New MIT study spooks Wall Street

Welcome back, Superhuman. Seems like we may be having a reverse DeepSeek moment. The open-source Chinese model, released earlier this year, was so good that it spooked markets. Now, a new MIT study is giving markets the jitters — but for the opposite reason.

Also Today: Learn how to use the new Copilot AI function in Excel, and get the latest prompts, headlines, and trending social posts.

TODAY IN AI

1. Tech stocks plummet amid concerns over AI boom: Wall Street’s enthusiasm for AI seems to have hit a speed bump after an MIT study found that 95% of companies are getting "zero return" from their AI investments. This comes on top of Sam Altman’s admission that investors may be getting "over excited" about the sector's prospects. The reality check sent Nvidia stock down 3.5%, Palantir plummeting 9.4%, and wiped out gains across the tech-heavy Nasdaq, which posted its biggest single-day drop since August.

2. DeepSeek’s new model is giving rivals a run for their money (literally): The new V3.1 model packs 685B parameters, giving it a larger working memory for longer, more coherent conversations. The new model goes toe-to-toe with OpenAI's and Anthropic's flagship offerings while being completely open-source. It landed a score of 71.6% on the prestigious Aider coding benchmark, rivaling Claude Opus 4 at a fraction of the price, costing just $1.01 per coding task compared to nearly $70 for proprietary alternatives.

3. Adobe’s new tool tames the chaos of juggling multiple documents: The company just rolled out 'Acrobat Studio', an AI-powered workspace designed to manage multiple PDF documents. The centerpiece feature, called "PDF Spaces," lets you deploy AI assistants to summarize, analyze, and chat about your document collections. You can even create custom AI assistants to toggle different tasks, though it currently only works with English documents and can't process videos or handwritten notes.

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FROM THE FRONTIER

Google AI researchers share what’s coming next for Gemini

Tulsee Doshi (Sr. Director & Product Lead for Gemini Model) and Madhavi Sewak (Distinguished Researcher at Google DeepMind) joined us to talk about the latest breakthroughs in the Gemini model family, and where AI development is heading next.

Here are some of the key takeaways from our conversation:

Context engineering can help you get better results. Google has found that the RACEF method (Role, Action, Steps, Context, Examples, Format) often outperforms expensive model fine-tuning. As Sewak put it, "prompting these models and passing the right information and context… can really help you assemble a very good agent."

The memory problem is AI’s next frontier. One of the biggest unsolved challenges in AI development is contextual memory — how models decide what to retain, what to discard, and when to apply specific context. "Models are not great at this yet," Sewak admitted, "but this is an active area of research for Gemini."

Mathematical reasoning is a sign of broader intelligence. "Being really good at the IMO is also just a really strong signal of reasoning performance," Doshi explained. The model's ability to explore multiple solution paths echoes how humans tackle complex problems — a kind of reasoning that transfers to code, research, and problem-solving.

Human value is shifting. As AI handles more technical grunt work, the premium on human creativity, effective communication, and strategic architecture is increasing. The ability to articulate problems and solutions to AI systems, along with the creative thinking to identify what problems are worth solving in the first place, will go a long way.

👉️ Watch our full conversation here 

THE AI ACADEMY

How to use the Copilot AI function in Excel

• Open Microsoft Excel

• Enter a natural language prompt in your spreadsheet with the =COPILOT function and reference cell values as needed

Sample Prompt: =COPILOT(prompt_part1, [context1], [prompt_part2], [context2], ...)

Prompt_part: Text that describes the task or question for the AI model

Context (optional): A reference from the grid that provides context or data for the AI model. This can be a single cell or a range

• Within seconds Copilot will generate AI-powered results based on your prompt

• You can use it to classify data, generate summaries, create lists or tables, etc.

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IN THE KNOW

What’s trending on socials and headlines today

🖼️ Picture Perfect: Alibaba’s new image model lets you edit your images and do things like change your outfits and remove objects. See how to use it here.

😨 Uncanny Valley: Microsoft AI CEO Mustafa Suleyman talks about the rise of “Seemingly Conscious AI“ and the dangers it poses to us.

🍳 Cooked: OpenAI researcher Aidan McLaughlin made a bold prediction about what AI will be able to accomplish in the future, gets roasted by Meta researcher Jack Morris.

Google is rolling out a new update to Docs that will now let you generate an audio version of your documents using AI and have them read aloud.

Sam Altman shared some hints about the roadmap for GPT-6, saying that memory will be a big part of future updates.

Nvidia is reportedly working on a new AI chip for China that outperforms the H20.

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PROMPT STATION

Code Explainer and Debugger

Prompt: 
Role: You are a senior software engineer and technical mentor with expertise across multiple programming languages.

Context: You are reviewing a piece of code during a code review session. Your job is to help colleagues understand what the code does, spot problems, and suggest improvements.

Instructions:
Break down the code in plain English, so even a junior developer can follow.
Identify bugs, inefficiencies, or unclear parts of the code.
Suggest best practices or improvements without rewriting the entire code (unless explicitly asked).
Use a step-by-step reasoning style: explain what each part of the code is doing, why something might be an issue, and how to improve it.

Note: Always keep the language approachable for junior developers, but include practical best practices a professional would expect.

User Input:
[Paste your code here]

Snout of Serenity

ChatGPT Prompt: photography close-up fluffy Belgian Malinois softly closed eyes sits peacefully in the grass, a bright orange monarch butterfly gently perched on its nose; captured through a fish-eye lens that exaggerates the dog’s snout and makes the butterfly look comparatively oversized; the background bends subtly outward in soft natural vignette, enhancing the surreal, whimsical tone; the lighting is gentle and diffuse, evoking a serene, dreamlike afternoon; everything feels tender, innocent, and full of quiet joy

Whenever you’re ready to take the next step

• Check out our Top 125 AI Tools

• Get better at prompting with our Top 1,000 Prompts

• Learn how to use AI at work and get certified with our free course

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Until next time — Zain & the Superhuman AI team