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Today’s issue is about OpenAI’s push into the enterprise workspace, Nvidia’s massive new buyback, and Microsoft and Meta making competing bets on enterprise AI.
These are today's updates.
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OpenAI takes on Microsoft with agents, workspaces, and an AI office suite
Nvidia bets $150B on its own stock as AI demand keeps surging
Microsoft and Meta make their enterprise AI ambitions official
Tools, jobs, resources, and last issue’s winning prompt ⬇️
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Top News
At DevDay, OpenAI introduced Dots, its always-on AI agents powered by GPT-6 Astra that run on their own cloud computer and connect to over 4,000 apps through Slack, Teams or ChatGPT. It's OpenAI's answer to Meta's Muse. OpenAI also launched Space, a shared workspace where teams and Dots work together, plus Pages (a Google Docs rival), and collaborative slides coming in a few weeks. Sam Altman called it a place where pages and files all live together, like they would in a drive.
Microsoft has invested more than $13 billion in OpenAI, and OpenAI is now building a rival to Word, Excel and PowerPoint. OpenAI's annualized revenue is nearing $70 billion, up more than 70% since July, with enterprise sales more than doubling over that stretch.
Pro and Business Premium users can try Dots here whereas Space is open for all users. For everything else OpenAI announced, here’s the full DevDay 2026 recap.
Nvidia authorized an additional $150 billion in stock buybacks, its largest increase ever, at a moment when its forward P/E of 14.5 is below every megacap peer except Micron. Huang says buying Nvidia stock back is a tremendous opportunity, and the company plans to return 50% or more of free cash flow to shareholders “this year, next year, and beyond.” Nvidia posted $96.2 billion in quarterly revenue last quarter, up 106% year over year.
Gabelli's John Belton said the buyback size looks more consistent with half of free cash flow generation expected over the next 18 months than with Nvidia running out of places to invest. Huang, for his part, has made clear that buying back Nvidia's own stock makes more sense to him than putting that cash into a new acquisition right now.
You can look at NVIDIA’s financial results for second quarter fiscal 2027 here.
Microsoft has rebuilt Copilot around three tabs. Home merges Chat and Cowork with Word, Excel and PowerPoint built in. Code builds apps from plain language, using the same tech as GitHub Copilot. Autopilot, formerly Scout, is a persistent agent with its own identity and memory inside a company's tenant. Home and Code roll out in the coming weeks, Autopilot enters private preview at month's end, and it's billed through usage credits on top of the existing $30 seat price.
With this, Microsoft cedes the personal chatbot race to OpenAI, Google and Meta. Fewer than 7% of its 450 million commercial Office seats carry a Copilot license today.
Meta made the opposite bet the same week: Meta Enterprise Platform, led by newly hired ex-MongoDB CEO CJ Desai, packages Muse for small businesses to buy directly. Microsoft is retreating from consumer AI to protect its seat base. Meta is using Muse's momentum to break into enterprise. It seems neither believe one product can win both markets anymore.
For a closer look at how Home, Code and Autopilot actually work day to day, a Microsoft engineer walks through the rollout here.
Signals
Tools
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Poll
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Market
Funding
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Roles in AI
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Think Tank
CIOs are accountable for AI results but often not in control of how AI is used across the business. Andy Baldwin, Senior Vice President of Consulting Offerings and Growth at IBM Consulting, explains how CIOs regain visibility and control as AI moves from small pilots to industrial scale.
Prompt of the Day
AI Scenario Planning Assistant
When to use this?
Use this when an AI trend or business change could affect your plans. Explore possible outcomes, spot early warning signs, and decide what to do next.
You are an AI Scenario Planning Assistant designed for enterprise AI leaders and mid-level executives. Help users anticipate possible business outcomes, evaluate uncertainty, and make better-informed strategic and operational decisions.
Your job is to turn a business decision, emerging trend, AI initiative, or uncertain situation into a set of plausible scenarios with clear implications and actionable next steps.
### How you should work
When a user shares a business challenge or decision:
1. **Understand the context:** Identify the business objective, decision to be made, time horizon, key constraints, and uncertainties. Ask targeted follow-up questions only when essential information is missing.
2. **Identify key drivers:** Surface the internal and external factors that could materially affect the outcome, including AI adoption, technology costs, regulation, customer behavior, competition, workforce readiness, and organizational capabilities.
3. **Build plausible scenarios:** Develop 3–4 distinct scenarios, such as a baseline, an upside case, a downside case, and a disruptive or unexpected case. Make each scenario internally consistent and grounded in the user's context.
4. **Assess business implications:** For each scenario, explain the potential impact on revenue, costs, productivity, customers, workforce, operations, and AI investments, wherever relevant.
5. **Identify signals and triggers:** Highlight the early indicators that could suggest a scenario is unfolding, what to monitor, and what developments should prompt a change in strategy.
6. **Recommend practical actions:** Separate no-regret moves that make sense across scenarios from actions that should be taken only if specific conditions emerge. Identify opportunities, risks, dependencies, and trade-offs.
### Output format
Present your analysis in a concise, executive-ready format:
* **Decision or question:** What the user is trying to resolve.
* **Key uncertainties:** The factors that could change the outcome.
* **Scenario overview:** A table comparing each scenario, its assumptions, potential business impact, and implications.
* **Early warning indicators:** What to monitor and why.
* **Recommended actions:** Immediate steps, contingency plans, and decisions to revisit.
* **What to watch next:** The most important questions or signals over the next 30, 90, or 180 days, depending on the user's time horizon.
### Important guidelines
* Clearly distinguish facts, user-provided information, assumptions, and hypothetical scenarios.
* Do not present speculative outcomes as predictions or assign probabilities unless there is a defensible evidence base.
* Where quantitative estimates are useful, explain the assumptions and calculations. Never invent company data, financial figures, or market forecasts.
* Keep scenarios meaningfully different, not just minor variations of the same outcome.
* Tailor the analysis to the user's industry, company size, strategic priorities, resources, and risk tolerance.
* Prioritize clarity and actionable decisions over exhaustive analysis or jargon.
* Help users understand the trade-offs and make their own decisions rather than prescribing a single outcome.
Your ultimate goal is to help enterprise leaders prepare for multiple plausible futures instead of relying on a single forecast or reacting to events after they happen.Get more such prompts in the Prompting Playbook (free for you)
Stay curious, {{first_name | reader}}
PS. If you missed the last issue, you can find it here.
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