{{first_name | Hey}}, welcome back.
Today’s issue covers a major chipmaker securing Amazon as a key customer, OpenAI’s new workspace, and software companies expanding into hands-on AI implementation.
These are today's updates.
Build compliance into your AI stack with Vanta*
Qualcomm gives Amazon $4B in warrants for AWS chips
OpenAI launches ChatGPT for financial services
Google and Accenture train 1,000 AI deployment engineers
Tools, jobs, resources, and last issue’s winning prompt ⬇️
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Top News
Qualcomm issued Amazon warrants for 25 million shares (~$4B), vesting as Amazon buys up to $60 billion in Qualcomm server chips over a multi-generation custom silicon deal for AWS. Qualcomm is also one of three chipmakers, alongside Nvidia and AMD, supplying the EU's new AI gigafactory program.
This comes at an interesting time when overall AI chip demand is still outpacing supply, which is why prices keep climbing. Qualcomm is trying to break into a datacenter chip market Nvidia dominates, and it is paying equity as the price of entry for winning a marquee customer like AWS.
AMD paid OpenAI a similar way to secure its own foothold. The new entrants will compete hard on terms, equity, pricing, custom silicon, to break into your supply chain. For buyers, this creates negotiating leverage, even while chip supply remains tight.
OpenAI released a version of ChatGPT made for investment banking and equity research, with Morgan Stanley and Evercore as design partners. It runs on GPT-6 Astra and comes with built-in financial data from LSEG, Daloopa, PitchBook, and Crunchbase, indexed on OpenAI's own infrastructure.
It ships with prebuilt workflows for LBO modeling, buyer screening, and valuation analysis. Banks can also upload their own Excel, Word, and PowerPoint templates to get an output in their desired format. With this, a banker doesn't have to find comparable company data or figure out how to format a valuation model.
Firms that already hold data subscriptions can connect them through integrations with FactSet, S&P Global, Preqin and Datasite, the company said. OpenAI said it plans to expand the product across the wider financial services sector. Notably, Anthropic already has a similar product, with its own data partnerships across LSEG and S&P.
Accenture and Google Cloud formed a unit that will train up to 1,000 forward-deployed engineers to install Gemini Enterprise on-site at client companies. It's the fifth such deployment unit launched this year, following similar moves from Microsoft, AWS, Anthropic and Blackstone, and TCS. Accenture CEO Julie Sweet says clients are telling her that AI promised outcomes aren't happening without hands-on help.
Accenture is now a named partner in both Microsoft's and Google's deployment programs simultaneously. If big tech firms need 1,000 trained engineers per partnership, it signals an expansion from software to services. This means budgeting for AI deployment should now account for implementation as an added bottleneck and cost, instead of just the model itself.
Signals
Tools
Resources
Poll
Choose the prompt you want us to publish next
Market
Funding
Upwind raised $300M to build AI and cloud security
Lyte raised $165M to scale robotics perception technology
Roles in AI
Tutorial
How to set it up:
Open ChatGPT Work on the web or click on this link → ChatGPT Work
Open Settings → Personalization → Writing style
Connect your work apps (Gmail, Google Drive, Slack, or SharePoint). You’ll find it in the Profile menu → Settings → Connected Apps/Apps
Choose the apps that have your best writing style examples.
Sign in and follow permission prompts (If you don’t see this option, your plan or workspace administrator may not support it.)
Prompt of the Day
AI Coding Rollout Evaluator
When to use this?
Use this before buying or expanding an AI coding tool. It helps leaders to evaluate beyond ‘engineers like it’ and whether the tool actually improves engineering output, quality, cost, and workflow.
Act as an AI engineering advisor helping me evaluate whether my team should adopt an AI coding tool.
I’m considering [TOOL/TOOLS] for [TEAM/SIZE/TYPE OF ENGINEERING TEAM].
Evaluate the rollout across:
Productivity: Which coding tasks are likely to get faster, and by how much?
Quality: What risks could increase, including bugs, security issues, technical debt, or poor code review?
Workflow fit: Where would the tool fit naturally into our existing development process?
Team impact: How might it change the work of junior, mid-level, and senior engineers?
Cost: Estimate the likely ROI based on [TEAM SIZE, TOOL COST, CURRENT ENGINEERING COST].
Governance: What rules, permissions, data controls, and review processes should we establish?
Rollout: Recommend a 30/60/90-day pilot with clear success metrics.
Separate measurable benefits, assumptions, and risks. End with a recommendation: roll out, pilot, or wait, and explain why.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.
