One of us opened this week’s show with the thing we keep circling back to: the gulf between people who are actually AI-pilled (using these tools every day, shipping with them, trusting them with real work) and everyone else is widening. Every week. It’s a little terrifying, because the tools make the pilled group powerful in a way that’s genuinely hard to articulate if you’re on the other side of the canyon.

The other one of us put it differently: you have your normal work, and then you have this second firehose pointed at your face all day long, and you’re just trying to get a sip down without drowning. The water isn’t going in your mouth. That’s the whole AI-builder mood in April 2026, summed up.

This week, three things happened that all tell the same story from different angles. Pull up a chair.

1. Anthropic walked into Figma’s boardroom, then walked out with the product

Here’s the timeline, because it’s kind of incredible.

Mike Krieger is Anthropic’s CPO. He also sat on Figma’s board. Last week, on Wednesday or Thursday, he stepped down. Submitted his resignation. A day or two later, Anthropic shipped Claude Design, which is, let’s not be cute about it, a Figma competitor.

That’s a fucked up little conflict of interest. You can file it next to Eric Schmidt sitting on Apple’s board right up until the Android launch. Design cycles take longer than app cycles, so the CPO had to know what was coming. The fact that it landed within 48 hours of the resignation is not an accident.

Whether you think it’s a dirty move or just the market working at its current temperature, the real signal is velocity. Anthropic is now shipping a product a day. If you’re a frontline seller anywhere in the tech stack and your customer says “what about this new Claude thing?”, there’s a real chance you hadn’t heard of it when you woke up that morning.

2. Uber’s CTO blew his entire 2026 AI budget by April

This one is not hypothetical anymore. Uber’s CTO, Praveen Neppalli Naga, admitted it publicly: Uber’s full-year AI budget is gone before summer. Claude Code burned through it.

The math, per the reporting: Uber gave 5,000 engineers Claude Code access in December 2025. By February 2026, usage had nearly doubled. Four months after rollout, the annual AI budget was completely gone. Currently, 95% of Uber engineers use AI coding tools every month, 70% of committed code originates from AI tools, and 11% of live backend updates are AI-written. That’s 1,800 AI-generated code changes per week flowing into production.

The CTO’s quote is the tell: “back to the drawing board.” A company with a $3.4B R&D line and dedicated budget planning teams missed the trajectory that badly. If Uber missed it, your CFO missed it too.

And this is where token anxiety stops being a meme and becomes a line item. The 11 Labs CRO floated a take that stuck with us: if everyone throws a hundred-x (or a thousand-x) more agents at the same buyers who didn’t want to be talked to in the first place, that’s not a recipe for success. It’s a recipe for margin erosion dressed up as productivity.

So here’s the question nobody’s answering out loud: where’s the incremental revenue coming from? Engineering budgets are up. Productivity is up. Headcount is mostly still there. CFOs are running the same spreadsheet and arriving at the same place: you’re speeding up, but the top line has to catch up or something gives.

Our bet, call it our pessimistic 2027 prediction, is that the labs pull a Coke-and-Pepsi. First you open the system, get everybody on the platform, build lock-in. Then you close it and raise prices. The trigger will be layoffs: once enterprises free up budget by letting people go, the labs will know there’s newfound room to charge, and they’ll take it. Layoff day + price hike = revenue shoots through the roof.

3. The GTM teams already living in 2027

If you want to see what the other side of the gulf looks like, Brendan Short at The Signal put on a live event this week with six GTM operators showing off what they’ve actually built with MCPs + Claude. It’s the best snapshot we’ve seen of what “AI-pilled GTM” actually means in practice. Rough inventory:

