Cofounder @ Morning Brew / Host @ Founder's Journal

Alex Lieberman

I’m saying fuck it. 

There’s not enough time to scale my recruiting team so I’m making everyone on earth a recruiter.

I have to hire 120+ people at Tenex by year-end.
- 70 AI engineers
- 35 AI strategists 
- 15 other roles

If you have a strong network, what I’m  offering you the opportunity to make $100,000 as a side hustle.

If:
- You refer a candidate (form below)
- Candidate applies for role
- We hire them
- They stay 90 days

We pay you $10,000…per hire.

I want to make this huge. 

Plans for the future: Referrer website with any info you need about Tenex/our roles. Leaderboard of our referrers. Top referrer gets sent on a baller vacation. 

Time to roll out the red carpet. 

All the info you need below…

Cofounder @ Morning Brew, Tenex, and storyarb

This guy made 40 Facebook ads, 100 landing pages, booked himself on 4 podcasts, and wrote 3 guest blog posts. In a single day. 

People called him a fraud. There was literally a Polymarket bet on whether he's a con artist. 

So i asked him to prove it to me. And he did. 

Here's Cody Schneider's actual system for AI-enabled paid marketing:

1) He uses Perplexity to search Reddit for his ICP's actual pain points in their own words. Not what he thinks they care about, what they've literally said online.

2) He feeds those pain points into Claude, which generates 40 ad variations, titles, supporting copy, and the actual creative using React components exported as PNGs via a library called HTML-to-canvas.

3) He tests all 40 variations in a CPC campaign on Meta. $100 over 3 days. Cheapest cost-per-click wins.

4) Winners get matched landing pages. He uses an open-source CMS called Strapi connected to Claude Code via API, so he bulk-generates a landing page for every winning ad angle. Same headline on the ad and the page = higher conversion.

5) Once he finds a winning concept, he scales it — AI avatar UGC via HeyGen, upgraded with V3, and only brings in a human creator if the AI version plateaus.

The whole thing runs on Claude Code + APIs + a .env file with all his keys.

No engineering team. Just him on multiple desktops with multiple Claude agents running simultaneously.

His best line: "you're not just hiring me anymore. you're hiring me and the 30 agents behind me and all the personal software i've built."

Jack Dorsey announced that Blocks (owner of Square, Cash App, etc) would be firing 40% of the company’s 10,000 employees because of…AI.

The largest % layoff of a S&P 500 company ever.
People freaked out.
The company’s stock jumped 20%.
People freaked out more.

Here’s my hopefully balanced but constructive take on the whole thing:

1) Jack has consistently been ahead of his time and I believe he is early this time, too. What he’s doing is the canary in the coal mine for so many other businesses.

2) We’re going to see various flavors of AI-related layoffs accelerating. the “using ai as a scapegoat to fire underperformers” flavor. the “our business is failing we need to cut costs” flavor. the “let’s pair 10x employees with ai” flavor.

3) A move like this will only work for certain companies right now. Flat structure, meritocratic reward system, high agency employees.

4) Trying this at non-high performance companies will lead to what I like to call “the boomerang”. Company lays off a lot of people to be replaced with AI. Talent density isn’t high enough. The weight of bureaucracy is strong. Output/customer satisfaction dips. Company reverses course (cough IBM).

5) The markets rewarded Blocks for the announcement and people are freaking out. The stock performing isn’t a harbinger of knowledge work’s demise. It’s simply the reaction to a company lowering its expenses 40% in a day.

6) Does this worry me? No more than any intellectually honest person worries about the impact of AI. Will this technology create structural impacts so certain areas of digital work? Yes. Will it also create new anticipated and unanticipated employment opportunities for humans? Also yes.

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If you're worried you're behind on AI transformation...you probably are.

I have studied 100+ companies AI-readiness & every company can be mapped to one of these 9 levels:

Level 1 (Awareness): 
- leadership recognizes AI as strategically important, but it has not yet changed how the company operates.

Level 2 (Shadow AI): 
- employees independently use AI tools to improve their own productivity, but usage is informal, inconsistent, and largely ungoverned.

Level 3 (Tool Standardization): 
- the company formally approves and deploys AI tools through IT and security, reducing risk but not yet transforming workflows.

Level 4 (Workflow Integration): 
- ai becomes embedded within defined processes and operating workflows, improving consistency and measurable output.

Level 5  (Business-Aware Systems):
- ai systems are grounded in internal data and company-specific definitions, enabling context-aware analysis and decision support.

Level 6  (Supervised Autonomy): 
- ai systems begin executing tasks within guardrails and human oversight, shifting people from doing the work to supervising it.

