risk vs. Low Risk

Here is what I predict will happen next: TRUMP : "If you resist an arrest you deserve to die" Of course... totally normal.

Well, as a Canadian I just hope Carney had an intent behind it's speech and that escalation was somewhat part of the plan. 4D chest game... we shall see in 3 months. BeerBaron

I dont understand why its being discussed here. Fraud is not a political subject and has nothing to do with any sides of isles. It has to be caught and fixed. Nothing to debate. I find it irrelevant if the fraud was from immigrants or a sub contractors for the US Air Force. Lots of things should not be politized and this is one of them.

Yeah I was thinking about the lasers, optical transducers, etc... I don't think those have reached the performance ceiling equivalent to the silicon limit. I'm a bit wary of investing in anything related to AI hype right now but if your are going to scale super-horizontally you need ridiculous latency and bandwidth to avoid bottlenecks. We should start another thread discussing opportunities for picks ans shovels for AI where valuations have not hit stratosphere yet. BeerBaron

NVidia does does disclose MTBF but it's mentioned that they have a median life around 6 years. The problem with GPU is that it's all parallel work. So if you lose a piece, the whole round of compute might be lost. In a way it's kind of the same thing as wireless networks (packet collision when two client emit at the same time), below a certain level and it is quite manageable but above another limit and the whole thing becomes useless. You can obviously mitigate that by mixing compute between GPU or having some redundancy which is what everybody is doing but then, you add a load on the telecom side of things. In fact as I come to think of it, I'd rather invest in some next gen telecom for GPU cluster than the GPU themselves. Demand should scale as much but with a much better reliability and still has room to improve for quite a while. BeerBaron

Well, if you listen to Jensen's interview on BG2 he talks about how B100 VS H100 and puits some 2X number and much higher when optimized to the new hardware. According to him, it make absolutely no sense to buy H100 if you can buy B100. The operating cost of the H100 VS the B100 is much higher per TFLOPS. So one could argue that if there were not supply shortage those old GPU would have a negative value. Similar to when miner ASICs were profitable until an ew generation came up... they became fancy space heaters over night and made more NPV to thrown then in the trash than keeping operating them. I would say if NVIDIA scales up production it basically destroys the amortization period. Furthermore, amortization is currently artificially long because of a supply issue and that if your are not a hypercaler with long terms commitments you are shit out of luck. I asked ChatGPT to make me a comparative table comparing the same TFLOPS (reference TFLOPS is 100xH100 cluster) between architectures. TAKE WITH A GRAIN OF SALT AS I'M NOT AN NVidia ANALYST. But it gives and idea. Cost Component A100 Cluster (600 GPUs) H100 Cluster (100 GPUs) B100 Cluster (56 GPUs) GPU CapEx $9,600,000 $3,000,000 $1,820,000 Server / Node Infrastructure (CapEx) $6,000,000 $1,000,000 $1,400,000 Total CapEx $15,600,000 $4,000,000 $3,220,000 --- --- --- --- 5-year Power Cost $1,365,000 $398,500 $223,200 5-year Cooling / PUE Overhead (included in power) (included) (included) 5-year Maintenance / Spares $1,330,000 $450,000 $322,000 5-year Staff / Operations Cost $5,500,000 – 6,000,000 $1,000,000 – 1,300,000 $800,000 – 1,100,000 Total OpEx (5 years) $8,200,000 – 8,700,000 $1,850,000 – 2,150,000 $1,345,000 – 1,645,000 --- --- --- --- 5-Year Total TCO $23.8M – $24.3M $5.85M – $6.15M $4.56M – $4.87M

Impossible to overclock GPU in a large cluster, they would all have to be equally overclocked in order to see the gain. There is kinda of a sync period where between each propagation where data is more or less shared across the whole GPU cluster. Furthermore, GPUs are notoriously unreliable, losing a few GPU can significantly delay the training process, imagine if an overclock triples the early death of those GPU.

I have been using Kimi K2 thinking for a day. Its a great product IMO on par with GPT5. I think this is one of those thing that destroys about 500B of market cap on the western side. At this point LLM are clearly a commodity. I don't see how OpenAI keeps the consumer in at 20$ a month. If people switch between streaming platforms they will swith LLM. Very little moat on the consumer side. I recommend everyone to try kimi. Its free.

