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Monday, 27 July 2026

Another Tech Giant Blames AI: Monday.com Cuts 20% of Staff in Restructuring

 

TEL AVIV — Monday.com, the Israeli work management company known for its bright, customizable project boards, announced this week it will lay off about 20% of its workforce as it doubles down on artificial intelligence.

In an SEC filing Wednesday, the company said it will cut just over 600 employees. Monday.com framed the move as part of a “restructuring plan” to create a “leaner, more focused operating model” that supports its “AI-driven growth strategy.”

Co-founder Eran Zinman addressed staff in a LinkedIn memo, insisting the layoffs were “not made to reduce costs or replace people with AI.” Instead, he said the company is adapting to the AI-first vision it laid out roughly a year ago during a platform-wide rebrand. 

The restructuring will cost Monday.com an estimated $45 million to $55 million. Despite the cuts, the company maintained its forecast of up to 20% year-over-year revenue growth for 2026. Monday.com operates two offices in the U.S.


AI Becomes the Common Thread in 2026 Layoffs

Monday.com joins a growing list of tech firms linking workforce reductions to AI in 2026.

According to a new Financial Times analysis, U.S. tech companies have cut nearly 140,000 jobs since January. Amazon, Oracle, Meta, and Microsoft alone are responsible for almost 50,000 of those, as they invest hundreds of billions in AI data center buildouts.

The FT also found a market penalty for the messaging. Companies that cited AI as a factor in layoffs underperformed the Nasdaq by nearly 10% in the 30 trading days after their announcements. But the story isn’t one-sided. AI-focused companies like Anthropic and OpenAI are hiring fast and absorbing displaced talent. Inside larger firms, jobs are also shifting. Meta cut 8,000 roles in May but moved about 7,000 employees into new AI-focused positions. IBM said it is tripling entry-level hiring for AI and hybrid-cloud roles even as it eliminates thousands of other jobs.


A Running List of 2026 Tech Layoffs Tied to AI

July 9 — Microsoft Cut 4,800 roles, or 2.1% of staff, mostly in Xbox. The company said AI is “changing how work gets done” but the roles were “not being replaced by AI.” CFO Amy Hood said total headcount declined year-over-year and will keep falling.

June 22 — Oracle Disclosed a 21,000-person reduction over 12 months, a 13% drop. In a filing, Oracle said “the adoption and deployment of AI technologies... have resulted, and may continue to result, in reductions to our workforce.”

June 3 — GitLab Laid off 350 people, 14% of staff, to fund AI infrastructure. CEO Bill Staples cited “agentic workloads” and a need to rebuild for “100x growth.” The company is exiting 22 countries and reported Q1 revenue of $264M, up 23%.

May — Google Alphabet made quiet cuts across Cloud, including cybersecurity teams, as revenue grew 63% to $20B. The company cut 35% of managers overseeing small teams. Outside estimates put 2026 cuts at 1,500–3,000 engineers.

May 20 — Intuit Announced 3,000 job cuts, 17% of staff, to “reduce complexity and reallocate resources toward AI.”

May 20-21 — Meta Cut 8,000 employees, 10% of staff, while moving 7,000 into AI roles. CEO Mark Zuckerberg said “success isn’t a given” in AI.

May 14 — Cisco Cut 4,000 jobs, 5%, despite beating profit expectations. CFO Mark Patterson said it was about “realigning resources around... AI.”

May 7-8 — Cloudflare Cut 1,100 people, 20%, after its best revenue quarter ever at $639.8M. CEO Matthew Prince said most cuts were in middle management and finance.

May 5 — Coinbase Cut 700 employees, 14%, and flattened to 5 layers below the CEO. CEO Brian Armstrong said engineers now “ship in days what used to take a team weeks” with AI.

May 5 — PayPal Plans to cut 4,500 jobs over 2-3 years, about 20%. CEO Enrique Lores created an “AI transformation” team to redesign processes in coding, support, and risk.

April 16 — Snap Cut 1,000 employees, 16%, citing AI’s ability to “reduce repetitive work and increase velocity.”2026 YTD — IBM

Between 3,000 and 9,000 U.S. roles cut since late 2025. About 200 HR roles replaced by AI agents. The company plans to triple entry-level AI hiring.

March 11 — Atlassian Cut 1,600 jobs, 10%, to “rebalance” toward AI and enterprise sales.

Jan 30 — Dell Workforce fell 10% to 97,000. Severance totaled $569M as AI server revenue is projected to double.

Feb 26 — Block Cut 4,000 jobs, nearly half the company. Jack Dorsey said smaller teams with AI tools “fundamentally changes what it means to build a company.”

Feb 10 — Salesforce Cut <1,000 roles in product and its Agent force AI unit. CEO Marc Benioff said AI agents meant the company needed “less heads” in support.

Jan 28 — Amazon Cut 16,000 corporate jobs. CEO Andy Jassy said generative AI and agents “should change the way our work is done” and will “reduce our total corporate workforce.”

As AI moves from buzzword to budget line, 

2026 is shaping up to be the year tech companies stop experimenting with it — and start restructuring around it.

How to Set Up an Automated Cloud Lab Environment on a Budget

Building a hands-on lab environment is the single best way to level up your IT skills, study for certifications (like the AZ-104 or AZ-400), and test new configurations. However, the biggest fear for any IT professional is accidentally leaving a cluster of Virtual Machines (VMs) running and waking up to a massive cloud bill. 