Team What they built Why it matters Eric Fitz (SMB AE, Zendesk) Sumble MCP + Claude Cowork → identifies call-center prospects in his 1,800-account territory, drafts tech-stack-referenced emails 99.8% deliverability on ~5,000 emails, new edge on a territory that had been reused for 5 years Josh Nelson (RevOps, Nooks) DIY data warehouse on Render + Redash + custom MCP → Claude queries HubSpot, Mixpanel, Nooks, CPQ in plain English What would have taken a DBA two years, shipped over a hackathon weekend Vinayak Mehta (SWE, Clarify) Clarify MCP → iMessage screenshots become CRM entries, LinkedIn ad engagers flow into outbound, contracts auto-update deal fields The rep never leaves the Claude window Anis Bennaceur (CEO, Attention) Call-recording MCP + Clay → auto-clusters persona pockets with Claude-generated names (”The Frustrated Veteran”) Daily scheduled runs; every customer call compounds into better outbound Sam Gong (SVP Mktg, WorkSpan) Octave MCP → 60% of their shared context layer auto-populated from SKO transcripts in one hour Killer quote: “I would not have bought this tool without the MCP.” Jiquan Ngiam (CEO, MintMCP) 25 Slack agents, GitHub as long-term memory, Attio MCP read-only for governance 1-to-5 human-to-agent ratio across the whole company

The thing you can’t miss in this group: the MCP is doing the work of the moat. Six months ago a good product was the moat. Now a good product with a good MCP is the moat, because the product is what gets plugged into Claude and the MCP is what makes it actually useful inside the conversation your rep is already having. If your vendor doesn’t ship one, they’re about to get skipped.

4. Meanwhile, your kids already picked Gemini

One of our kids is seven and currently pulling us toward Gemini: “look it up on Gemini, let me design my images on Gemini.” At school, it’s all Chromebooks, Google Workspace, and Adobe Express. My daughter sat down with Google Stitch last night and was immediately generating app prototypes without instruction.

We keep forgetting this: the sleeper in the model race isn’t Claude or OpenAI. It’s Google, because Google already owns the desks, the keyboards, and the browsers that eleven-year-olds are sitting at every morning. Gemini hit 750M users, 3.1 Pro shipped globally, they dropped a Mac app, and Antigravity (their IDE) now bundles Claude models alongside Gemini and Nano, with the kind of Chrome integration you only get when you own the operating surface. A Gemini subscription gets you Opus 4.6 in the IDE. That’s a weird sentence to write, but here we are.

Distribution eats model quality for breakfast. Always has. Always will.

5. What we’re trying this weekend

Builder energy is peaking and so are the subscription bills. Here’s the shortlist of what’s actually earning its $10-to-$50/month right now, and what you can ship without paying a dime.

Google Stitch: free UI/UX prototyping on a Google Labs URL. Prompt in an app idea, get iPhone mockups, export to AI Studio. Not great portability to Figma, but incredible for ideation if you’re not a designer. Run it with a seven-year-old. Trust us.

shadcn + Claude Code: you do not need to pay $300 for Tailark. Point Claude at the shadcn library, describe the aesthetic, let it cook. Our recent WordPress-through-Claude-Code refresh was maybe an hour of “accept, accept, accept.” Dark theme and light theme, pattern-matched to a reference site we liked.

Antigravity (Google): if you’re bouncing between Chrome, logs, and an API, the browser integration for troubleshooting is genuinely the best we’ve used. Built on VS Code. Agent sessions organized by project folder, same as Claude’s new Code tab.

Framer: $10/mo including domain. For anyone who is tired of the WordPress/Wix/LightSail triangle and just wants a static site up. One of us is publishing a Claude-built fantasy-football-101 interactive guide this weekend purely because the niche is wide open and the tooling makes “publish” a one-evening affair. (Monetization play: affiliate links to DraftKings. The resource page writes itself.)

Paper Design: a likely company-of-one, $10/month, nails UI/CSS styling in a way Figma has to work at. Probably profitable from day one; watch it.

The pattern is the same across all of them: it’s a weekend to ship now. If your moat was “it takes three months to build this ad-generation pipeline,” that moat is gone. Opus 5.1 will close it further next quarter.