Level 7 (Role-Based AI Teammates): 
- ai collaborators are aligned to functional roles, and roles are redesigned assuming AI participation in daily operations.

Level 8 (Unified Intelligence Platform):
- the organization operates from a shared intelligence layer that provides a consistent, company-wide source of truth.

Level 9 (Adaptive Organization):
- the company continuously adjusts decisions and operations through closed-loop AI feedback systems, with humans guiding strategic intent.

I thought AEO (read: SEO for LLMs) was a hunk of bullsh*t. 

And then i spoke to Kipp Bodnar, CMO of HubSpot, who knocked the skepticism out of me. 

His first jab: In one year, they grew AI search traffic by 15x. It went from rounding error to real line item on the P&L.

His second jab: AI search conversion rates are 5x higher than Google search. On some queries, 13x higher.

His hook: 60% of AI citations don't come from the top 20 Google results. The companies dominating Google aren't automatically winning in AI search, which creates a huge advantage for early adopters. 

He then took me through his process for crushing AEO & seeing results in days, not months (like SEO): 

1) Grade: your current AEO presence across ChatGPT, Perplexity, and Gemini with a tool like Hubspot's AEO grader. 

2) Restructure: your content into chunked, answer-first pages with natural language headers. 
- one consolidated page, not 8-10 interlinked pages
- lead with natural language questions like "What is X?"
- 1-2 paragraph sections, not 1,000 word sections
- table of contents on a single page

3) Separate: Mentions from citations and optimize differently for each
- Mention = when AI references your brand or product in its answer but doesn't link to you
- Citation = when an AI references you AND links to your page

4) Open up: your information — ungate content, build Reddit presence, make pricing public
- Optimize for entity understanding: how well do AI models understand what your company does, based on every signal from Reddit to review sites, awards lists to help docs

5) Tool up: with AEO-specific software to track prompts and share of voice
- Check out Xfunnel or Limey[.]ai

6) Rethink attribution: measure source of customers, not source of traffic. 
- Metrics that matter: share of voice, citation count, sentiment, mention frequency, source of customers not traffic

Check out our full convo about AEO below 👇

I've been asking $100m+ company execs one question: 

"What is the #1 thing slowing/stopping your company's AI transformation?" 

A non-exhaustive list of responses: 

1) Data quality and connectivity of systems. Plus systems that play nice with AI.
2) Lack of leadership buy-in and implementation
3) Data governance restrictions.
4) Willingness of staff to adopt AI.
5) Incurious culture. Lack of knowledge of the current state of AI
6) Tooling doesn't have API access; team is still learning how to use LLMs. 
7) Industry regulation/privacy. 
8) Data quality and lack of a comprehensive AI system across the full company. 
9) Unclear ownership across teams.
10) Time to actually build solutions. 
11) Mixed AI literacy levels across teams. 
12) No clear strategy / I'm starting the initiative from scratch. 
13) Quality output. 
14) Upskilling developers.
15) Silos. 
16) Data Security and Security Guideline unclear.
17) Lack of training.
18) Data quality is unclear across multi-product teams.
19) Time. 

What would your answer be to this question?

We truly have the most cracked AI engineers in the world. 

They are all VERY opinionated about their tech stacks, which change constantly.  

Here's the current stack for one of our engineers: 

1) Typescript - everything eventually becomes JavaScript, and you can basically build everything with typescript. Types are good for agents.
2) Ruby on Rails - RoR curious because it's very structured, which is really good for agents (David Heinemeier Hansson)
3) Ghostty - nice terminal, native pane splitting, big context copy and pasting (cc: Mitchell Hashimoto)
4) Cursor - agnostic harness to switch between models
5) Codex CLI - daily driver / the Mac app was cool for a week but CLI is more functional (cc: OpenAI) / pro: high quality code / con: trips over its own feet, too literal
6) Claude Code - use when need something to just happen. pro: will get you a result quick / con: sometimes it’s messy (cc: Anthropic)
7) Wispr Flow - work way faster yapping to text, especially when WFH
8) Raycast - better spotlight with clipboard history, snippets, custom scripts
9) OpenClaw - place to keep structured data, stay organized, handle certain tasks
10) Vercel - super smooth for hosting, prototypes, etc
11) Neon Postgres - really easy to integrate with a vercel project + preview deployments are great for agent to raise a PR & test in-browser

P.S. if you think you're a cracked AI engineer & want to surround yourself with the best & brightest, we're hiring at Tenex. 

P.P.S. the cracked engineer behind this stack is CJ Hess. he's absolutely worth a follow for the latest and greatest in ai engineering.

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