Todays Newsletter

Tldr;

Routine tasks keep getting automated—lean into exceptions (policy, escalation, audit), add one AI-in-role CV line, and track time saved, error rate, and $ impact.

UK–OpenAI talks hint at government-scale access; Google’s AI Mode in Africa means more on-page answers; Excel’s COPILOT() helps drafts but has limits; Meta’s talent war continues; xAI sues Apple/OpenAI—distribution rules may shift.

Treat AI answers as a storefront: structure pages for citation and first-touch conversion, diversify beyond iOS/Search, and package small public-sector training/pilot offers where relevant.

String.com shines for event-driven automations—be explicit about trigger → actions, test with sample data, keep human approval on money-touching steps, and note editor limits (e.g., Python steps, multi-triggers).

One-week plan: ship one revenue-linked automation, publish a “Before → After → Result” proof box, send 5 targeted outreach notes with a 1-pager, and log TTV, hours saved/client, and margin weekly.

How to Thrive as AI Changes Jobs: A Simple Playbook

AI is reshaping work. Don’t panic—plan. Here’s a clear, plain-English guide you can use this week.

1) Make your AI use visible

Hiring managers want proof you work with AI.

Add one line to your CV: “Cut review time 38% by using an AI checklist and batch QA; caught 12% more issues.”

In interviews, say what you did, the tool you used, and the result.

2) Own the “exceptions”

AI is good at routine tasks and weak on messy cases. Make those your lane.

List your team’s tasks. Circle the tricky 10–20%.

Write a 1-page rule sheet: what counts as high risk, when to escalate, who approves.

Track two numbers: turnaround time and error rate.

3) Ship one tiny automation

Start small. Show results fast.

Ideas: a spreadsheet rule that flags edge cases, a no-code form that routes risky items to humans, or a short script that batch-checks outputs.

Measure before/after minutes saved and quality changes.

4) Build a mini portfolio (2 pieces)

You don’t need a big website—just proof.

Human-in-the-loop QA pack: sample data, prompts, pass/fail rubric, error log.

Policy/checklist tool: a simple decision tree or form with screenshots.

Add a short readme: goal, steps, results.

5) Beat the ATS (resume scanners)

Mirror keywords from the job post: tools, tasks, metrics.

Use the X–Y–Z line: “Did X, measured by Y, resulting in Z.”

Add a two-line “Tools & Wins” box at the top with 1–2 standout metrics.

6) Run a 30-60-90 reskill plan

• Days 1–30: Take one micro-course. Recreate a public AI workflow.

• Days 31–60: Pilot it on real work. Track accuracy, speed, and errors.

• Days 61–90: Roll it out wider. Write a 1-page impact memo and share it with your manager.

7) Pivot to safer, higher-value roles

From Moderation/QA → policy writing, safety QA, audit, escalation.

From Marketing Ops → analytics, testing, prompt libraries, content QA.

New grads → internships or contract roles in safety/ops; include one tiny AI project with each application.

8) Use a simple outreach script

Send this to a hiring manager or team lead:

9) Measure what matters

Pick three numbers and update them weekly:

1. Time saved (minutes per task)

2. Quality (error rate or precision/recall)

3. Money impact (cost per item or revenue lift, if known)

10) Avoid these pitfalls

Fancy tools, no results. Always attach a metric.

Hiding your AI use. Show your process and your checks.

No baseline. Take a quick “before” snapshot first.

11) One-week quick start

• Day 1: Map tasks; mark the messy ones.

• Day 2: Pick one tiny automation.

• Day 3: Draft your 1-page rule sheet.

• Day 4: Test and record before/after time.

• Day 5: Add an “AI-in-role” line to your CV.

• Day 6: Send 5 outreach notes with your 1-pager.

• Day 7: Write a short memo: goal, steps, results, next move.

💬 OpenAI Developments

OpenAI (UK): “Nationwide ChatGPT Plus” idea floated

A UK minister and Sam Altman discussed—but did not pursue—a proposal to give every UK resident ChatGPT Plus, with estimates up to £2bn. OpenAI and the UK also signed a July MoU to explore AI in public services, signaling government-scale distribution paths that could lower adoption friction for clients. For ventures, this points to rising customer familiarity with premium AI and potential B2G pilots in education, health, and justice.

🚀 Tech Industry Moves

Google launched AI Mode in South Africa, Kenya, and Nigeria, using Gemini 2.5 to break complex questions into subtopics and synthesize answers; users can query by text, voice, or image. Results include source links and fall back to standard web results when uncertain—important for trust and verifiability. If you market to African users, expect more on-page answers and design pages that are easily cited by AI summaries.

Microsoft Excel now supports natural-language prompts in cells for summarizing, tagging, and simple text generation. Microsoft warns it’s not for tasks needing accuracy or reproducibility, and usage is rate-limited (100 calls/10 minutes; 300/hour) with no live web or internal-docs access; a Copilot license is required. Treat it as assistive glue for lightweight reporting or prototyping, with manual checks for anything financial or legal.

Meta: Hiring blitz vs. “frontier” debate: Reporting highlights nine-figure offers (up to ~$200M) to lure researchers and a new Superintelligence Labs push. Demis Hassabis said “Meta right now are not at the frontier,” arguing mission beats money, while Sam Altman called the approach “mafioso poaching” and said he expects to win. Talent churn can open collaboration and recruiting windows for smaller teams, especially via short-term research partnerships.

xAI sued Apple and OpenAI in U.S. federal court (Texas), alleging they locked up markets through OS-level integration and App Store ranking practices; xAI seeks billions in damages. OpenAI called it “consistent with Mr. Musk’s ongoing pattern of harassment”; Apple didn’t comment at publish time. Distribution rules on iOS matter—watch defaults, ranking changes, and any remedies that could open new user-acquisition channels.

Key Takeaways & Opportunities

AI answers are fast becoming the storefront—structure pages with clear headings/FAQs and capture leads on first touch. Use embedded tools like Excel’s COPILOT() for drafts and cleanup, not final numbers; keep human checks and a simple audit trail. Government-scale access (e.g., UK talks) hints at wider premium AI usage—package training and small B2G pilots. And because OS-level deals and App Store rankings can shift discovery overnight, diversify acquisition (email, web, Android, partnerships) and monitor iOS defaults for new openings.

💡 AI Tool Deep Dive

String AI

String is Pipedream’s AI agent builder: you describe the workflow, it scaffolds real steps, lets you test, then deploys straight to your Pipedream account. Think “spec → runnable automation,” without wiring every API by hand.

Where it shines: event-driven work (if-this-then-that) that touches real apps and data—email triage, lead routing, daily research digests. It’s not ideal for general chat, one-off queries, or front-end apps.

How to get reliable results: write a prompt that names (1) the trigger (“new Typeform submission at /webhook”), (2) the source app, and (3) the actions (“create HubSpot contact; post Slack summary”). Break big goals into steps and avoid long, hazy prompts—this cuts token cost and rebuilds.

Dev loop: click Edit/Debug with AI, run tests with sample data, inspect outputs, then Deploy. Start small, iterate, and keep humans in the approval step if the task affects customers or money.

Limitations to plan around: AI editing currently can’t handle workflows that use Python steps, multiple triggers, control-flow blocks, GitHub Sync, or Pipedream Connect features. Use Node.js steps when you need custom code.

Starter play: “Typeform lead → enrich → create HubSpot contact → Slack approval → Gmail follow-up.” String speaks thousands of apps, so name what you use explicitly.

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