{{first_name | Hey}}, welcome back.
Today’s issue is about a new AI model race centered around decision-making, a surprising signal from the IPO market, and Google’s latest frontier model that only a few can use.
These are today's updates.
Amazon, OpenAI and Cloudflare race to build the next class of AI models
Oura’s IPO delay hints at a broader chill in the market
Google launches its most powerful AI model yet, but keeps it largely out of reach
Tools, jobs, resources, and last issue’s winning prompt ⬇️
Thought Leadership
Why enterprises are mixing AI models: Enterprise AI now runs on general-purpose and specialist models working together. Speed alone doesn't cover work that demands domain depth, governance, or consistency. Marcus Tober (SVP AI & Innovation, Semrush) and Steve Syrek (Engineering, Agentic AI, DeepL) discuss how leaders decide what runs where, what stays dedicated versus embedded, and how MCP makes all that possible.
Neatprompts is where enterprise teams discover what's worth paying attention to in AI. Every week, 100,000+ readers rely on us for actionable insights and practical workflows.
If your product helps businesses adopt, deploy, or scale AI, we'd love to introduce it to our audience.
Top News
Amazon released Strands Decider 2B, an open-source model that picks from a fixed set of options instead of generating text, built on a small Qwen3.5 base. It started as an internal side project by AWS engineer Marc Brooker, who built it after seeing Jev, a similar model from startup TypeSafe AI. It runs locally, returns answers in under 100 milliseconds, and is fully open source with all training data and scripts included.
Marc’s version briefly topped the JevBench leaderboard before Amazon turned it into an official product. OpenAI shipped a competing Decisions API at DevDay the day before, and Cloudflare followed with its own model, Clef, the same day as Amazon.
LLMs vs Jev Comparison Chart
What makes this interesting is that three established companies raced to commoditize, for free, the exact niche a small startup had just carved out. TypeSafe barely had an hour before the bigger players caught up. Jev reportedly ran a task for $2.94 that would cost $372 on a frontier model, which is the entire reason this category was invented, and why big players moved this fast to give it away.
Oura pulled its Nasdaq IPO one day before pricing, even though demand was reportedly four times higher than the shares on offer. The company blamed uncertainty in the IPO market, but according to a source, the real issue is that shares were set to price at the low end of its $40-to-$44 range.
Oura isn't struggling. It's profitable, expects 90% revenue growth this year, and has 5.7 million paid members. And yet, when a healthy, oversubscribed company still backs out instead of pricing low, something bigger than that company is going on.
Rising bond yields and high oil prices get most of the blame. Two other companies delayed IPOs this month too. Anthropic is reportedly doing the same, likely until after the midterms. If Anthropic, with the strongest IPO story in tech this year, is also waiting this out, it indicates that even the safest bet in the market isn't safe enough to price right now.
Google introduced Gemini 4 Argon, its most powerful model, built for long, complex tasks in coding, enterprise work and cybersecurity. Introductory pricing is $2 per million input tokens and $10 per million output tokens. It undercuts GPT-6 Astra's $10/$50, before rising to $4/$20, which matches Claude Opus 5.5. However, independent evaluators found a mixed picture where Argon ties GPT-6 Astra on Artificial Analysis's overall index, trails Claude Opus 5.5, and ranks eighth on Arena's Agent leaderboard.
Even though its benchmarks look strong, almost nobody gets to use it. At launch, Argon is limited to vetted cyber defenders through Google's Fairwind program, while paid API customers and Ultra subscribers wait with no firm date. Google wants Argon to prove it's still a frontier AI leader. But almost nobody outside one trusted group can use the model right now. So for most people, this is a marketing claim, not a real product yet.
Signals
Tools
Moveworks: An AI assistant platform for your entire workforce to search and act across business applications; used by Unilever and Toyota
Dots by OpenAI: Always on agents built to handle everything
Omnia Agent: The AI agent that does 95% of your GEO/AEO work
Resources
Poll
Choose the prompt you want us to publish next
Market
Funding
Armadin raised $255M to scale its agentic security platform
Ridgeline raised $250M to build AI-native investment management software
Roles in AI
Sr. Director, Product Marketing – AI at Genesys
Data Engineer, Data Foundations at Cohere
Tutorial
Open Claude. Click + and attach your report.
Ask for the deck. Name the audience, the key message and the sections you want.
Note: If you still see separate Chat and Cowork options, you don't have the new experience yet. Slides is a paid beta, starting with Pro and Max.
How to turn a report into a presentation with Claude Slides
Sample Prompt: "Create a 10-slide deck about Q3 results for the leadership team, with sections for revenue, product updates, and team highlights. The main message is that revenue grew but churn rose. Use the attached report. Keep the text short, no full paragraphs."
Answer Claude's questions before it starts building.
Edit by naming the slide: "On slide 3, change the headline to 'Churn rose in Q3' and cut the bullets to two."
You can also ask Claude to add, remove or split slides, like "Expand slide 4 into two slides."
For charts, describe the data: "Add a bar chart of revenue by quarter on slide 2." You can ask for logos and images the same way.
If your company has set up a design system in Claude Design, the deck picks up your colors and fonts automatically.
Present from Claude, or share the deck. On a work account you choose who can view, comment or edit, and everyone with access can chat with Claude in the same thread.
Download it as PowerPoint or PDF and open the file once before sending.
Prompt of the Day
AI ROI Calculator
When to use this?
Use this when you're evaluating an AI initiative and need to turn productivity gains, costs, and business impact into a clear ROI estimate. It's especially useful before presenting an AI investment case to finance or leadership.
Act as an AI ROI analyst. Help me estimate the potential ROI of an AI initiative using the information I provide.
Ask me for the following inputs, one at a time:
AI use case and business process
Number of employees affected
Average employee cost per hour
Current time spent on the process per employee
Expected time saved with AI
Frequency of the process
Expected improvement in revenue, conversion, quality, or other measurable outcomes
AI tool, software, implementation, and training costs
Any ongoing annual costs
Expected adoption rate
Expected implementation timeline
Then calculate:
Annual hours saved
Annual labor savings
Additional measurable business value
Total annual AI costs
Net annual benefit
ROI percentage
Payback period
ROI under conservative, expected, and optimistic scenarios
Show the assumptions and calculations clearly. Separate hard-dollar savings from estimated or non-financial benefits. Flag any assumptions that could materially change the result, and tell me which 3 inputs I should validate first.
Use this formula for ROI:
ROI = (Total annual benefit − Total annual AI cost) / Total annual AI cost × 100
Don't invent missing numbers. If I don't know an input, use a clearly labeled assumption and show how the result changes if that assumption is higher or lower.Get more such prompts in the Prompting Playbook (free for you)
Stay curious, {{first_name | reader}}
PS. If you missed yesterday’s issue, you can find it here.
