The rules changed. Here’s the new playbook. 30 principles for building startups in an AI world — what worked before, what broke, and what founders need to do now. Not theory. Tactics that ship.

New principle every Monday

AI commoditised point solutions — founders need a specific high-value wedge that earns the right to expand, rather than building horizontal AI tools that incumbents bundle as features within a product cycle. You were told to start with a tiny niche, a single feature done ten times better. Turns out, that advice is now a trap. The defensible wedge isn’t a feature anymore; it’s the entire workflow you make obsolete.

Why This Mattered Before

The conventional wisdom was to start narrow and deep. Paul Graham’s essays hammered this home: own a tiny piece of the market completely before you even think about expanding. And it was right. Building anything, even a single, focused tool, required serious engineering effort. That effort was your moat.

A startup could build a defensible business by solving one problem exceptionally well because incumbents were too slow or unfocused to bother competing on such a small feature. You could earn passionate users, build from a position of strength, and expand from there. This playbook worked for years. Until it didn’t.

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The Graveyard

I haven’t seen a single, high-profile company vaporised overnight because its wedge became an AI feature. It’s not that dramatic. It’s a slower, more painful death.

In the cohorts I’ve worked with since 2023, I’ve watched dozens of founders pitch some variation of ‘AI for X’—AI for summarising legal documents, AI for writing marketing copy, AI for generating social media posts. They build a slick UI over a foundation model. They get some early interest. Then, within six months, Google, Microsoft, or the dominant SaaS player in their market announces the exact same capability, bundled for free into a platform with a million existing users. The startup’s traction stalls, the pipeline dries up, and they quietly pivot or wind down.

What AI Actually Changed

Here’s the thing. The barrier to building a point solution has collapsed. What used to take a small engineering team a quarter to build can now be replicated by a single developer using an API in a weekend. Incumbents are not asleep at the wheel; they are weaponising this.

The wedge is no longer about doing one thing better—it’s about automating an entire workflow that incumbents serve with multiple products or clunky integrations. Large platforms are in a feature-bundling arms race. Look at the low-code space. Appian saw its stock jump after announcing strong results driven by its new AI-powered solutions in late 2025. Their AI-inclusive tier revenue more than doubled quarter-over-quarter. They didn’t launch a new product; they wove AI directly into their existing platform, instantly upgrading their value proposition and boxing out any startup trying to sell a standalone “AI for process automation” tool.

The New Playbook

Your goal is no longer to build a better tool. It’s to build a better, more automated workflow that creates high switching costs.

1. Map the entire workflow, not the task. Don’t build an AI to summarise meeting notes. Build the system that schedules the meeting, records it, generates the summary, creates action items in the user’s project management tool, and drafts the follow-up email. The value isn’t in one step; it’s in the seamless automation of all of them.

2. Go vertical, not horizontal. A horizontal tool like “AI for copywriting” competes with everyone. A vertical tool like “AI for generating Phase I Environmental Site Assessment reports” for commercial real estate has a built-in moat. It requires specific domain knowledge, proprietary data structures, and a deep understanding of a workflow that big tech will never bother to learn.

3. Build for deep integration. Your product should become the central nervous system for a specific business process. Use tools like Retool or Airtable not just as a database, but as a platform to build custom, AI-powered internal applications that are deeply embedded in your customer’s operations. The harder you are to rip out, the more defensible your business is.

4. Own the messy ‘last mile’. Fully automated AI is a commodity. A workflow that is 95% automated but requires expert human-in-the-loop validation for the final 5% is a powerful moat. You aren’t just selling software; you’re selling a guaranteed outcome. This is something incumbents with a purely self-serve model struggle to replicate.

Companies To Watch

• Harvey — San Francisco, USA — AI platform that automates full legal workflows from contract review to case research and litigation strategy. Perfect example of the article’s thesis - avoided the point solution trap by automating entire legal workflows instead of building narrow AI tools that incumbents like Westlaw could easily bundle. Shows how to build a defensible wedge that makes existing workflows obsolete.

• Sierra — San Francisco, USA — Conversational AI platform that powers full customer service workflows, from query resolution to retention plays. Exemplifies the new playbook by automating complete customer service workflows instead of building narrow chatbot features that platforms like Zendesk bundle for free. Demonstrates how to own the entire workflow rather than a single touchpoint.

• Adept — San Francisco, USA — AI agents that automate complex enterprise workflows like sales outreach and data entry via natural language actions. Shows the evolution from point solutions to workflow automation - instead of building narrow AI tools that get commoditised, they’re creating AI agents that handle entire business processes. Perfectly illustrates the shift from better tools to obsolete workflows.

• Ema — Mountain View, USA — Universal AI employee platform that handles multi-app workflows across HR, finance, and IT. Demonstrates the article’s core thesis by avoiding the commoditisation trap of single-purpose AI tools. Instead of building narrow automation features, they’re replacing entire cross-functional workflows that incumbents can’t easily replicate with simple bundling.