To be fair he did pass a lot of laws in 2017, he's trying to reengineer the world's economic system and fix a unsolvable war, that should count as +20 in my book. Everybody should agree that he has moved a lots of s**** around. The outcome of that disturbance can only be measured in years. In the mean time, plug your nose and hope it grows flowers and not disease. I just wished he would define a successful outcome so people could measure the success. BeerBaron

As you know, policies take time to effect. Short term is usually noise. In the interest on seperating signal vs noise and learn, i suggest we revisit those metrics in 365 days. Here are the metrics i propose: Core inflation. Base is 2.5% Gas prices. Base is WTI crude. Jan 20th base +-10 % for volatility. Aka anything within that range is noise. Below, we can give some credit. You want to adjust the metrics?

Probably the best description of Sach and Chamath I've heard. Chamath's position changed from neutral to "kiss ass2 the day he decided to put money on it's horse. I don't blame him, we just have to know it's comments will be filtered to nurture it's investment. Not neutral, the same way a CEO in a conference call will never tell you it's company has gone to s****. I did find the NYT journalist very clever to ask for defined success outcomes and that the goals seems to move daily with MAGA supporters. Some of these goals are pushing in totally different direction. I do think clear objectives should be put in place and communicated to the public. Just putting anecdotal claims in 2Y should not be enough considering the amount of shock the endeavour will put on the system. BeerBaron

The Carney article lacks so much supporting evidence it falls in the category of conspiracy. Not that i would not like it to be true tough. What i would say though is that there was probably an intent to Carney visit to Europe other than sending a message to the US. Strategic discussion were probably held.

Well, not to bring this thread on topic but Pettis had a podcast before the 2nd that did not seem available when i started this thread. Insightful on not the reheated stuff from all medias.

I'm almost wondering if this administration bad communication is a feature and not a bug. After all they had 2 months to prepare this press release. Reminds me of Russian generals contradicting each other. So much noise, people just give up on the truth I guess. BeerBaron

I'm considering Australia, not really exporting to US, good trade relations to China and on an island far far away from whatever else can explode.

I think we are getting a bit off subject here. I don't think Mr. Pettis mentions wokeness or birth rate and that is a completely different non-macro problem. If you gents don't mind I'm all in for a 1980s civil debate not a 2025 blue VS red one Some aspects that I find weak about it's theory is about German laws that made export cheaper. Yes but, Canada, US and Mexico are trade deficit economies and have very weak labor laws VS Germany. A piece that I'm trying to grasp is how the trade deficit is linked to global US debt. Like it is tighly geared or loosely geared? Let's say it's in the middle in the Geering (IE 1$ of trade deficit brings 50 cents of global US debt). Cutting off trade deficit overnight would explode interest rates (less capital inflows) and start a very nasty deleveraging process. I can't imagine what happens if the US whole debt to GDP ratio has to be shrunk down to 50% of what it is today. We are talking great depression numbers. What are you guy's toughts? Thanks BeerBaron

I recently listened a video from Michael Pettis that was recorded 11 months ago that discusses trade imbalances and tarifs. The author makes a cases that sustained trade deficits brings an ever rising debt level at the deficit country over time if the economy is mature because the deficit country cannot invest massively in infrastructure. This seems to align a bit with Trump's view that a trade imbalance is bad for the US in the long term. The depth of it's analysis is, of course, way deeper that Trumps P&L view but. might be the basis for the policies coming soon. Here are the key takeaways that I get from the video: -Trade imbalances could be solved from Tarifs, Capital Controls, Or international concerted efforts for penalize consistant current account surplus countries. -Solving the issues with tarifs should be done over 5-10 years. -The trade deficit has to translate into an increase of the debt levels Mr. Petti's view seems quite contrary to most economist in regards to comparative advantage. Stating countries like China and Germany supppress their wages in order to fuel their industrial base. I would agree with China, but the argument seem weak with Germany. I would like to hear your views about this in terms of flaws in it's analysis but also, if 30Y of trade deficit has created huge amounts of debts what does it mean in term of deleverage if we reverse course in 4 years. Toughht BeerBaron