Stories from teams using ELI, raw numbers, and sharp opinions about the SaaS stack.
The AI funding rounds keep coming, month after month, and the checks aren't getting smaller. This is where we're tracking July through December 2026: who raised, who wrote the check, and what each bet actually says about where AI is headed next. Less hype, more receipts.
If you're a founder, COO, or Head of IT at a scaleup right now, your AI spend probably isn't one line item anymore. It's scattered across agents, chatbots, unapproved vendor subscriptions, and unexplained API bill jumps, with no system to make that spend legible or controllable. The fixes are well-documented by Stanford, Microsoft, and Amazon, but nobody owns applying and enforcing them company-wide. This piece isn't code to paste in, it's what to ask your engineering team and why your AI spend owner needs visibility across every vendor and agent before these fixes actually save money. Save this. Send it to whoever owns your AI infra budget.
Sapiom is an execution engine that lets AI agents buy and run services; ELI governs what your AI agents spend. Here is how the two compare in 2026 — and when to use each.
Companies are burning money on scattered AI subscriptions and agent usage with zero tracking, and the moment an investor asks about ROI, there's no real answer. This piece explains why that gap exists and how to close it before it becomes a board problem.
Fable 5 is included in paid Claude plans only through July 12 — Anthropic extended it five days from the original July 7 cutoff after users pushed back. After the 12th, it moves to usage credits: $10 per million input tokens, $50 per million output tokens. That's the kind of pricing you save for real leverage, not autocomplete.
Most companies don't have a spending problem. They have a visibility problem. The average mid-sized company runs 120+ SaaS tools. Gartner puts wasted SaaS spend at 10-30% annually. In 2026, with AI platforms billing on usage and tokens instead of flat seats, that number is getting worse. You're not overpaying for software. You're flying blind on what it's actually costing you. The fix isn't an audit after the fact. It's asking the right questions before you sign anything.
The shift isn't from bad AI to better AI. It's from one-off prompting to loops that run without you. Whether you're using Claude Scheduled Tasks, your own agent stack, OpenAI Codex, or a cron job, the principle is the same: recurring work should run itself, not wait on you to open a chat window. The system matters more than the model. Here are 10 loops worth building if you're running lean.
Everyone's racing to deploy AI agents. Almost nobody's feeding them properly. An agent with no context is a very expensive intern who showed up on day one, never got onboarded, and now has send access to your email. It doesn't know what you sell. It doesn't know who signs off on spend. It doesn't know that the navy-and-teal deck is a fireable offence.
Loop engineering is the practice of designing systems that prompt your AI agents instead of you doing it manually. You define a goal, the AI iterates until it's done, and the whole thing runs without you triggering each step. If you're still typing every prompt by hand, you're leaving a lot on the table. Including money.
I spend all day in the software trenches so you don't have to pay sticker price for any of it. Bookmark this page. It only grows.