The thing we keep landing on

Ben Horowitz was everywhere this week describing the same thing we’ve been circling on the pod: “If you keep looking at it like the old world, and it’s got completely different laws of physics, you are definitely going to die.” He was speaking to founders about the anxiety of not moving fast enough.

But there’s a second anxiety he nailed that’s harder to talk about: workers are afraid of becoming irrelevant, and more than 54% of workers bypassed their company’s AI tools in the past 30 days and completed the work manually instead; another 33% haven’t used AI at all. Combined, roughly eight in ten enterprise workers are either avoiding or actively rejecting the technology their employers are spending record sums to deploy.

Two anxieties. Same machine. No one bridging them.

That’s the gulf. It’s not going to close on its own, and nothing in the current release cadence is slowing down to let anyone catch up. You either build something this weekend, or you watch your seven-year-old do it for you.

See you next week. Stay hydrated. The firehose isn’t stopping.

Quinn & Doom

Referenced this week:

• Brendan Short, The Signal Club: 6 Ways GTM Teams Are Using MCPs and Claude Today

• Nick Lichtenberg, Fortune: a16z’s Ben Horowitz sees ‘AI anxiety’ consuming Silicon Valley founders

• Forbes / Josipa Majic: Ben Horowitz says America must rebuild and AI hits a bottleneck everywhere

• Callum Williams (@econcallum) on X: original post

• @mstockton on X: Uber CTO on blown AI budget

Let that sink in: a company that was considered the “laggard” eighteen months ago is now arguably the hottest enterprise AI company on the planet.

Claude Mythos & Project Glasswing

Anthropic also debuted Claude Mythos — their most capable model yet — alongside Project Glasswing, a security-focused initiative that represents a step function change in cybersecurity capabilities. Mythos is reportedly so effective at discovering zero-day exploits that Anthropic is giving preview access to large tech companies and roughly 40 additional organizations building software and maintaining open source projects, so they can patch vulnerabilities before the model is widely available.

Our hot take: It wouldn’t surprise us if running a Mythos-grade security scan becomes a compliance requirement within the next 18 months. And if it does, model availability will likely become gated and premium-priced — we haven’t seen the labs restrict access like traditional SaaS feature tiers yet, but this feels like the kind of capability that changes that.

OpenAI’s Enterprise Problem (And Why It Might Not Be Their Fault)

Here’s the thesis Quinn has been building: OpenAI may be contractually handcuffed in the enterprise.

When OpenAI signed its deal with Microsoft several years ago, Microsoft became the only hyperscaler that could host OpenAI models. Fast forward to today and the competitive landscape has completely inverted. Anthropic signed deals with Amazon, and now every major cloud has Claude in its lineup. AWS sellers push Claude on Bedrock. GCP reps sell Claude on Vertex. Even Microsoft sellers can offer Claude on Azure Foundry.

Meanwhile, OpenAI’s only enterprise advocates are Microsoft reps — who are incentivized to sell a buffet of models, not just OpenAI. The result? Tens of thousands of enterprise sellers at AWS, GCP, and Azure are all effectively selling against OpenAI every single day. As Quinn put it: “When you walk into an account, you’re literally like — I will sell you anything but OpenAI. Because I can’t.”

Doom added some color: most of OpenAI’s go-to-market team are former AWS startup sellers and account managers. They know the motion, but they’re now the only sales force swimming against the current.

Until OpenAI gets its stateful platform (their equivalent of Claude Code and Cowork) running on other hyperscalers, the enterprise distribution problem is real.

Meta Launches Muse Spark

Meta Superintelligence Labs announced Muse Spark — the first frontier model built on their completely rebuilt AI stack. Alexander Wang shared on X: “Nine months ago we rebuilt our AI stack from scratch. New infrastructure, new architecture, new data pipelines. Muse Spark is the result.”

