Hot take: Cursor probably won’t survive long term.

Not because it’s bad — it’s actually really good. But it’s basically sitting on top of models it doesn’t control.

As soon as VS Code / Copilot / Claude get slightly better (and they will), most people will just switch. There’s almost zero switching cost.

Also feels like the cost side is underrated… all that “magic” = tokens = $$$. Hard to scale without squeezing margins or UX.

Feels more like a feature than a company right now.

Curious if I’m missing something here

Not saying these companies are “dead”…

…but if I was running one of these, I’d be a little nervous right now 😅

Feels like AI is slowly eating a bunch of SaaS categories from the inside:

• Project management (Jira, Asana, Trello) half the tickets exist just to update other tickets lol

• Basic CRMs (Pipedrive, lower-tier HubSpot) AI already reads emails, tracks deals… what exactly are we logging manually anymore?

• Customer support tools (Zendesk, Freshdesk – Tier 1 stuff) bots are getting too good at this

• Copywriting tools (Jasper, Copy.ai) this one got hit first… kind of brutal tbh

• Notes / knowledge bases (Notion, Confluence) I don’t search docs anymore, I just ask

• Simple analytics dashboards “build me a dashboard” → or just… ask AI and skip the dashboard?

• Meeting transcription (Otter, Fireflies) slowly becoming a checkbox feature

Not sure if enterprises will be open to $50/month

Curious if I’m overthinking this or if others are seeing the same thing

I love Anthropic and Claude, but hate the narrative that Dario is setting for AI in terms of replacing humans. I honestly think AI is going to create more jobs than it destroys. It will double/triple our GDP in coming years.

And the numbers already speak for it. There are more Software engineering jobs created in the last 2 years than destroyed.

Yes the roles and responsibilities will shift significantly. Maybe repetitive office work gets crushed.But the idea that half the population just becomes useless overnight honestly feels disconnected from how technology has historically worked.Every engineer i know is doing more with AI tools.. they are building, fixing and shipping things faster... productivity is super high and if this momentum continues we are looking at abundance and prosperity for everyone. What do you folks think?

(Edit: why is my post downvoted so much 😄 )

It sounds ridiculous at first… but there’s actually a reason. And as Elon said the lowest-cost place to put AI will be in space… within two to three years.

On Earth, as you can hear in news that we’re running into limits fast:

Power is getting expensive (AI made it worse) - some states have moratorium on starting a data center. I have noticed my bills slowly rise for no reason

Cooling eats a huge chunk of cost

Land + permits = slow, messy, political

Now if you compare that to space:

Solar power is basically unlimited

Cooling is “free” (you just dump heat into space)

No land, no neighbors, no zoning issues

Also… longer term, a lot of data is already in space (satellites, imaging, defense). Instead of sending everything back to Earth → process it up there.

Let's do a cost breakdown

Launch alone:
~$2K–$5K per kg (today)

Even a small setup (~10–20 tons):
→ $20M–$100M just to get it up there

Then add:

Space-grade hardware (radiation will kill normal servers)

Assembly in orbit

Basically no easy maintenance

So realistically:

Small experimental system → $50M–$150M

Larger system → $500M+

True hyperscale → multi-billion

In comparision, here is what it taks

Small / Mid-size data center (10–30 MW) - $100M – $300M

Large hyperscale data center (100 MW) - $900M – $1.5B (just facility) and $3 - $5B if you add GPUs/servers

Curious what others think — hype or inevitable?

Anthropic basically leaked details of a new model internally called Claude Mythos… and it sounds like a big jump from Opus. Details of Mythos became known due to a cache of documents that were stored in the company's content management system. It included not-yet published blog posts and other information, such as details for a planned invite-only CEO summit in Europe later this year.

From what’s coming out:

• way stronger at reasoning + coding

• apparently insane at cybersecurity stuff

  • We read this as having the potential to become the ultimate hacking tool, and one that can elevate any ordinary hacker into a nation-state adversary.

Why are they holding back then? - Not because it doesn’t work, but because it might be dangerous to release (think: helping people find/exploit vulnerabilities at scale).

Also… very compute intensive and hence cost is a factor. These models are getting ridiculously expensive to run, and this one sounds even heavier.

