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Chips, China, and the AI Reckoning: Reflections on 25+ Years in Semiconductors (Part 2)
Published on: July, 17 2025
Introduction
Maya runs a small web shop that sells specialty teas. Good product, clean site, loyal customers but when people ask AI assistants about “best teas for sleep,” they rarely hear from her. One Tuesday morning she decides to figure it out. This is her story and yours if you run any kind of site.
1) The Question, the Scouts, and the Newsroom
Maya asks an AI: “What teas help with sleep?”
Behind the scenes, the AI doesn’t just run one search. It spins up several versions of the question like “teas for insomnia,” “bedtime herbal blends,” “caffeine‑free sleep tea” and sends them out in parallel like ten quick scouts. That’s fan‑out optimization: many small queries instead of one big bet.
The scouts come back with links, snippets, and data. A stricter referee (a re‑ranker) reads everything and says, “These five pieces are closest to what the user actually wants.” The AI then writes a short answer using the best evidence and, ideally, shows sources.
Why this matters to Maya: if her page only talks about “bedtime blends” but never mentions “sleep tea” or “insomnia,” some scouts may skip her. If her content is muddled or slow to load, the referee’s going to pass.
Plain takeaway: AI search is a pipeline:
rewrite → fan‑out → re‑rank → answer. You “rank” by being easy to fetch, easy to understand, and easy to trust at each step.
rewrite → fan‑out → re‑rank → answer. You “rank” by being easy to fetch, easy to understand, and easy to trust at each step.
2) What “rank” Means in AI Answers
Before the scouts can even find Maya, crawlers have to read her site. Crawlers are automated visitors with name badges like Googlebot, GPTBot (OpenAI), ClaudeBot (Anthropic) PerplexityBot, CCBot (Common Crawl), and others. They follow links, read sitemaps, and respect your house rules:
- robots.txt at your site’s root (broad allow/deny)
- <meta name="robots"> tags in HTML (page‑level rules)
- X‑Robots‑Tag HTTP headers (great for PDFs/images)
Maya checks her rules the same way she’d check a shop sign: Are we open? To whom? For what?
In this world, ranking ≠ magic. It’s: easy to fetch, easy to understand, easy to trust. Maya sketches a plan: make her pages crawlable, speak the user’s language (and synonyms!), and show real expertise with concise, verifiable claims.
3) The Door Policy (robots.txt + Cloudflare)
Before scouts can even find her, crawlers need to be let in. Maya checks two things:
a) Her house rules (/robots.txt):
(“Google‑Extended” is a control token for Google’s AI models; it doesn’t replace normal Googlebot crawling. Use it if you specifically want to opt out of Gemini training/grounding. OpenAI’s GPTBot, ClaudeBot, and PerplexityBot can likewise be allowed or disallowed via robots.)
4) The Map for Machines (introducing llms.txt)
Now Maya realizes: even if bots can enter, how do they quickly understand what matters on her site?
Enter /llms.txt, a proposed open standard by Jeremy Howard. It’s a simple Markdown file at your site root that gives LLMs a curated, compact guide: what your site is about, which pages to read, and (optionally) clean .md mirrors of important pages so models can ingest them without UI clutter. It’s designed mainly for inference time (the moment a user asks an AI about you), and it complements robots.txt/sitemaps rather than replacing them.
That one page gives the scouts a map, not just a door. (Think of sitemaps as “everything we have,” while llms.txt says “start here for the best context.”)
5) Shelves the Referee Loves (Content That Re‑Ranks Well)
With the door open and the map in place, Maya tidies the shelves:
- Answer‑first intros. Every guide begins with a crisp 2–4 sentence summary (the stuff assistants quote).
- Synonyms = more scouts find you. If the page is about sleep tea, also use bedtime blend, insomnia tea, caffeine‑free nighttime tea in natural places.
- Structure helps machines. Tables for dosage/taste/contraindications; short FAQs to match common sub‑questions.
- Structured data (Schema.org). She adds appropriate markup (Article/FAQ/Product/Organization) so machines parse her entities cleanly.
