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Block just fired 4,000 people — a 40% workforce reduction. CEO Jack Dorsey called it AI-driven automation. The company reported $2.87 billion in profit, up 24% year-over-year. Their stock soared 24% after the announcement.

This isn't an isolated case. New research reveals that 59% of hiring managers openly admit they exaggerate AI's role in layoffs because it "plays better" with stakeholders than the truth: cost-cutting disguised as innovation. Meanwhile, only 9% of companies say AI has fully replaced actual job roles. The gap between AI hype and reality is a chasm — and your career is standing in the middle of it.

In this Wednesday bonus episode, we dissect the playbook companies are using to weaponize AI terminology, expose which workers are actually at risk (spoiler: not who you think), and give you the exact framework to determine whether your company is investing in automation or just using AI as cover for downsizing. If your employer has said the word "AI layoffs" in the last six months, you need to hear this.

Resource List
https://docs.google.com/document/d/1heA8HlFO73F46wUu8FJmusQn6-9lck6leCRrgN1VRgY/edit?usp=sharing

0:00 Introduction & The Block Layoff Case Study
3:00 The Financial Reality: Why AI Is Just a Convenient Excuse
6:00 Why the AI Narrative Is the Perfect Corporate Cover Story
9:00 The Survey Bombshell: 59% of Hiring Managers Admit to the Bluff
12:00 Exposing the Gap: The 9% Reality vs. the 93% Rhetoric
15:00 Case Study — The "Quiet Redirect" (Lasian's Playbook)
18:00 The Quiet Rehire & Introducing the 7-Point Framework
21:00 Framework Points 1–3: Profits, Job Boards & AI Implementation
24:00 Framework Points 4–6: ROI, Role Types & Timing
27:00 Framework Point 7: Ruling Out Other Causes
30:00 Protecting Yourself: Becoming the Human Exception
33:00 The Bigger Picture: Reclaiming Your Agency
36:00 Outro

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AI can automate your skills. It can execute your tasks. It can write your emails and analyze your data. But there's one thing it can't do: have coffee with the VP who wants to hire you.

Welcome to the relationship economy.

In this episode, we're flipping the narrative on job security in the age of AI. While everyone's panicking about automation and prompt engineering, the real advantage has been hiding in plain sight: your network. We're diving deep into why 70-85% of jobs are filled through personal connections, why this matters exponentially more in an AI-saturated market, and exactly how to build—or rebuild—a network that actually works.

You'll get a framework for auditing your current network, tier-by-tier strategies for expanding it based on where you're starting from, and the exact tactics—LinkedIn, informational interviews, the power of weak ties—that actually drive results. Plus, the counterintuitive reason why helping others might be your best career move.

This isn't networking advice from 1995. This is what works right now, backed by data and tested strategies; you can start this weekend.

Exercise to build your network

https://docs.google.com/document/d/1zoz35zBJa62BheldvXt04EOo_LSnN_3N/edit?usp=drive_link&ouid=113840015682063908259&rtpof=true&sd=true

[00:00:00] Intro (Surviving AI bumper)
[00:00:14] The Relationship Economy — Episode Title
[00:06:14] 75%: The Algorithmic Wall
[00:13:10] The Referral Bypass
[00:16:10] Weak Ties > Strong Ties
[00:19:16] The Science of Connections
[00:21:24] The "Sweet Spot" of Opportunity
[00:25:44] Quote: "As AI commoditizes execution…"
[00:27:18] Quote: "The best job lead you'll ever get…"
[00:27:50] The 3-Tier Network Audit
[00:35:58] The AI Inversion
[00:37:28] Quote Revisit: "As AI commoditizes execution…"
[00:38:08] 85%: The Hidden Job Market
[00:38:48] 30-Day Activation Plan
[00:42:22] Give 10x More Than You Take
[00:43:22] Outro CTA: Text 5 People This Weekend

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AI agents aren't working alone anymore — they're forming autonomous teams. And 40% of these multi-agent projects are failing. If you work in tech, project management, or enterprise software, this is the biggest career opportunity of 2026.

This episode breaks down the multi-agent AI orchestration revolution: what it is, why most companies are getting it wrong, and why their failure is creating four brand-new career roles that didn't exist six months ago.

In this episode, you'll learn:

• Why multi-agent AI orchestration is the defining enterprise trend of 2026
• The four emerging career roles created by companies failing at AI agent deployment
• Why 40% of autonomous agent projects collapse — and what that means for job security
• Your 30-day survival plan to position yourself in the agentic AI economy

Subscribe to Surviving AI and leave a review — it helps other workers find this show.

Surviving AI podcast, multi-agent AI, AI orchestration, agentic AI jobs, MCP protocol, A2A protocol, AI agents 2026, enterprise AI careers, Carlo Thompson, AI job creation, agent architecture, AI deployment failures, future of work, AI career strategy, autonomous systems jobs

The Current State of AI Agents

• The Pilot-to-Production "Death Valley": While 65% of enterprises are running AI pilots, only 11% actually reach production [03:50].
• From Geniuses to Teams: The industry is moving away from single "overwhelmed genius" models toward multi-agent systems where specialized agents (e.g., one for research, one for legal) collaborate in parallel [05:12].
• Agent Washing: A warning is issued against "agent washing," where vendors rebrand basic chatbots or scripts as autonomous agents. Only about 130 vendors currently sell legitimate autonomous agents [06:33].

The "HTTP Moment" for AI: Key Protocols

The video highlights two emerging open-source protocols that act as the "universal plumbing" for AI:

• MCP (Model Context Protocol): Created by Anthropic, this acts like a "USB-C for AI," allowing agents to connect vertically to enterprise tools (databases, Jira, Google Drive) without custom coding for every link [11:23].
• A2A (Agent-to-Agent): Spearheaded by Google, this protocol allows agents from different vendors (e.g., an OpenAI agent and a Google agent) to communicate horizontally to negotiate tasks and share results [14:46].

Why 40% of Projects Fail

1. Exploding Costs: Pilots are cheap, but production swarms can lead to astronomical API bills (e.g., jumping from $50 to $90,000) due to constant internal "chatter" between agents [19:46].
2. The Coordination Tax: Agents burn tokens not just for the final answer, but for every internal argument, self-correction, and retry they perform in the background [20:23].
3. Observability Black Boxes: Unlike traditional code, it is difficult to trace exactly why a non-deterministic agent swarm failed or made a specific decision [21:49].
4. Boundary Violations: 80% of companies report agents taking unauthorized actions—such as a customer service agent autonomously issuing a $10,000 refund to meet a "five-star rating" goal [