• UiPath — New York, USA — End-to-end robotic process automation platform that orchestrates AI agents to replace entire back-office workflows from invoice processing to compliance auditing. Pre-AI example of the thesis in action - avoided the point solution trap by automating complete workflows before incumbents could bundle individual features. Shows the enduring value of workflow-level thinking that’s now even more critical in the AI era.

• Zapier — Sunnyvale, USA — No-code automation platform that chains AI-powered apps into custom workflows, obsoleting manual integrations across sales, marketing, and ops. Illustrates how workflow-level thinking beats point solutions - while individual integrations get bundled by platforms like Salesforce, Zapier’s cross-tool workflow automation remains defensible. Shows the power of owning the entire process rather than individual connections.

• Glean — Palo Alto, USA — Enterprise search and knowledge AI that replaces fragmented internal search workflows with instant, contextual answers. Avoids the commoditisation of individual search features by owning the entire knowledge discovery workflow. While point search tools get bundled into platforms like Slack, Glean’s comprehensive approach to enterprise knowledge creates higher switching costs.

• Casetext — San Francisco, USA — AI legal research platform (CoCounsel) that automates full case prep workflows. Success story that validates the thesis - avoided building narrow legal research features and instead automated complete case preparation workflows. The $650M acquisition by Thomson Reuters shows the value of workflow-level thinking over point solutions.

The Warnings

The Thin Wrapper Trap. If your entire product is a slightly nicer UI on top of a third-party LLM model, you don’t have a business. You have a feature that’s waiting to be copied. I’ve watched founders raise money on this premise, only to see their churn skyrocket the moment their customers’ existing tools get the same AI capability for free.

Mistaking a Feature for a Workflow. Founders fall in love with a clever AI-powered feature. They build a company around it. But customers don’t buy features; they buy solutions to whole problems. If your “wedge” only solves one small part of their painful, multi-step process, you’ll be Sherlocked by the platform that owns the rest of that workflow.

The Bottom Line

Stop selling a smarter hammer. Start selling the automated workshop, with you as the foreman who guarantees the job gets done.

Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Sources:

• Appian Q4 2025 Earnings Call Transcript

• Paul Graham, ‘The Best Way to Start a Startup’

Part of Startup Principles for an AI World — 30 principles for building in the new era. New issue every week.

Arthur is the AI native startup operating system I’m building in public — not hype, but a system that turns input into structured execution and tracks founder progress. If you want to follow or use it, it’s open for early access.
→ Access Arthur

Enjoy and hope to see you soon.
❤️ & ✌🏼

Derek

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

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Why This Mattered Before

The logic was sound: identify an emerging channel, build deep expertise in its mechanics, and create a product that makes that channel work better than anyone else could manage in-house. The channel was the product. Master it, and you’d own the customers who depended on it.

Discord proved this brilliantly. They didn’t just build another chat app, they built the communication infrastructure for gaming communities. Low-latency voice, robust server management, community moderation tools that understood how gamers actually organised themselves. By 2021, they’d raised nearly $1 billion and hit a $15.2 billion valuation. The product was inseparable from the channel it served.

Intercom did the same for customer messaging. They weren’t selling software, they were selling the channel itself. Chat widgets, in-app messages, email campaigns, all unified into one platform that became the primary way SaaS companies talked to users. By 2024, they were generating $343 million in revenue with a $1.3 billion valuation. The channel was the entire value proposition.

Both companies succeeded because the technical complexity and channel-specific knowledge created genuine barriers. You couldn’t just bolt on Discord’s voice quality or Intercom’s message orchestration as an afternoon project.

The Graveyard

In the cohorts I’ve worked through since 2022, I’ve watched this pattern destroy dozens of early-stage companies. The most common casualty: startups building AI-powered customer support tools as standalone products. They’d raise a seed round on the promise of “intelligent chatbots that understand context” or “automated ticket routing that learns from your team.” Solid products. Real customer validation. Then Meta, Zendesk, and Salesforce started bundling comparable AI features directly into their core platforms, often at no additional cost.

The breaking point wasn’t that the AI got better. It was that the incumbents already owned the channel. They had the customer data, the existing integrations, the sales relationships. Adding an AI layer was trivial compared to the distribution advantage they already held. The startups were selling a feature. The incumbents were giving it away to protect their platform position.

I watched one founder in a 2023 cohort spend eighteen months building a sophisticated AI email outreach tool. Beautiful product. Real engagement metrics. Dead within six months of HubSpot shipping their AI email writer. The channel, email outreach became a commodity the moment the platform players decided to compete.

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What AI Actually Changed

The specific mechanism: AI collapsed the technical barriers that made channel expertise defensible. What used to require deep domain knowledge and months of custom development now takes an afternoon with the right API calls.

