Greetings founders, and welcome. You're all here. I'm psyched. Let's do this.

Infrastructure is becoming performance art. One chip startup cleared ten billion in funding before anyone could run independent tests. One streaming platform replaced paid placement with an algorithm that decides who gets seen.

The demos look credible. The checkbooks opened. The engineers haven't stopped asking questions. What works when proof arrives after the bet, and guaranteed distribution gets swapped for relevance you can't buy?

Most finance teams still track SSP in spreadsheets — manually updated, disconnected from billing. On July 22, Tabs' product and marketing leads show what changes when SSP runs natively inside your billing platform: built from real billing data, connected to your Product Catalog, and generating audit-ready documentation automatically.

You'll leave with a clear view of how SSP and billing connect, what transaction price allocation looks like under ASC 606 in practice, and what it takes to close faster without reconciling SSP to contracts by hand.

July 22, 2026 · 1:30–2:00 PM EDT · Live + recording

Register now to save your spot, or read more about how Product Catalog powers billing automation.

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In AI hardware, belief sometimes clears tape-out faster than physics. Etched just leapt to a $10.3B valuation on a $300M Sequoia-led Series C by promising “low-voltage inference” chips and “cluster-scale memory” that can run modern models without GPUs, at lower cost and higher speed.

The core bet: a custom prefill chip plus novel interconnect and memory can structurally beat Nvidia on inference, not just on price or supply. That’s despite limited external access to hardware and ongoing doubts from engineers about whether the claimed efficiency gains are even physically plausible.

Big-name backers are effectively underwriting that the demos are real and the physics scales, turning Etched into a flagship example of investors funding compute narratives ahead of public benchmarks. If those assumptions break, today’s record-setting Series C becomes a cautionary tale about technical diligence in the AI capex boom.

When you start a company there is really no guide, handbook on how to scale your team.

All companies are different, all companies need to scale differently- but most importantly with startups, things move so fast that your scale strategy needs to be that flexible.

For Bezos, even the pause menu is just another AI demo. Lighthouse, a Prime Video overhaul he’s personally driving, turns home screen algorithmic: AI rankings, auto-generated thumbnails, prompt-based browsing, Alexa voice search; the recommendation engine becomes the product.

The collateral damage is the business model around it. Lighthouse scraps paid shelf space in favor of relevance scores, pushing studios from guaranteed hero slots into an opaque ranking contest across 200M viewers and ~4.2% of US TV time. Visibility that used to be bought now has to be earned or renegotiated.

This is the pattern: every large surface becomes an AI-governed marketplace. Anyone building on “we can always pay to be seen” is downstream of platforms that can, overnight, put AI at center and void those guarantees.

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