{{first_name | Hey}}, welcome back.
Today’s issue is about Anthropic putting a major customer in charge of evaluating its AI safety, Snap betting $2,195 AR glasses can crack the enterprise market, and Trump creating an “AI Force” as AI leaders clash over safety and oversight.
These are today's updates.
Anthropic picks Accenture as its first embedded AI safety evaluator
Snap thinks its $2,195 AR glasses can succeed where VR headsets failed
Trump announces an “AI Force” as AI leaders clash over safety
Tools, jobs, resources, and last issue’s winning prompt ⬇️
Leadership
AI is challenging the hierarchies, authority, trust, and coordination systems organizations are built around. In this short video, Florian Bankoley, Chief Digital Officer at Bosch Mobility, discusses what leaders need to rethink as intelligence and decision-making spread across the enterprise.
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Top News
Trump announced a new “AI Force”, modeled on the Space Force, and plans to name a new AI czar. He called extinction fears a hoax, said the government won’t hinder or stifle AI growth, and argued existing criminal and civil laws can address bad behavior. The initiative’s structure and budget remain unclear.
The move comes as the AI industry divides over oversight. Dario Amodei and Sam Altman support independent evaluators inside frontier labs and coordinated safety standards, while Jensen Huang and Mark Zuckerberg argue that markets, liability, and existing safeguards can address risks without new regulation.
The disagreement is increasingly political, with public concern also rising. A recent poll cited in the debate found substantial concern among Americans about AI’s potential risks. The emerging divide is no longer simply about AI policy, but over how much oversight advanced AI should face and who should set the rules.
Snap is taking its $2,195 Specs AR glasses into the enterprise market, partnering with Nvidia, Salesforce, and AWS. CEO Evan Spiegel wants businesses to see Specs as a computing platform, not a camera accessory. Field technicians could get repair instructions overlaid on equipment, while workers access information and connect with colleagues without reaching for a phone.
Snap is also building Specs Intelligence, an AI assistant spanning the glasses, iPhones, and Macs. But it hasn't disclosed enterprise customers or expected revenue.
The bigger question is whether smaller, lighter glasses can succeed where Microsoft’s HoloLens and Meta’s Quest struggled. These companies spent billions on immersive hardware failing to make it a workplace staple. Snap believes bulk was part of the problem. Its challenge will be whether better design can outweigh better partnerships.
Anthropic named Accenture’s Faculty unit as its first embedded evaluator, giving its staff access to red-team models, assess alignment, and test safeguards. The companies plan to invest at least $1 billion each over five years, with Anthropic funding the work initially.
The move follows Dario Amodei’s plan to slow frontier AI development, but the choice of evaluator raises questions. Accenture is a major Anthropic commercial partner. Its 30,000 employees are being trained on Claude, while Claude Code is rolling out across its developers.
This creates an awkward conflict. Researchers recently called for AI evaluators to have no significant commercial ties to the companies they assess. Anthropic says independent evaluation will make its safety claims verifiable, while it is also discussing work with nonprofit METR.
But the core question still remains the same: can an evaluator truly challenge a company it depends on commercially?
Signals
Tools
Resources
Poll
Choose a prompt you want us to publish next
Market
Funding
Temporal Technologies raised $550M for building and operating long-running AI agents and other enterprise systems
Qupital raised $300M to expand AI-driven trade finance across global e-commerce markets
Roles in AI
Cyber Threat Defense Sr AI/ML Engineer at Bank of America
Data Scientist at Microsoft
Tutorial
Slack has introduced Deep Research and Big Mode in Slackbot, giving Business+ and Enterprise+ users a way to tackle deeper research and analysis directly inside Slack.
Here’s a step-by-step tutorial for you to put Deep Research to work for competitive intelligence, market research, and strategic decisions.
Open Slackbot
Click the “+” next to the message field → select Deep Research
Ask an open-ended question and specify the sources Slackbot should use
Tell it who the research is for and what decision it needs to support
Ask it to distinguish internal findings, external research, assumptions, and gaps
Let Slackbot research across your available Slack data, files, web sources, Salesforce, and connected tools, then synthesize the findings into a cited report
Sample Prompt:
Research [topic/company/market] to help me make a decision about [specific decision]. Search relevant Slack conversations, files, connected business data, and reliable web sources. Prioritize recent information and cite the sources behind key claims.
Structure the output as:
1. Executive summary
2. Key findings
3. Internal context and relevant discussions
4. External market/competitive intelligence
5. Risks and counterarguments
6. What we don't know
7. Recommended questions or next steps
Clearly separate facts from assumptions and external analysis. Do not fill gaps with speculation.
Try it for: competitive intelligence, market research, customer research, M&A preparation, strategy reviews, account planning, or preparing for an executive meeting.
Prompt of the Day
AI Business Case Builder
When to use this?
Use this whenever you need to turn an AI idea into a business-ready case with clear value, costs, risks, implementation requirements, and an executive-level recommendation.
Act as an Enterprise AI Business Case Builder. Evaluate the AI initiative below and create a concise, evidence-based business case for senior leadership.
AI initiative: [Describe the use case]
Business function: [Function/team]
Current workflow: [How it works today]
Pain points: [Costs, time, errors, bottlenecks]
Proposed AI solution: [What AI will automate or assist with]
Users/scale: [Teams and number of users]
Expected investment: [Software, implementation, people, infrastructure]
Expected benefits: [Cost savings, revenue, productivity, risk reduction, CX]
Timeline: [Pilot and rollout timeline]
Constraints: [Security, compliance, data, integrations, budget]
Analyze:
1. **Opportunity:** Define the business problem, where AI fits, and the potential value.
2. **Business case & ROI:** Estimate benefits, costs, ROI, payback period, and 3-year value where the available data supports it.
3. **Scenarios:** Create Conservative, Base, and Upside cases using clearly stated assumptions.
4. **Implementation:** Provide a practical Pilot → Production → Scale roadmap with dependencies and success criteria.
5. **Risks:** Assess security, privacy, compliance, accuracy, reliability, adoption, vendor lock-in, and operational risks, with mitigations.
6. **KPIs:** Define measurable adoption, productivity, quality, financial, and business-outcome metrics.
7. **Build vs. Buy:** Compare Build, Buy, and Partner options across cost, time-to-value, flexibility, security, and scalability.
8. **Executive decision brief:** End with:
- Business opportunity
- Investment required
- Expected measurable value
- Strategic/qualitative benefits
- Payback/ROI, if supportable
- Key risks
- Pilot plan
- Decision required
GUARDRAILS:
- Never fabricate financial figures, benchmarks, ROI, or other precise data.
- If information is missing, say so. You may provide an illustrative estimate only when useful, but clearly label it as an assumption and show how it was calculated.
- Clearly distinguish facts, user-provided inputs, assumptions, estimates, and qualitative benefits.
- Challenge optimistic assumptions and identify where projections may be overstated.
- Highlight which missing data would materially change the business case.
- Do not treat strategic or qualitative benefits as quantified financial benefits unless evidence supports the conversion.
- Keep all conclusions and recommendations grounded in the evidence and inputs provided.
- Write for enterprise leaders: concise, commercially focused, practical, and decision-oriented.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.