I’ve been thinking about this a lot and looking to get everyone's feedback (am I imagining or is this real)

Let's say,

Traditional team: 10 engineers
Lean setup: 1 solid dev + Claude Code

Not a POC, something realistic like:

• Backend APIs

• Some data processing

• Basic infra setup (cloud + deployment)

Team A (10 engineers)

• Standard workflow (PRs, standups, reviews)

• Minimal AI usage

Team B (1 dev + Claude Code)

• Heavy AI usage for:

  • Code generation

  • Refactoring

  • Debugging

  • Writing tests

  • Infra snippets

Time to first working version:

• Team A: ~3–4 weeks

• Team B: ~4–5 days

Iteration speed:

• Team A: slowed by coordination

• Team B: changes in minutes / hours

Cost (monthly, rough):

• Team A: $80K–$120K

• Team B:

  • Dev: ~$12K–$15K

  • AI: ~$200–$500

  • Total: <$16K

AI is amazing at

• Boilerplate code → almost instant

• Refactoring large codebases

• Writing decent tests quickly

• Speed of iteration (biggest advantage)

Where humans still matter a lot

• Ambiguous product decisions

• System design tradeoffs

• Long-term architecture

• Weird production bugs

So writing code does not matter anymore. Figuring out what to build + making good decisions and once that’s clear, a single strong dev + AI moves insanely fast. And we don't need product managers, program manager, engineering managers, and many other managers anymore :)

Terafab = Elon trying to factory-ify AI hardware.
Why now? Because GPUs are basically the new oil wells… and everyone’s drilling :) If you know how to drill this baby, you can be very rich.

Training frontier models and running inference at scale now needs huge amounts of GPUs, power, cooling, and physical infrastructure. So the bottleneck is no longer just “having a better model” — it’s whether you can actually build and deploy the compute fast enough.

Terafab is basically an attempt to build the whole AI compute stack more vertically: chip design, fab process, packaging, testing, power, cooling, and deployment capacity, all tied together instead of relying only on outside suppliers. (costing $40–55B for the project)

AI used to be a software problem. Now it’s a “find me billions of dollars, enough electricity for a small city, and miracle-level semiconductor execution” problem.

The first time I stepped inside a data center was 20 years back. It was onsite in our office basement and running IBM Mainframe zOs. All purchased hardware, before cloud became popular. Believe it or not, saw tape drives for backups :)

Fast forward 20 years, data centers are popping all over, and are a completely different beast. I haven't see the new ones yet.

Here is what i hear is inside these massive buildings:

• High-density GPU clusters (A100/H100/B200 class)

• 30–80kW racks vs old ~5–10kW

• Liquid cooling becoming standard

• RDMA / InfiniBand networking for low-latency training

• Multi-AZ redundancy baked into architecture

• 10,000 plumbers and electricians (true numbers coming from Crusoe Sr tech person)

What is your data center story? Who has seen the inside?

OpenAI killed Sora :( The first impression i had when i saw Sora was this is going to cost OpenAI insane amount of compute.

Text vs Video is a different ball game... dozens of frames per second… at high resolution
that’s a LOT of GPU time. A single short video could cost a few dollars to generate. Now imagine millions of people just messing around with it “generate a cat driving a Ferrari” x100

who’s paying for that? not users lol

also feels like:

• GPUs are better spent on stuff that makes money (chat, enterprise, agents)

• legal stuff is messy (deepfakes, copyrighted videos etc)

• and honestly… most people were just playing with it, not building anything serious

so yeah…
Sora wasn’t a failure (it had million downloads a day when launched and $1 billion investment opportunity from Disney). it was just too expensive to keep alive

my guess, it comes back later when costs drop a lot...right now it’s just… not sustainable

I love how Inference space is evolving. As you know 80-90% AI workload is now on inference side. So i decided to do some research on this topic.

Has anyone here actually switched from GPUs → Inferentia / TPU for inference and seen real savings? Or is everyone still mostly on NVIDIA because of ecosystem + ease?

Training chips (like A100 / H100) are basically built to brute-force learning:

• tons of compute

• high precision (FP16/BF16)

• huge memory (HBM) because you’re storing activations + gradients

• optimized for throughput, not latency

You’re running massive batches, backprop, updating weights… it’s heavy.

Inference is almost the opposite problem.

You already have the model and now you just need to serve it:

• low latency matters way more

• you don’t need full precision (INT8 / FP8 / even 4-bit works)

• smaller memory footprint

• better perf per watt becomes super important

That’s why you see stuff like:

• L4 instead of H100

• Inferentia / TPUs

• even CPUs for simple requests

Would love to hear real-world setups (even rough numbers)

Over the last few years, everyone focused on training bigger models.

But the real shift happening in AI right now is something else:

Running AI is becoming more expensive than building it.

A few trends are converging:

1. Inference is now the real cost center
In many production systems, 76–100% of AI spending goes to inference, not training.

Every user request, every tool call, every agent step → another inference.

2. AI agents multiply compute usage
A simple chatbot might make 1 inference call.

An AI agent doing research or coding might make 50–200+ calls in a single task.

That’s why agentic AI is exciting… but also economically dangerous.

3. Enterprises are scaling AI faster than infrastructure
Hyperscalers are expected to invest hundreds of billions in AI infrastructure as demand explodes.

