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u/michahell

r/antiai r/vibecoding r/ResearchML r/LocalLLaMA r/SeekingAlpha r/ValueInvesting r/SuccessionTV r/AIBubble
▲ 17 r/AIBubble

Nvidia has a stake in... SpaceX

https://www.cnbc.com/2026/08/14/nvidia-discloses-21-billion-stake-in-spacex-at-end-of-second-quarter.html

You can't make this shit up 😂

I'm working on a presentation showing Google's CAPEX hiding shenanigans, showing their insane Q2 SpaceX paper gains booked as "other income". Next slide is literally an image of SpaceX's FY26 Q2 copying the CAPEX hiding-as-RPO-scheme, booking RPO as revenue and even earnings! So while googling SpaceX for something unrelated, I find this news that just happened 2 days ago.

Remember the Hyperscaler bubble diagram that was/is circulating?
SpaceX is not even on there yet, and they should be!

u/michahell — 3 days ago
▲ 8 r/ValueInvesting

The information reports USDA has let ONE of it's SalesForce contracts expire

>The U.S. Department of Agriculture, which spends about $3 billion a year on IT, uses Salesforce for “pretty much everything” including case management and maintaining its public facing portals and websites, according to chief information officer Sam Berry. But over the last year, the sprawling agency reduced its use of Salesforce for some tasks as it turned to other software suppliers such as enterprise AI firm C3.

So "The Information" (what an incredibly dumb name to be honest) is a costly paid "Deep Research" entity that does write interesting things (imo) and also publicised Anthropic's private (quarterly?) financials.

Screw their insanely expensive paid subscription. However, if you subscribe to their mailing lists, you do get exposed to some interesting bits of information.

Like the excerpt above. Here's that last newsletter in PDF format for you to read. yw:
https://files.catbox.moe/81sc3h.pdf

I'm still a strong believer in regression to the mean, for sure for SaaS (I do only have a small position in SalesForce). However - and I share this quote with you from "The Information" 's latest newsletter that I just glanced over - this situation, if true, shows some proof that the latest pro-SaaSpocalypse proponents' argument has some measurable truth to it: "The SaaS erosion will happen slowly over time while cheaper and/or self-built solutions are being built". I still don't think this holds for all companies. I still think that the moat is much more than just software: data, network effects, analytics over multiple data-sets that one company just can't have by definition, (customer) service, offloading responsibility & focusing on a core mission. Just like everything else is outsourced or bought that is not central to any specific business.

Yet maybe that truth is somewhere in between NO, AI IS A DANGLING CARROT OVER A TREADMILL PROMISE and YES ALL SAAS WILL DIE.

The question then becomes, where is the border? Which companies will try to do this (and succeed) and which ones will not even try? Which SaaS is easiest to replicate and why?

reddit.com
u/michahell — 8 days ago
▲ 251 r/ValueInvesting

The death of SaaS has been greatly exaggerated

Despite talk of SaaSpocalypse, seat-based revenue has increased

2025 Apr: 65%
2026 Apr: 76%

I do wonder how skewed this is, given that current AI companies actually also sell seat-based subs.

Oh no! Anyway...

ramp.com
u/michahell — 12 days ago
▲ 878 r/AIBubble+1 crossposts

I CANNOT believe I've got DeepSeek-V4-Flash-0731, a frontier model, running on my home PC. Insane!

So this is the stuff of absolute insanity. In less than 20 months we've gone from super expensive cloud models only, to being able to run a Q3 quant of DeepSeek on an Intel Windows PC with a very average 24GB of VRAM. No wonder the big boys are panicking (and yes it's slow as porridge). https://ibb.co/zTvqR8YR

reddit.com
u/michahell — 16 days ago
▲ 11 r/AIBubble

For everyone not understanding why LLM inference is so costly and doesn't scale financially, at all

https://substack.com/home/post/p-208795331

>Each individual inference task might require less compute than the training phase, but here’s the key: it happens constantly, at scale, for potentially thousands or millions of users. This continuous demand, focused on speed and model performance for a good user experience, is what drives the cumulative inference cost. Achieving efficient inference often requires careful tuning of the software infrastructure.

>This leads to a significant imbalance. For most companies deploying these models, the ongoing inference cost vastly outweighs the initial training cost. It’s common for inference to account for 80-90% of the total compute dollars spent over a given model's production lifecycle. Why? Simply frequency and scale. The model serves far more requests during its operational life than the number of batches processed during its training. This trend makes understanding and reducing inference costs a critical focus for any company looking to deploy AI sustainably.

u/michahell — 21 days ago
▲ 706 r/vibecoding

Where are all the successful clones?

Where is the successful Adobe clone? The service-now clone? The spotify clone? The netflix clone?

I was promised vibe-coded almost-free clones. I can’t find them, where are they?
Anyone know of good clones without their included 10x bugs, 2px-offset-buttons, privacy-disregarding, memory-leaking, attack-surface-generating features?

u/michahell — 2 months ago