u/Adorable-Reindeer280

Been chewing on this a good while and I’ll tell you what, it’s something else how few founders have actually sat down and run the numbers on this.

OpenAI and Anthropic are burning billions to keep token prices artificially low right now.

That ain’t a conspiracy theory, that’s just what’s happening.

They’re subsidizing the cost of building so the ecosystem grows fast and everybody gets comfortable real quick.

Fine and dandy for now. But that can’t last forever, bless their hearts.

Here’s the part that don’t sit right with me: most AI startups built their whole unit economics around subsidized inference.

Their margins look decent today. But those margins only exist because somebody else is eating the real cost. That dog ain’t gonna hunt forever.

Token prices go 5x? A whole mess of “profitable” AI SaaS companies out there are suddenly underwater. And a lot of founders haven’t even stress tested a 2x scenario, let alone 10x. They’re just whistlin’ Dixie while the numbers quietly stop making sense.

The ones who probably come out the other side are easy to spot:
• Founders who actually control their inference costs (fine-tuned smaller models, caching hard, not just throwing tokens at every problem)
• Companies with margins fat enough to absorb a price war without flinching
• Products where customers are so locked in they’d pay a whole lot more with nowhere else to turn

Everybody outside those three buckets is running their startup on borrowed runway. Not their runway. Somebody else’s.

Now here’s the thing that gets me. Most technology gets cheaper over time, right? That’s just how it goes. But AI might be the exception to that rule. Training costs, energy infrastructure, chip shortages… that stuff don’t just automatically trend to zero because we want it to.

If you’re building an AI product right now, do yourself a solid and model the 10x token cost scenario this week. Not because it’s definitely coming. Just because if your business can’t survive it, you’d rather find that out now than in the middle of your next fundraise when it’s too late to do much about it.

Anybody here actually run this math yet? Genuinely curious what y’all found.​​​​​​​​​​​​​​​​

Bit of a different take.

Has anyone used cold email purely to get people into a lead magnet instead of booking a call?

Thinking send the cold email, offer something free, they bite, now they’re warm.

Nurture em with a few more freebies over a week or two, then start pitching.

Feels like a smarter play than asking a cold stranger to buy something.

But curious if anyone’s actually done this or if I’m overthinking it.

Been running outbound for a good while now and figured I'd get some opinions from y'all on this.

Everybody in this sub talks about personalization. Spend more time per lead, research harder, reference their podcast, mention their dog, whatever. Reckon it's overrated past a certain point.

Here's how I actually rank what moves reply rates, in order:

1. ICP match. Obvious. If they ain't a fit, nothing else matters.

2. Research depth. News, hiring, funding, LinkedIn posts. Makes the email feel less generic. Helps. But it's not the moat folks think it is.

3. Intent signals that match your specific offer. They posted a job that screams your pain point. They tweeted about the problem you solve. They just raised and gotta spend. This is where things actually start working.

4. Catching that intent while it's still hot. This is the part nobody talks about and it's the whole ballgame.

The reason "saw your job post yesterday for VP Sales, here's how we helped X hit quota" works ain't because of the personalization. It's because they posted that job 24 hours ago. They're in pain right now. They opened the req because something's broken. Your email shows up in the exact window where they're looking for help.

Same email three weeks later? Dead. Role's filled. Pain moved on. They don't care.

Here's where it gets operationally messy though.

Say you build a list of 10,000 leads, all with fresh intent signals from the last 7 days. Your sender setup is 10 domains, 30 sends per mailbox per day. Stay safe on deliverability and you're pushing maybe 300 emails a day.

Math: 10,000 / 300 = 33 days to clear the list.

By day 5 your "yesterday" job posts are stale. Day 10, half the roles are filled. Day 20, the funding round is old news. The lead sitting at position 4,000 in your queue is getting an email that says "saw your recent post" except the post is from a month ago and they can tell.

This is the part of outbound I don't see discussed enough around here. Everybody optimizes copy. Nobody talks about the fact that the throughput math just don't support the strategy most GTM teams are running.

What I've been doing that seems to help:

Sort the queue by signal freshness, not lead score. The 300 hottest signals get sent today. Everybody else waits or gets routed different.

Different copy for different signal ages. Hot (under 3 days) gets the time-anchored "saw this yesterday" angle. Warm (4 to 14 days) gets a softer "noticed y'all been hiring" angle. Anything older than that, drop the time reference entirely or quit emailing them.

Match infrastructure to list size, not the other way around. If you want to hit 10k hot leads in 7 days you need way more sender capacity than 10 domains. Most GTM folks build the list first and the infra never catches up.

Smaller and faster beats bigger and slower. 500 leads with fresh intent will outperform 10,000 with stale intent every single time. Quit building lists you physically cannot action in time.

Automate the signal to send pipeline. If a new job post triggers a workflow that takes three days to action, you've already lost.

Couple questions for the sub:

Are any of y'all actually sorting by signal freshness? Or just by lead score / company size?

How big is your sender pool relative to your hot list size?

Anybody running hot vs warm vs cold as separate sequences with different copy? Curious if the lift is real or if I'm overthinking it.

Mostly want to hear how other folks are handling this because I figure the timing vs throughput tradeoff is the biggest unsolved problem in outbound right now and it don't get enough airtime.

(disclaimer: ideas and opinions are mine from running outbound, used Claude to help structure and format the post so it didn't read like a wall of text)

Your AI wrote the post.

My AI liked it.

His AI left a comment.

Her AI replied “Great insight! 🔥”

Nobody read anything.

We all got “engagement.”

This is LinkedIn in 2026. A platform of robots performing business for an audience of other robots.

The humans left a while ago….✈️