6) The Five‑Minute Keyword Storm: Maya’s Shortlist Machine
Maya’s team drops keyword data into Anion’s in‑app workflow, sometimes 80,000 keywords in a single file. No human can sensibly pick the best 5 to 10 from that pile, so the workflow does the heavy lifting:
- Clean & dedupe. First pass scrubs unnecessary terms and collapses duplicates/near‑duplicates. That still leaves 50,000+ viable phrases, too many to judge by hand.
- Fit & score to the site. We align each keyword to the actual website content and intent, then score it with market signals: volume, competition/difficulty, and trend to produce a single impact score.
- Cluster variants. Synonyms and phrasing twins roll up together (“sleep tea” ≈ “tea for insomnia”), so we’re comparing ideas, not just strings.
- Shortlist in under five minutes. The storm settles into roughly 0.3% of the original list from 80,000 to about 240 high‑impact candidates, each with built‑in analytics (competition, difficulty, volume) so planning is obvious.
- The “full‑score” tip. If the team wants to be ultra‑selective, filtering to only full‑score terms shrinks the list even further, often just a handful, making it trivial to choose the final 5 to 10.
7) Putting It All Together (Maya’s Quick Checklist)
If you want AI assistants to see your content
- In Cloudflare, set Block AI bots = Off. Cloudflare Docs
- Allow the AI bots you choose in robots.txt (e.g., GPTBot, ClaudeBot, PerplexityBot, Google‑Extended).
- Publish a helpful /llms.txt that links to LLM‑friendly .md versions of your best pages. llms-txt
- Use structured data and answer‑first writing so re-rankers love you. Google for Developers
If you prefer to keep AI crawlers out
- In Cloudflare, set Block AI bots = On. Cloudflare Docs
- Disallow the AI agents you don’t want via robots.txt. (Normal search can stay open.)
- Remember: robots.txt is advisory to bots, so network‑level controls matter. Cloudflare Docs
8) The Quiet Epilogue
Just like Maya, you can guide AI to call out your products. Anion can help you open the right doors, organize your content, and appear in your audience’s AI results.
Get in touch today.
Get in touch today.
Add comments…
You know that exhausting feeling of juggling five different tools just to run one marketing campaign. One tool for emails, another for social posts, one for ads, one for website analytics, and endless spreadsheets to track leads. At the end of the month, you still wonder which of these efforts really worked.
That was exactly where one of our clients found themselves.
Let me share how we helped them simplify everything with a single, integrated solution.
From Scattered Tools to a Unified Platform
Initially, their focus was only on basic email campaigns. Messages went out, but results were not tracked. Sales and marketing were not connected, and opportunities were slipping through the cracks.
As their business grew, their marketing needs became more complex. They wanted a way to manage all activities in one place instead of relying on multiple disconnected tools.
That was when we introduced them to unified platform that could manage CRM, email, automation, ads, social posts, and reporting in one place. As you might have guessed, the platform we chose for this client was HubSpot.
Gaining Visibility into Customer Journeys
The first moment our client saw the value of a unified platform was when theilr website was connected to it. Suddenly, with proper cookie tracking and GA4, they could see which pages visitors engaged with, how long they stayed, and which actions they took.
Instead of guessing what visitors cared about, they had real actionable data.
Turning Form Submissions to Actionable Leads
We then added forms that captured inquiries directly from the website and fed them into the CRM. This gave us clear visibility into where leads were coming from and how they were engaging.
Instead of scattered and unorganized contacts, we now had structured data. By segmenting these leads based on interests and behavior, we could personalize communications for each audience group and make every interaction more relevant.
Running Campaigns Without the Chaos
With segmentation established and preferences known, we managed emails and personalized communications, scheduled social posts, and ran ad campaigns directly within the platform, eliminating the need to switch platforms or transfer data.
The best part? All the results were consolidated in a single dashboard. Engagement, reach, CTRs, and were easy to see and compare.
