Guru99 AI Report › News Letter › Current Edition

Anthropic Launches Claude Opus 4.8 After $65B Raise

ALSO: Wix cuts 1,000 jobs, Amazon’s tokenmaxxing backfires

Krishna Rungta June 2, 2026

Welcome to Guru99 AI Report!

Top Story: Hi friend, the biggest AI story this week isn’t about raw power — it’s about honesty. Meanwhile, agents are trading stocks, fixing their own mistakes, and shaking up entire companies. A lot to unpack below, so let’s get into it.

🤖 Meet Claude Opus 4.8: The Most Capable Model Yet

Brief Buzz: Anthropic just launched Claude Opus 4.8, its most powerful model yet — on the same day it announced a massive $65 billion funding round at a $965 billion valuation. The upgrade sharpens coding and reasoning, but its standout trait is refreshingly human: honesty.

It’s four times less likely to let coding flaws slip by unnoticed, and more willing to flag uncertainty rather than bluff its way through.
A new effort control lets you decide how hard Claude works, trading speed against depth.
Dynamic workflows let Claude Code run hundreds of parallel subagents to handle huge projects, like codebase-wide migrations.
Pricing stays the same: $5 per million input tokens, $25 per million output.
Anthropic teased Mythos, an even more intelligent model class, arriving “in the coming weeks.”

💡 Why Should You Care?

AI that confidently invents facts is a genuine problem. Opus 4.8’s push to admit what it doesn’t know could mean fewer hallucinations and more trustworthy answers for anyone leaning on AI.

🚀 Robinhood just handed AI the keys to your portfolio

Brief Buzz: AI assistants already manage your inbox and calendar. Now Robinhood wants them handling your portfolio. Its new “agentic trading” beta lets you connect AI agents to a dedicated account, set a budget, and let them buy and sell stocks on your behalf (WSJ, FT).

Robinhood uses MCP — a standard that connects AI tools to outside apps — to link agents to a dedicated trading account.
Agents can analyze portfolios, suggest strategies, and execute trades within limits you define.
Robinhood plans to expand beyond stocks into options, crypto, futures, event contracts, and prediction markets.
Gold Card users also get virtual cards, letting an assistant spend within set caps.
The bigger shift: agent apps now need permissions, spending limits, audit logs, and a panic switch baked in. (See how it works)

💡 Why Should You Care?

AI is moving from “help me think” to “act on my behalf.” Once an agent can touch real money, the question isn’t whether it can do the task — that’s mostly a yes these days. The real question is what could go wrong. If you try it, start with a tiny budget, require approvals, and review every single move before handing over more control.

📉 Wix Cuts 1,000 Jobs as AI and a Strong Shekel Reshape the Business

Brief Buzz: Website-builder giant Wix is laying off roughly 1,000 employees — about 20% of its workforce — in the largest round of cuts in its history. In a memo posted publicly, CEO Avishai Abrahami pinned the move on two forces: a strengthening Israeli shekel and a sweeping rewiring of the company around AI.

Headcount falls from 5,277 to about 4,200, with more than 60% of the team based in Israel.
A surging shekel has inflated costs for a company that earns mostly in dollars but pays most salaries in shekels — a structural squeeze better products can’t fix.
Abrahami called AI the biggest shift in how companies are built since the 1970s, flattening management and rolling out AI-native roles dubbed “Xengineer” and “Creators.”
Wix joins Meta, Cisco, and Intuit in tying layoffs to AI, even as its stock has slid over 50% this year.

💡 Why Should You Care?

When a company that helps millions build websites restructures around AI, the message is blunt: this isn’t just new tools — it’s reshaping who gets hired and whose role vanishes.

📊 Amazon’s ‘Tokenmaxxing’ Backfires as Staff Game AI Metrics

Brief Buzz: Amazon wanted its developers using AI, so it started ranking staff by token usage. The result? Employees are now burning tokens on pointless tasks just to climb the leaderboard, turning a productivity push into an office numbers game.

Amazon set a goal for 80%+ of developers to use AI weekly, tracking model and token usage through staff rankings this year.
Its in-house tool MeshClaw lets employees build AI agents that can deploy code, sort emails, and operate across company software.
Staff told the Financial Times the pressure created “perverse incentives,” with people wasting tokens to inflate their stats.
Amazon says the numbers aren’t used in performance reviews, and has since limited who can see individual usage data.

💡 Why Should You Care?

Here’s the catch: a token counter proves AI was used, not that work got better. As more firms embrace “tokenmaxxing,” rewarding quantity over quality just trains employees to optimize for the scoreboard.

🚀 OpenAI just built a tax AI that keeps getting smarter on its own

Brief Buzz: OpenAI just shared how it built “Tax AI” — an agent that drafts complex tax returns and then teaches itself to get better. Working with Thrive Holdings and accounting network Crete, the team used its Codex tool to turn accountants’ corrections into automatic upgrades.

The breakthrough isn’t the accuracy — it’s the self-improvement loop: every time a human accountant fixes an error, the system logs it, and Codex proposes a tested code change to stop it recurring.
Results came fast — returns hitting 75% field accuracy jumped from 25% to 86% in six weeks, eventually reaching up to 97% draft accuracy.
Across 30+ firms and ~7,000 returns, it cut prep time by about a third and lifted throughput roughly 50%.
The tricky part wasn’t simple W-2s but messy K-1s, rental schedules, and spreadsheets — the judgment-heavy work that usually eats an accountant’s hours.

💡 Why Should You Care?

An AI that learns from its own mistakes — without waiting for engineers to patch it — hints at how fast “agents” could absorb the tedious, detail-heavy parts of skilled professions.

⚖️ One prompt, every AI: the side-by-side comparison

In this guide, you’ll learn how to use OpenRouter Fusion to test the same prompt across multiple AI models at once. Rather than opening five apps and guessing, you can compare outputs side by side and build a quick cheat sheet for work.

Step-by-step:

1. Create an OpenRouter account

Open OpenRouter Fusion and choose how you’d like to pay for AI usage — either OpenRouter credits or API keys you already pay for.

2. Select the models

In Fusion, pick the models you want to compare — we tested Opus 4.7 vs. GPT 5.4 vs. Grok — and run one benchmark prompt at a time, keeping it identical across models.

3. Try a prompt

“You are advising a 20-person SaaS company deciding whether to replace its weekly status meeting with an async written update. Write a recommendation memo with 3 benefits, 3 risks, and a 2-week implementation plan. Keep it concise and practical.”

4. Compare results

Open the responses, read the side-by-side analysis, and note which model performs strongest. In the demo, roughly 10 comparisons cost around 40 cents.

💡 Pro Tip

Treat your most-used prompts as a recurring benchmark — re-run them whenever a new model drops, since the “winner” for a given task changes fast as models update. Keep a running note of which model wins each type of task, and lean on OpenRouter’s model browser to weigh price and speed before committing more spend. Over time, this turns into a personalized routing map that quietly saves you both money and guesswork.