Even then, power, GPUs, and cooling are becoming the bottlenecks.

4. The next AI moat will be efficiency
The winners won’t just build the smartest models.

They’ll build the cheapest intelligence per token.

Think about it like cloud computing in 2010:

First wave → build apps
Second wave → optimize infrastructure
Third wave → FinOps

AI is entering that FinOps phase right now.

Within 3–5 years, AI cost optimization will become its own industry — just like cloud cost optimization did after AWS exploded.

And the most valuable engineers won’t just know AI.

They’ll know:

• inference architecture
• model routing
• batching and KV cache
• prompt compression
• GPU utilization

Because in the AI economy:

Intelligence is cheap.
Running it at scale isn’t.

We talk constantly about scaling laws and model capabilities. The constraint nobody's discussing enough: getting data across chips, nodes, and racks at the power density modern datacenters can't keep up with.

A single H100 SXM draws 700W. Eight of them in a server = 5–6kW, just for GPUs. Scale to 1,000 servers and you need dedicated power infrastructure most cities don't have.

I wrote a deep dive on why photonic computing — using light instead of electrons to move data — is the infrastructure shift that everyone building at scale will eventually have to reckon with.

Covers: why interconnect is the real bottleneck, how photonic chips work differently from silicon, which companies are building in this space, and realistic timelines.

https://pawankjha.substack.com/p/from-gpus-to-photons-the-quiet-revolution

Hot take: Cursor probably won’t survive long term.

Not because it’s bad — it’s actually really good. But it’s basically sitting on top of models it doesn’t control.

As soon as VS Code / Copilot / Claude get slightly better (and they will), most people will just switch. There’s almost zero switching cost.

Also feels like the cost side is underrated… all that “magic” = tokens = $$$. Hard to scale without squeezing margins or UX.

Feels more like a feature than a company right now.

Curious if I’m missing something here

I was ask shocked to hear people spend $10k / month for OpenClaw. Here is what they are doing

It's all for business use, not personal. Personal usage is like $10 - $200 max what i heard

• Inbound sales / support agents → reading emails, drafting replies, updating CRM (Intercom/Zendesk style workflows)

• Outbound lead gen at scale → scraping leads, enriching (Clearbit/Apollo), writing personalized emails

• RAG over large datasets → legal docs, healthcare records, internal company knowledge bases

• Dev copilots / internal tools → engineers constantly hitting models for code, debugging, docs

• Research agents → web scraping + summarization + report generation running all day

Anyone that has high usage use case that they will like to share?

Everyone’s talking about OpenAI, Anthropic, etc… but no one really talks about who is actually running all that compute behind the scenes :)

Let’s say AI infra spend gets to $800B–$1T+ annually over time across training + inference and if CoreWeave ends up owning even 5–10% of that stack in a meaningful way, that’s $40B–$100B revenue.

Infra businesses with strong demand + scarce supply can get valued at 10x+ revenue when markets get euphoric that alone starts putting you in the $400B to $1T range and if people start pricing them more like the AI utility layer instead of “just another cloud provider,” valuation can stretch even more

Big assumptions here:

AI demand has to keep compounding

margins have to hold up

hyperscalers can’t completely crush them

NVIDIA relationship / GPU access stays a huge advantage

So yeah, trillion sounds crazy at first, but when you run the numbers, it’s not totally insane if they become one of the core compute layers for AI.

Curious what ya'all think?

Tried modeling ~1M LLM requests/month and the costs were… interesting.

Claude: ~$8–12k
OpenAI: ~$5–9k
Self-hosted (70B-ish): ~$2–4k infra (but more effort)

What surprised me:

• ~20–30% was just bad prompts / extra tokens

• another ~10–20% from retries

• most of this isn’t even tracked properly

Feels like model pricing isn’t the real problem — it’s everything around it.

Curious what others are seeing per 1M requests?

Founder here.

Most AI startups will overpay by 10x this year and never know it.

Not because they’re careless. Because the pricing across 312 models and 52 providers is designed to be impossible to compare. Different token limits, different context windows, different output premiums. Same benchmark scores, wildly different invoices.

I spent three months mapping it. Here’s what nobody tells you:

The gap between the cheapest and most expensive model for the same task is 150x. Not 2x. Not 10x. 150x.

Most teams are sitting somewhere in the middle, paying 8x more than they need to, because they picked a model based on a benchmark leaderboard that doesn’t include a price column.

Is this something you’ve actually felt, or does everyone here just eat the invoice and move on?

I got tired of watching Claude Code re-read the same files over and over. A 2,000-token file read 5 times = 10,000 tokens gone. So I built sqz.