Automating Follow-Ups So Nothing Falls Through
The next major improvement came with automated nurture workflows. They could ensure that every lead received timely follow-ups without manual effort. For example, if someone downloaded a brochure from the site, an automated nurture email was sent a week later. Based on engagement, they either received additional resources or were retargeted with ads.
Follow-ups stopped being manual and started being automated.
Reports that Tell the Story
Every marketer wants to know: Is this effort paying off?
With reporting dashboards our client could finally see performance across email, social, ads, and web all in one place. No messy spreadsheets. No blind spots.
What About AI?
HubSpot continues to evolve. At INBOUND 2025, they announced “Loop Marketing,” which takes AI even deeper into the platform. Tools such as ChatSpot and Content Assistant now support writing emails, suggest social content, build dashboards, and even optimize campaigns.
It feels less like using software and more like working with a teammate.
Final Thoughts
This transformation answered our client’s biggest questions:
- Can it integrate with the website? Yes.
- Can it manage email, social, ads, and reporting in one place? Yes.
- Can it save time with automation and AI? Absolutely.
We work with a range of marketing automation and CRM platforms including HubSpot, Eloqua, and others to help businesses connect their marketing, sales, and customer data in one place.
HubSpot is one of the platforms we specialize in, but our experience covers several tools so clients can choose what fits their needs best.
As a HubSpot Solution Provider, we use HubSpot’s full suite of marketing and CRM tools to help our clients run smarter campaigns, nurture leads, and see measurable results.
Recently, we earned the HubSpot Marketing Hub software certification. It validated our expertise and strengthened our ability to deliver measurable results for our clients.
Combining our semiconductor marketing experience with the latest tools has given our clients end-to-end expertise, helping them scale their marketing efforts more efficiently and cost-effectively.
Add comments…
Introduction
Maya runs a small web shop that sells specialty teas. Good product, clean site, loyal customers but when people ask AI assistants about “best teas for sleep,” they rarely hear from her. One Tuesday morning she decides to figure it out. This is her story and yours if you run any kind of site.
1) The Question, the Scouts, and the Newsroom
Maya asks an AI: “What teas help with sleep?”
Behind the scenes, the AI doesn’t just run one search. It spins up several versions of the question like “teas for insomnia,” “bedtime herbal blends,” “caffeine‑free sleep tea” and sends them out in parallel like ten quick scouts. That’s fan‑out optimization: many small queries instead of one big bet.
The scouts come back with links, snippets, and data. A stricter referee (a re‑ranker) reads everything and says, “These five pieces are closest to what the user actually wants.” The AI then writes a short answer using the best evidence and, ideally, shows sources.
Why this matters to Maya: If her page only talks about “bedtime blends” but never mentions “sleep tea” or “insomnia,” some scouts may skip her. If her content is muddled or slow to load, the referee’s going to pass.
Plain takeaway: AI search is a pipeline:
rewrite → fan‑out → re‑rank → answer. You “rank” by being easy to fetch, easy to understand, and easy to trust at each step.
rewrite → fan‑out → re‑rank → answer. You “rank” by being easy to fetch, easy to understand, and easy to trust at each step.
2) What “rank” Means in AI Answers
Before the scouts can even find Maya, crawlers have to read her site. Crawlers are automated visitors with name badges like Googlebot, GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, CCBot (Common Crawl), and others. They follow links, read sitemaps, and respect your house rules:
- robots.txt at your site’s root (broad allow/deny)
- <meta name="robots"> tags in HTML (page‑level rules)
- X‑Robots‑Tag HTTP headers (great for PDFs/images)
Maya checks her rules the same way she’d check a shop sign: Are we open? To whom? For what?
In this world, ranking ≠ magic. It’s: easy to fetch, easy to understand, easy to trust. Maya sketches a plan: make her pages crawlable, speak the user’s language (and synonyms!), and show real expertise with concise, verifiable claims.
3) The Door Policy (robots.txt + Cloudflare)
Before scouts can even find her, crawlers need to be let in. Maya checks two things:
a) Her house rules (/robots.txt):
She adds clear directives for the bots she’s okay with (Googlebot for search; optional AI‑specific agents like GPTBot, PerplexityBot, and Google‑Extended if she wants to permit their AI use). If she prefers to opt out of certain AI uses, she can disallow those agents while keeping regular search open.