Quinn gave it a spin for image generation and wasn’t blown away, but the signal is clear: Meta is spending billions, the stack is new, and the competition isn’t slowing down.

Are Tokens the New Cloud?

This was the big question of the week, prompted by a16z’s excellent “Charts of the Week” on the SaaSpocalypse. The data from the Jefferies CIO survey is striking:

In August 2024, fewer than 10% of companies were spending 5% or more of their tech budget on AI. By January 2026, that figure jumped to over 60%. And the breakdown across spending tiers — 5-10%, 10-15%, and 15%+ — shows that this isn’t just dipping a toe. Companies with billion-dollar tech budgets are now committing meaningful percentages to AI tokens and infrastructure.

As Quinn put it: “Doom, are tokens the new cloud?”

Doom’s answer: “I mean, this says it is.”

The SaaSpocalypse: Nuance Over Narrative

We’re not on the SaaS apocalypse bandwagon — but we’re not ignoring the data either.

The a16z charts break down year-over-year spend changes among the largest AI-adopting companies. HubSpot is the clear winner, with the highest median and average growth in the panel. Figma, Box, Cloudflare, Datadog, and Semrush also posted strong 25%+ growth among the biggest spenders.

On the other side: Dropbox is flat-to-negative. Atlassian is down 21%. Asana is down 45%.

There was also a telling signal: 71% of CIOs expect to cut systems integrators to fund AI or cybersecurity. But zero respondents planned to cut cybersecurity budgets. Zero.

Doom made the observation that the OG incumbents — your Salesforces, your tightly-integrated stacks — are better positioned than the mid-tier point solutions. His team just added Salesforce seats. But he’s hungry for the Cowork MCP integration so non-engineers on his team can leverage Claude directly against their CRM data.

Quinn’s take: SaaS won’t die, but procurement is going to come in hard. Expect seat count pressure, base-plan downgrades, and revenue headwinds for any platform that isn’t going all-in on AI.

AWS Trainium Backlog

A quick signal worth noting: AWS CEO Matt Garman mentioned that all currently active Trainium infrastructure is already spoken for — and future capacity is earmarked as well. For context, Trainium is Amazon’s custom silicon designed for deep learning workloads with strong price-performance. It’s now also handling inference, which overlaps with their Inferentia chip. The backlog is likely multi-billion dollars, driven heavily by Anthropic’s demand.

As Doom put it: “Long story short — buy your call options for Amazon.”

Builder Corner: What We’ve Been Building

Quinn: Set up CI/CD pipelines pushing from GitHub to a hyperscaler running database, container, and gen AI environments — no manual laptop auth needed. Also configured Cowork scheduled tasks to process podcast transcripts and generate research prep docs using Opus 4.6. Created and presented an Enterprise Security Stack presentation (now on YouTube). Next up: a new category presentation every week.

Doom: Been using Dune Analytics with MCP integration to query on-chain data dashboards built by data scientists, then asking Claude to go deeper on unfamiliar metrics. The analysis comes back confident — sometimes too confident — so everything gets annotated “Hey, this is from Claude” before it goes to the team.

Quinn’s tip: Use Apple’s voice memos for quick brain dumps. The built-in Whisper transcription is rough, but paste it into an LLM and it cleans up instantly. Great way to capture authentic voice as a writing starting point.

Fringe Lines drops weekly. Subscribe so you don’t miss the next one. And if something hit different this week, tell us in the comments — we read every one. — Quinn & Doom

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4/ Recommended Tables & Nano Banana Pro Image Prompts

Recommended Tables

Table A — SaaS Spend Winners & Losers Among Large AI Adopters

Table B — AI Share of Enterprise Tech Budgets Over Time

Source: a16z / Jefferies CIO Survey panel of large AI spenders

Links & Resources

• a16z Charts of the Week: SaaSpocalypse Interrupted —

• Anthropic Claude Mythos Security concerns & Project Glasswing

• Anthropic Managed Agents

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It used to be that ideas were cheap and execution was hard. That equation is flipping fast — and what it means for enterprise sales, SaaS, and your career is worth paying attention to.

