Meta inks deal with Midjourney

Welcome back, Superhuman. Can AI simulate a trillion-dollar company? Elon Musk definitely thinks so. The xAI founder is building a new company called Macrohard (yes, Musk loves a pun) and is actively recruiting for it.

Also Today: Learn how to save hours every week with Wispr Flow Snippets, and get the latest prompts, headlines, and trending social posts.

TODAY IN AI

1. Meta inks deal with Midjourney to tap its image and video models: The deal gives the social media giant access to the startup’s "aesthetic technology" for future AI products. The collaboration follows a massive spending spree by CEO Mark Zuckerberg, as the Llama maker looks to keep up with the competition — OpenAI offers Sora, and Gemini has its Veo model for image and video generation. Users could potentially see visual features powered by Midjourney in their Meta apps soon.

2. Apple reportedly eyes Google’s AI models amid Siri troubles: Desperate times, desperate measures. With Siri’s planned upgrade hitting multiple snags, Apple has unexpectedly reached out to Google — its most direct competitor in the smartphone OS business — about using Gemini to power its voice assistant, reports Bloomberg. If true, a new custom AI model will serve as Siri’s foundation; a major shift in Apple’s strategy as it looks to catch up.

3. The US government is officially taking a 10% stake in Intel: In an unprecedented intervention by the federal government in private markets, the White House is converting $8.9B in Chips Act grants to equity in the troubled chipmaker. The deal, which makes the US the third-largest shareholder in Intel, signals a dramatic shift in industrial policy, as the administration seeks to revitalize domestic chip manufacturing. The move has sparked a debate over the government’s role in private enterprise.

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FROM THE FRONTIER

Google shares its receipts on AI energy consumption — and busts a major myth

Here’s something interesting to think about. For all the chatter around AI’s environmental footprint, how much energy do you think a single text prompt uses? Hint: the number probably isn’t as big as some are claiming.

Efforts to understand AI’s energy footprint have largely been stonewalled, mostly due to a lack of transparency from major tech companies. Now, Google has finally pulled back the curtain with a detailed report on just how much energy its Gemini models consume.

Here’s what it found: A typical text query burns through about 0.24 watt-hours of electricity — about as much as running a microwave for one second. Each text prompt also uses about five drops of water and pumps out 0.03 grams of carbon dioxide — figures that are "substantially lower" than many public estimates, says the company.

What makes this study unique is its "full stack" approach. Unlike other studies that zero in on the AI models themselves, Google accounts for everything — from the chips running the AI to the cooling systems keeping data centers operational. It’s the most transparent estimate yet from a Big Tech company with a popular AI product.

There are some important caveats, though. Google’s figure only represents the energy required for a single prompt, not the total energy that went into training the underlying AI model (which can be a lot). Some queries, like generating images and videos, can also use a lot more power. While Google’s transparency and efficiency gains are a welcome step forward, the industry still has some way to go if it’s to keep up with AI’s increasing energy demands.

THE AI ACADEMY

How to save hours every week with Wispr Flow Snippets

• Get Wispr Flow and open the app.

• Go to Settings → Add New Snippet.

• Give it a name (e.g., “My Calendly link”) and paste the text or link you want it to expand into.

• Trigger it with your voice. Next time you’re dictating, just say the snippet name.

Sample Prompt: Say “intro email”, and Wispr Flow types: Hey, would love to find some time to chat later…

• Works in email, Slack, docs—literally anywhere you type.

• Set it up once. Save time forever.

Get Wispr Flow today (at no cost) — and start turning your voice into shortcuts.

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IN THE KNOW

What’s trending on socials and headlines today

⌛️ Blast From The Past: AI-generated video clips that give users a nostalgic time capsule into the 80s and the 90s are racking up millions of views on X.

🧠 Memory Moats: ChatGPT has dropped a new "Project-Memory" feature that allows the chatbot to use conversations within the project for context, but restricts it from dabbling into saved memories from outside the project.

🌐 Browser Breach: A new report has X users concerned about a major security flaw in Perplexity’s Comet browser that makes accounts and sensitive information vulnerable to security risks — screenshots included.

Nvidia is reportedly pitching a modified version of its advanced Blackwell AI chip for US approval days after halting production of its Chinese-approved H20 architecture.

Elon Musk’s xAI has dropped an older version of its AI model Grok — specifically, the model weights used in Grok 2.5 — on the open-source platform Hugging Face. Plans are in place to open-source Grok 3 in six months.

Harvard startup Halo is planning to launch a $249 pair of smart glasses that perform what its founders have dubbed "vibe-thinking". The specs will record everything you do, use AI to whip out facts, and recall all your previous conversations.

PRODUCTIVITY

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PROMPT STATION

Cost Saving Playbook

Prompt: 
Role: You are a Finance Ops/CFO partner expert at reducing costs without hurting growth.

Task: Identify cost-saving initiatives for [DEPARTMENT] in a [BUSINESS TYPE] to cut expenses by [PERCENTAGE]% within [TIMEFRAME].

Inputs (fill if known, else assume benchmarks):
- Current spend: [AMOUNT, CURRENCY]
- Top cost buckets: [LIST]
- Headcount/contracts/tools: [LIST]
- Guardrails: [e.g., no layoffs, maintain SLAs]

Method:
1) Break spend into 5–10 buckets.
2) For each, suggest levers (eliminate, renegotiate, consolidate, automate, optimize).
3) Size savings (Low/Likely/High, % of total).
4) Note one-time costs & time-to-impact.
5) Score using ICE (Impact × Confidence × Ease ÷ Months).
6) Tag “Quick Win” if ≤30 days, low risk.

Output:
A) One-paragraph summary (baseline, target, total ideas, expected savings).
B) Prioritized top 10 initiatives (line per idea: Initiative — Likely savings — Time-to-impact — Quick Win?).
C) Table:

| # | Initiative | Bucket | Lever | Baseline Spend | Savings Range | % Target | Cost | Time (mo) | Owner | Risk | ICE Score | Quick Win |

D) 30/60/90 plan (bullets).
E) Assumptions & data gaps.
F) Top 3 Quick Wins (1–2 sentences each).

Newspaper Art

ChatGPT Prompt: A collage of paper depicting [character name]. Overlapping newspaper patterns.

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Until next time — Zain & the Superhuman AI team