Estimated read time: 3 minutes
I used to hate on AISDRs and AI orchestration tools.
I'm coming around. Not because I've seen it nailed yet, but because every model release closes the gap a little more. My working assumption now is that the basics will be table stakes within a year. Writing a decent email. Picking the right contact at an account. That's not where the game gets won or lost.
The game gets won on knowing who to reach out to and why, right now.
If your AI can't answer that, it shouldn't be sending anything. Just because you can target everyone doesn't mean you should. It burns your TAM, tanks your deliverability, and when you send the same message to everyone you end up connecting with nobody.
Bad context costs you twice. Once in deliverability and pipeline, and again in the token bill underneath it. That second cost is what most teams haven't priced in yet: Token Economics.
Token Economics is the real unit cost of getting an AI to do useful GTM work. Most teams are optimizing the wrong half of the equation. They're tuning prompts, swapping models, debating Claude versus GPT for message generation. Meanwhile the actual cost center is upstream of all of that.
Whether you're an AI GTM team running an AIOps stack or a founder wiring Claude into a workflow, the expensive part isn't writing the email. It's the research. Crawling the web, parsing signals, reading job posts and 10-Ks and customer reviews, figuring out which accounts have an active reason to talk. That's where the tokens disappear.
It's also the work generalist LLMs are worst at doing cheaply. Every run starts from scratch. The model has no memory of the account from yesterday, no validated signal library, no sense of what's noise versus what's a real buying trigger for your specific product. So it reads everything, reasons over all of it, and produces a shallow answer at a premium price.
You end up paying action-tier compute for research-tier work, and the output still isn't good enough to trust without a human checking it.
That's the math problem. The more you scale AI outbound without fixing the upstream layer, the worse the unit economics get. You don't grow into profitability. You grow into a bigger bill.
This is where Syft sits.
Here's the split most teams miss. Research is a job that should be done once, deeply, and reused. Action is a job that has to happen in real time, in your customer's context, every time. Those are two different problems, and they want two different kinds of infrastructure. Collapsing them into one LLM call is why your bill is high and your output is mediocre.
We're the context layer in front of your action layer. We do the heavy lifting once, properly, and hand your downstream system a validated list of accounts with the "why now" already attached. Your AI gets a massive head start and stops burning tokens trying to figure out who to talk to. It can spend its compute where it's supposed to: orchestrating action across your stack and making informed decisions downstream.
True omniscience is impossible. But getting as close as we can to your total addressable opportunities, and feeding that into whatever system of action you're running, is a goal worth chasing.
The teams that win the next phase of AI outbound won't be the ones with the cleverest prompts or the prettiest agents. They'll be the ones who figured out which work belongs in the context layer and which work belongs in the action layer, and stopped paying premium prices for the wrong half.
By Zach Wright, Cofounder at Syft AI