What if you could spin up a fully functional Azure lab environment with a single click, practice your skills, and destroy it completely when you are done?I

n this guide, you will learn how to build an automated, budget-friendly Azure lab environment using Python scripts and the Azure SDK.


Why Use Python for Azure Automation?


While the Azure Portal UI is great for beginners, real-world enterprise environments rely heavily on automation. By using Python scripts to deploy your lab, you achieve two goals at once:

You save money by ensuring infrastructure only exists when you need it. 

You build highly sought-after DevOps and automation skills for your resume.


Let's dive into the step-by-step setup.

Step 1: Set Up Your Free Azure Account & Budget Alarms

Before writing a single line of code, you need an active Azure account. If you don't have one, sign up for an Azure Free Account, which provides popular services free for 12 months, plus a starting credit. 

Crucial Step: Prevent Billing Surprises 

Never trust your memory to shut down resources. Set up a hard budget limit immediately:

1. Log into the Azure Portal.

2. Search for Cost Management + Billing.

3. Click on Budgets -> Add.

4. Set a monthly budget threshold (e.g., $10) and configure an email alert to notify you when         spending reaches 80%.


Step 2: Prepare Your Local Python Environment

To interact with Azure via Python, you need to install the core Azure Management SDK libraries. Open your terminal or command prompt and run the following command:


bash

pip install azure-identity azure-mgmt-resource azure-mgmt-network azure-mgmt-compute

Authentication Setup 

The easiest and most secure way to authenticate your script locally is via the Azure CLI. Download and install the Azure CLI, then log in using your terminal:

bash

az login

The Python script will automatically pick up these credentials using the DefaultAzureCredential library.

Step 3: The Python Automation Script

Create a new file named deploy_lab.py. This script will automatically create an isolated Resource Group, a Virtual Network (VNet), a Subnet, and a clean Linux VM for you to practice on.

Copy and paste the following Python code:

python

import os

from azure.identity import DefaultAzureCredential

from azure.mgmt.resource import ResourceManagementClient

from azure.mgmt.network import NetworkManagementClient

from azure.mgmt.compute import ComputeManagementClient


# Define variables

SUBSCRIPTION_ID = "your-azure-subscription-id-here"

RESOURCE_GROUP = "IT_Pro_Lab_RG"

LOCATION = "eastus"

VNET_NAME = "LabVNet"

SUBNET_NAME = "LabSubnet"

 

print("🔄 Authenticating with Azure...")

credential = DefaultAzureCredential()


# Initialize clients

resource_client = ResourceManagementClient(credential, SUBSCRIPTION_ID)

network_client = NetworkManagementClient(credential, SUBSCRIPTION_ID)

compute_client = ComputeManagementClient(credential, SUBSCRIPTION_ID)


# 1. Create Resource Group

print(f"📦 Creating Resource Group: {RESOURCE_GROUP}...")

resource_client.resource_groups.create_or_update(RESOURCE_GROUP, {"location": LOCATION})


# 2. Create Virtual Network & Subnet

print("🌐 Creating Network Infrastructure...")

vnet_params = {

    "location": LOCATION,

    "address_space": {"address_prefixes": ["10.0.0.0/16"]}

}

network_client.virtual_networks.begin_create_or_update(RESOURCE_GROUP, VNET_NAME, vnet_params).result()


subnet_params = {"address_prefix": "10.0.1.0/24"}

subnet_result = network_client.subnets.begin_create_or_update(RESOURCE_GROUP, VNET_NAME, SUBNET_NAME, subnet_params).result()


print("✅ Lab network setup complete! Ready for VM deployment.")

# Add VM creation parameters here for advanced setups


Use code with caution.

                         IT Pro Tip: Replace "your-azure-subscription-id-here" with your actual Azure subscription ID found on your portal dashboard.


Step 4: The Crucial "Teardown" Script

The secret to keeping your cloud lab free or ultra-cheap is destroying it the second you finish studying.

Because we grouped all our lab resources inside a single Resource Group (IT_Pro_Lab_RG), tearing it down is incredibly easy. Create a second file named destroy_lab.py: 


python

from azure.identity import DefaultAzureCredential

from azure.mgmt.resource import ResourceManagementClient


SUBSCRIPTION_ID = "your-azure-subscription-id-here"

RESOURCE_GROUP = "IT_Pro_Lab_RG"


credential = DefaultAzureCredential()

resource_client = ResourceManagementClient(credential, SUBSCRIPTION_ID)


print(f"⚠️ Actively destroying all resources in {RESOURCE_GROUP} to save money...")

delete_operation = resource_client.resource_groups.begin_delete(RESOURCE_GROUP)

delete_operation.result()

print("💥 Lab completely removed. Zero ongoing charges!")


Run this script every time you finish your study session!


3 Lab Projects to Try First

Now that you can spin up and tear down an environment instantly, here are three entry-level infrastructure scenarios to build inside your new lab:

Network Security: Modify the Python script to add a Network Security Group (NSG) that blocks all traffic except your local home IP address.

Web Server Deployment: Automate a custom script extension to install Nginx or Apache on the VM immediately upon boot.

Storage Integration: Spin up a secure Azure Storage Account and write a Python routine to automatically back up your lab's system logs.

Summary

Automation ensures you never have to stress about unexpected cloud expenses again. By combining Python scripts with Azure's flexible cloud framework, you get a powerful, disposable sandbox environment tailored perfectly for upgrading your sysadmin or DevOps skills. 

What automation tools do you prefer using for cloud management? Let me know in the comments below!