(“Google‑Extended” is a control token for Google’s AI models; it doesn’t replace normal Googlebot crawling. Use it if you specifically want to opt out of Gemini training/grounding. OpenAI’s GPTBot, ClaudeBot, and PerplexityBot can likewise be allowed or disallowed via robots.)
4) The Map for Machines (introducing llms.txt)
Now Maya realizes: Even if bots can enter, how do they quickly understand what matters on her site?
Enter /llms.txt, a proposed open standard by Jeremy Howard. It’s a simple Markdown file at your site root that gives LLMs a curated, compact guide: what your site is about, which pages to read, and (optionally) clean .md mirrors of important pages so models can ingest them without UI clutter. It’s designed mainly for inference time (the moment a user asks an AI about you), and it complements robots.txt/sitemaps rather than replacing them.
That one page gives the scouts a map, not just a door. (Think of sitemaps as “everything we have,” while llms.txt says “start here for the best context”.)
5) Shelves the Referee Loves (Content That Re‑Ranks Well)
With the door open and the map in place, Maya tidies the shelves:
- Answer‑first intros. Every guide begins with a crisp 2–4 sentence summary (the stuff assistants quote).
- Synonyms = more scouts find you. If the page is about sleep tea, also use bedtime blend, insomnia tea, caffeine‑free nighttime tea in natural places.
- Structure helps machines. Tables for dosage/taste/contraindications; short FAQs to match common sub‑questions.
- Structured data (Schema.org). She adds appropriate markup (Article/FAQ/Product/Organization) so machines parse her entities cleanly.
6) The Five‑Minute Keyword Storm: Maya’s Shortlist Machine
Maya’s team drops keyword data into Anion’s in‑app workflow, sometimes 80,000 keywords in a single file. No human can sensibly pick the best 5 to 10 from that pile, so the workflow does the heavy lifting:
- Clean & dedupe. First pass scrubs unnecessary terms and collapses duplicates/near‑duplicates. That still leaves 50,000+ viable phrases, too many to judge by hand.
- Fit & score to the site. We align each keyword to the actual website content and intent, then score it with market signals: volume, competition/difficulty, and trend to produce a single impact score.
- Cluster variants. Synonyms and phrasing twins roll up together (“sleep tea” ≈ “tea for insomnia”), so we’re comparing ideas, not just strings.
- Shortlist in under five minutes. The storm settles into roughly 0.3% of the original list from 80,000 to about 240 high‑impact candidates, each with built‑in analytics (competition, difficulty, volume) so planning is obvious.
- The “full‑score” tip. If the team wants to be ultra‑selective, filtering to only full‑score terms shrinks the list even further, often just a handful, making it trivial to choose the final 5 to 10.
7) Putting It All Together (Maya’s Quick Checklist)
If you want AI assistants to see your content
- In Cloudflare, set Block AI bots = Off. Cloudflare Docs
- Allow the AI bots you choose in robots.txt (e.g., GPTBot, ClaudeBot, PerplexityBot, Google‑Extended).
- Publish a helpful /llms.txt that links to LLM‑friendly .md versions of your best pages. llms-txt
- Use structured data and answer‑first writing so re-rankers love you. Google for Developers
If you prefer to keep AI crawlers out
- In Cloudflare, set Block AI bots = On. Cloudflare Docs
- Disallow the AI agents you don’t want via robots.txt. (Normal search can stay open.)
- Remember: robots.txt is advisory to bots, so network‑level controls matter. Cloudflare Docs
8) The Quiet Epilogue
A month later, when someone asks an AI about sleep teas, more answers point to Maya’s clear, well‑mapped pages. She didn’t trick the system, she helped it: opened the right door, left a map, arranged the shelves, and let the scouts do what they do best.
Just like Maya, you can guide AI to call out your products. Anion can help you open the right doors, organize your content, and appear in your audience’s AI results.
Get in touch today.
Get in touch today.
Add comments…
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