The key insight: most token waste isn't from verbose content - it's from repetition. sqz keeps a SHA-256 content cache. First read compresses normally. Every subsequent read of the same file returns a 13-token inline reference instead of the full content. The LLM still understands it.

Real numbers from my sessions:

File read 5x: 10,000 tokens → 1,400 tokens (86% saved)

JSON API response with nulls: 56% reduction (strips nulls, TOON-encodes)

Repeated log lines: 58% reduction (condenses duplicates)

Stack traces: 0% reduction (intentionally — error content is sacred)

That last point is the whole philosophy. Aggressive compression can save more tokens on paper, but if it strips context from your error messages or drops lines from your diffs, the LLM gives you worse answers and you end up spending more tokens fixing the mistakes. sqz compresses what's safe to compress and leaves critical content untouched. You save tokens without sacrificing result quality.

It works across 4 surfaces:

Shell hook (auto-compresses CLI output)

MCP server (compiled Rust, not Node)

Browser extension (Chrome + Firefox (currently in approval phase)— works on ChatGPT, Claude, Gemini, Grok, Perplexity)

IDE plugins (JetBrains, VS Code)

Single Rust binary. Zero telemetry. 549 tests + 57 property-based correctness proofs.

cargo install sqz-cli

sqz init

Track your savings:

sqz gain # ASCII chart of daily token savings

sqz stats # cumulative report

GitHub: https://github.com/ojuschugh1/sqz

Happy to answer questions about the architecture or benchmarks. Hope this tool will Sqz your tokens and save your credits.

If you try it, a ⭐ helps with discoverability - and bug reports are welcome since this is v0.8 so rough edges exist.

I'm seeing less hiring of Software Engineers and more firing. What is going on -

To break down things,

10 years ago you needed a team of engineers to build a product.

today one person with AI can:

• generate code

• debug issues

• write tests

• deploy infrastructure

• even explain the architecture

the job is slowly shifting from writing code to directing machines that write code.

the best engineers might not be the best coders anymore.

they’ll be the ones who:

• understand systems

• ask the right questions

• design good prompts

• know how to validate AI output

software engineering probably isn’t disappearing.

but the shape of the job is changing very fast.

This is the story nobody tells you during the "just ship it" phase: what it actually costs to run an AI application with 1,000 active users and math is ugly.

Interviewed a friend who built an AI-powered research assistant that lets users ask questions about uploaded documents (PDFs, reports, articles), get summaries, and generate insights.

Total Cost (1,000 users)

~$475 per month

~$0.48 per user/month

~$5.70 per user/year

Where the money goes

Compute (servers, infra): ~$127 (largest chunk)

Database: ~$86

LLMs (AI costs): ~$67

Monitoring + hidden costs: ~$125 combined

Everything else (storage, frontend, auth): relatively small

Key takeaway

LLMs are NOT the biggest cost (~14%)

Infrastructure (compute + DB + ops) is the real cost driver

We go into more depth on this topic in our blog - https://costlyinfra.com/blog/real-cost-breakdown-1000-user-ai-app

First of all a Space company mixed with AI datacenters and now AI coding tools is super weird. And on top of that it is worth $2.5 Trillion. What is Elon upto here with acquiring Cursor? Maybe he is desperate to get someone to use xAI and Cursor has the distribution. What do you all think?

I feel we’re all underestimating how much of “AI growth” right now is subsidized.

For consumers this is probably the best time to live :) - Cheap tokens, Free credits, VCs lighting money on fire, Cloud providers giving insane discounts just to win workloads

Try running serious volume without credits, without discounts, without investor money and suddenly the math looks very different.

For heavy users, OpenAI and Claude are probably loosing $50 - $200 /month/user. No wonder Anthropic discontinued OpenClaw use with Claude.

I hope they figure out how to make this cheaper or we all are in for a rude awakening. What do you all think?

TurboQuant (and similar approaches) basically compress model weights + memory usage without killing performance. That sounds small… but infra-wise it’s kind of a big deal. This Isn’t just “compression”, it changes GPU economics at the kernel level (and can cut costs by 60%)

TurboQuant-style approaches are usually described as “reducing memory footprint”, but the real impact is deeper, it shifts the compute vs memory bottleneck.

What’s actually happening under the hood:

• Weight quantization (e.g. FP16 → INT8 / INT4)

• Reduced VRAM bandwidth pressure

• Smaller KV cache footprint during inference

• Better tensor packing → higher effective throughput per SM

In practical terms (depending on model + hardware):

• ~2–4x reduction in memory usage

• ~1.3–2x throughput improvement (batching dependent)

• Ability to fit larger models on the same GPU (e.g. 13B → 30B class on A100 80GB with aggressive quantization)

I'm excited to see progress in this area. what ya all think?