ElevenLabs Raises $500M as Companies Cut Call Center Costs ElevenLabs raised $500 million in a Series D under the direction of Sequoia, setting its valuation at $11 billion. The company sells voice agents that can speak naturally and run parts of customer service and sales, not just read text out loud. That changes the stakes for regular people because if synthetic voices get reliable and cheap, call centers and dubbing jobs will decrease, while every stranger on the phone will become harder to trust with the rising scams. Here is what the round reveals: Money: ElevenLabs says total funding is $781M and the round adds Sequoia plus a board seat for Andrew Reed.
This is another step in the AI pattern where tools stop being features and start becoming workers. Text automation pushed writers, image models pushed designers, and now voice models want to push support teams and dubbing studios. ElevenLabs could make the service cheaper, smoother, faster and more human or it can make every call feel suspicious by default. The next year will reward whoever proves safety in public, not just demos in private.
AI is only useful if it knows where it's going Think of AI as a car meant to deliver good answers for your business. Basic AI drives on memory alone and often gets lost. RAG adds GPS with live data, but it still needs manual control. Progress Agentic RAG goes further, choosing routes, checking sources, and adjusting in real time.
Progress Agentic RAG-as-a-Service gives you a ready-built, governed system so you focus on outcomes, not infrastructure. Your AI needs a GPS. And a driver. Book a demo: https://link.genai.works/hgzr Studios vs.
The project starts with a closed beta for select partners in March, and Amazon expects early results by May. Amazon states that the goal is to reduce time and costs from an inefficient process. Critics are looking at a studio to see how much of Hollywood's workflow can be automated before anyone notices. Here's what the reporting suggests: Team: It is being called a startup run mainly by product engineers and scientists.
This represents the next phase of AI in media, focusing on efficiently reducing budgets and timelines. If Amazon can compress weeks of prep into days, competitors will copy it, while unions will try to define what counts as human work in a pipeline full of automation. The upside is fewer delays with more shots at niche ideas. As speed becomes the primary objective, questions may arise about the necessity of maintaining a large crew.
March to May will tell us what kind of risks survive when software starts grading the price of imagination. Google $185B Plans for AI Could Trade Privacy for Convenience Google's parent company, Alphabet Inc., posted a strong quarter, then told investors it plans to spend $175B to $185B in 2026 on data centers and AI infrastructure. Sundar Pichai framed it as a necessity, saying parts of the business remain supply constrained, meaning that demand is there, but the servers are not. If Google is right, it can lock in capacity before rivals do but while it locks in costs, everyone else learns to do more with less.
Let's dive into what the details: Spend: $175B to $185B capex forecast for 2026 after about $91B last year.
It seems as though Google is attempting to avoid the AI anxiety that affected Microsoft and other companies by spending immediately as a bold and assured move forward. The potential benefits are improved AI reliability and reduced waiting time for compute capacity. However, regular people will see the risks first with the increasing presence of AI in Search, Gmail and Chrome leading to higher probabilities of incorrect responses, inaccurate summaries and concerns about privacy.
You upload product images, and it creates draft listings with titles, descriptions, tags, SEO text, and image alt text. Then you review everything and publish in bulk, so you’re not stuck adding products one at a time. Core functions (and how to use them): Photo to listing: Upload 5 to 10 photos of one item and get a ready-to-review product title, description, tags, SEO title, SEO description, and alt text. Bulk onboarding: Upload a big folder from a supplier and have it sort photos into products, then approve and publish them in batches.
Catalog cleanup: Scan your store for products missing basics like tags, SEO, or alt text, then generate only what’s missing instead of rewriting everything. Duplicate control: Find products that were added twice, then merge or delete duplicates so your store search and inventory stay clean. Brand consistency: Set simple writing rules like tone and a description format, so new listings follow the same style every time. Try this yourself: Choose 10 Shopify products with poor descriptions or SEO. Regenerate only the SEO title, description, and alt text in Listagrow.
Then conduct a quick before/after check: search your product page for the major phrase you'd type into Google, make sure it appears in the title and first two lines, and make sure the alt text clearly explains the image. Publish updates and repeat 10 more times next week.