This week, we’re covering what we’re seeing on the ground: non-technical founders building entire back-office systems in a weekend, Goldman Sachs projections reshaping the SaaS TAM, why your customers want AI from vendors they already trust, and an open-source agent called Open Claw that might keep you up at night.

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A Tree Guy Built His Own ERP in 40 Hours

A buddy who runs a tree service business — no coding experience whatsoever — sat down for a marathon weekend and built a full back-office ERP system. He started on Claude Desktop, hit the context window limits, moved to the web interface with multiple tabs, then discovered Claude Code. He paired it with Whisper for voice-to-text input and spent the last 15 hours of his build just talking to Claude Code. The result? A working system backed by Supabase.

Meanwhile, we deployed a fully automated image generation stack on AWS using Amazon Kiro — cloud formation deploys it, sets up all resources, and it parses rows from an Excel file as individual prompts, sends them to Amazon Nova Canvas, and saves the outputs to S3. Swap in your preferred image or video model and you’ve got a production content pipeline.

The takeaway that keeps echoing: execution is getting really easy. The idea might be the hard part now.

Marc Andreessen made a related point recently — Claude Code is amazing, and it was built in two weeks. But flip that around: it only took two weeks to build, which means competitors are right behind. Distribution is the moat, not the build.

AI Hasn’t Made Us Better Sellers Yet — But It’s Changing How We Work

Here’s an honest admission from the trenches of enterprise B2B sales: generative AI isn’t dramatically improving the selling motion yet. Not for account managers going deep on a handful of named accounts. It’s not the SDR-scaling, outbound-automating revolution that the hype cycle promised.

Where it is helping? The internal bureaucracy. The paperwork. The analysis you never had time to do.

One practical example: spinning up an internal Slack channel called “AI for BI” and just dumping Excel files into Claude. Prompting it with “pull out five interesting anecdotes from this data” and getting instant signals — stack-ranked insights with actionable suggestions. Would you have done that analysis manually? Probably not. Would it have required a formal request to your data team? Definitely.

Another example: a finance professional watched someone use Claude to generate a cash flow statement from a balance sheet and income statement. Work that would have cost thousands of dollars in consulting fees — done in minutes. And honestly? The consultant would have hated doing it.

The pattern emerging: AI isn’t replacing the selling. It’s eliminating the friction around it.

Your Customers Want AI From Their Existing Vendors

This might be the most important signal for GTM professionals right now. From the OnlyCFO 2026 budget analysis:

“I don’t want to purchase from another vendor. I have too many apps already. I want to purchase AI stuff from my existing vendors. I bought them because I trust them.”

If you’re an enterprise seller, this is your green light: lead with your existing customer relationships. Talk to your customers about your company’s AI offerings. Don’t put your customer success managers up against hungry, hyper-native AI startup reps in what feels like an entirely new sale — they will lose that fight.

Win where you have your moats. Start from your strengths.

And to the people predicting the death of SaaS — the reality is more nuanced. Nobody is ripping out a thousand-integration CRM like Salesforce to replace it with a vibe-coded internal tool. Most of the cost of software is in the maintenance, not the building. Compliance, updates, security — that’s where the money goes, and that’s not going away.

The Goldman Sachs TAM Projection That Should Get Your Attention

Goldman Sachs estimates the total addressable market for SaaS at roughly $30 billion today. By 2030, they project that shrinks by about a third to $20 billion. Sounds scary — until you see the other side of the ledger.

The agent TAM goes from essentially zero in 2026 to an estimated $30 billion by 2030. Add them together and the total market grows to $50 billion. There’s more value out there, not less. The nomenclature is shifting from seat-based licensing to outcome-based models, but the opportunity is expanding.

SaaS is under pressure — but perpetual licensing was supposed to be dead for years, and they still use on-prem Oracle databases. Don’t write the obituary yet.

One spicy take we heard: enterprise software won’t die, but Excel and PowerPoint will. Not in function — you’ll still work with tabular data and presentations — but in form. You’re not opening a spreadsheet and manually formatting columns anymore. You’re dropping the file into Claude and asking questions.

Open Claw: The Personal AI Agent That’s Both Exciting and Terrifying

Open Claw (formerly known by several other names before trademark issues intervened) is an open-source personal assistant that orchestrates AI agents. Here’s what makes it different from the enterprise agent frameworks like CrewAI, LangGraph, or Amazon Agent Strands:

• It runs locally — even on an old Windows laptop or a Raspberry Pi

• It connects to your model of choice via API

• It communicates through a Discord server — you just message it and it works in the background

• It gives you updates when tasks are complete

One person in our network has it running on a 10-year-old laptop, connected to Claude’s API, taking instructions through Discord. It does what you’d normally do in Claude Code — thinking, executing, iterating — but autonomously in the background.

The promise is enormous. The risk is real: people have been hacked after sharing API keys on public instances. Prompt injection remains a serious vulnerability if it’s exposed to the internet.

But here’s the thought experiment that keeps us up: if one person can run one Open Claw agent, why not two? Or five? You treat each one like a separate employee with its own credentials, workspace, and isolation. At that point, what does headcount even mean?

The scariest and most exciting question: if your Open Claw and my Open Claw can handle discovery calls, share architectural diagrams, break down infrastructure portfolios, and match up partner solutions — when do humans step back in? Probably at the $1 million signature line. But everything before that?

The Macro Backdrop: Revenue Per Employee and the Coming Squeeze

We’d be ignoring reality if we didn’t mention the broader context. Unemployment is at its highest since the Great Financial Crisis. Major tech companies — Amazon, Microsoft, Google — are all laying off. You can argue that’s COVID correction rather than AI displacement, but the structural shift underneath is real.

AI is deflationary for software. It’s easier and cheaper to build. The top decile AI companies are running average revenue per employee around $750,000. If your business model was built on the old P&L of high-margin seat-based software, you’re now competing against teams that do more with fewer people — and they’re going to push prices down to capture market share.

The optimistic view: once companies right-size their ratios and restructure their business models, there should be room to climb back up the value chain. More people should eventually equal more output and differentiation. But the transition period? That’s where we are now, and it’s going to be bumpy.

What We’re Watching

• The AI GTM 100: A curated list of the top AI-powered GTM tools. Clay remains the only unicorn (valued over $1B). We’re tracking names like Air Ops (generative engine optimization), Attio ($116M raised, CRM play), and Unify (outbound automation). The question: are we entering the age of “lifestyle SaaS” where it’s so easy to build that you don’t need to be a $10M+ company?

• Content generation pipelines: We deployed a system using n8n that scrapes a fashion catalog, pulls all product images, and generates 8-second model videos using Google’s Veo 3.1 — all within an hour. Gamma Studio just upgraded, and the gap between “I had an idea for marketing content” and “here’s the finished asset” is collapsing.

• Product-led growth ceilings: Anthropic’s growth is remarkable — people are just requesting seats organically, no AE required. But remember Andy Jassy’s reflection: “I wish I had hired an enterprise sales team sooner.” PLG has a ceiling. Even rocket ships eventually need account executives.

The Bottom Line

If you’re in GTM, the message is clear: build, experiment, and protect your existing customer relationships. The tools are absurdly powerful. The competitive landscape is shifting fast. And the value — both the TAM projections and the day-to-day efficiency gains — is real, even if the perfect AI-native sales motion hasn’t been figured out yet.

Nobody’s cracked the code on AI for enterprise B2B sales. That’s not a problem — that’s an opportunity.

Until next week — keep building, keep selling, and keep asking Claude, ChatGPT, and Gemini the questions you’d never have time to research yourself.

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