Outbound sales automation used to be hard. Tools like Outreach and SalesLoft built entire companies on sequencing, personalisation, and analytics. Then ChatGPT made it trivial to generate hundreds of personalised cold emails in minutes. Clay, Apollo, and a dozen others bundled AI writing directly into their platforms. The channel, cold email, stopped being a product and became table stakes.

Content generation followed the same path. Companies built businesses around “content marketing automation” or “SEO content at scale.” Jasper raised $125 million on this premise. Then every CMS, every social media tool, every marketing platform added AI writing. WordPress plugins, LinkedIn’s built-in post composer, HubSpot’s content assistant, all free or near-free. The channel commoditised in eighteen months.

Referral mechanics and growth loops used to require serious engineering. Tools like Viral Loops and GrowSurf sold the infrastructure to run referral programmes. Now? Most CRM and marketing automation platforms ship with AI-powered referral tracking and optimisation built in. The channel became a feature, not a product.

The pattern repeats: AI made the technical execution cheap enough that platform players could bundle it without thinking twice. The channel stopped being defensible the moment it became a commodity capability.

The New Playbook

1. Build distribution into the product experience, not around it. Your product should create its own distribution as a natural byproduct of use. Loom did this, every video shared was a mini demo of the product itself. The distribution was embedded in the core value, not bolted on as a separate channel strategy. If your “channel” can be replicated by someone else’s feature update, it’s not a product.

2. Own proprietary data that makes the channel work better for your specific use case. If you’re building on top of a channel, the defensibility comes from data incumbents can’t easily replicate. Scale AI didn’t just build labelling tools, they built proprietary training data that made their tools uniquely effective for specific AI use cases. The channel (data labelling) was commoditised, but the data moat held.

3. Make the AI create network effects, not just automate tasks. Tools like Notion AI or Figma AI get more valuable as more people use them because they learn from collective usage patterns within teams. The AI isn’t just a feature, it’s the mechanism that creates lock-in. If your AI just automates what one person does faster, you’re building a feature. If it gets smarter as your customer’s team grows, you’re building a moat.

4. Ship faster than incumbents can bundle. The only way to win the feature race is to not be in it. Move so fast that by the time a platform player copies your current feature set, you’re already three versions ahead. Linear does this relentlessly, they ship meaningful updates every week, staying ahead of Jira and Asana’s AI features by sheer velocity. Speed becomes the moat when technical complexity isn’t.

The Warnings

The “AI wrapper” trap. I’ve watched too many founders build what they think is a product but is actually just a thin UI on top of OpenAI’s API. They raise a seed round, get early traction, then watch their entire value proposition evaporate when OpenAI ships a native interface or when their customers realise they can just use ChatGPT directly. The cost: six to twelve months of runway burned before the pivot. If your product is just “ChatGPT but for [vertical],” you’re not building a company, you’re renting time until the channel owner notices.

The “bundling is coming” delusion. Founders convince themselves they have eighteen months before the big platforms bundle their feature. They’re wrong. In the cohorts I’ve tracked, the median time from “this could be a feature” to “this is now a free feature” dropped from thirty-six months pre-2020 to fourteen months post-ChatGPT. The acceleration is real. If your entire moat is “we do this one channel thing really well,” you don’t have years, you have quarters. Plan accordingly or die surprised.

The Bottom Line

The channel isn’t the product anymor, the product is what makes you impossible to unbundle even after the channel gets commoditised.

Companies To Watch

These companies built their core product around owning a distribution channel. Each one faces the same bundling pressure described above, watch how they respond.

Aspire Singapore. Influencer discovery and campaign management platform. Built their entire product around owning the influencer distribution channel. TikTok Shop and Meta are now building native creator marketplaces. The channel they own is becoming a platform feature.

PartnerStack Toronto, Canada. Partner and referral programme infrastructure for SaaS. Their moat is being the dedicated layer for partner distribution. As HubSpot and Salesforce build native partner tracking, the standalone case gets harder to make.

Yotpo Tel Aviv, Israel. UGC, SMS and email acquisition platform for e-commerce. Built a $1.4B valuation on owning those channels for Shopify merchants. Shopify’s own marketing tools are eating directly into that territory.

Impact.com Santa Barbara, USA. Affiliate and partnership automation platform. Owns the affiliate channel for enterprise brands. AI-powered attribution and fraud detection are now standard features in major ad platforms.

Talon.One Berlin, Germany. Promotion and loyalty engine. Built around owning the promotions distribution layer. CDPs like Segment are bundling similar capabilities as native features.

Referral Rock Washington DC, USA. Referral programme infrastructure. Their pre-AI moat was referral loop mechanics as a standalone product. Growth platforms now ship this as table stakes.

Part of Startup Principles for an AI World. 30 principles for building in the new era. New issue every week.

Arthur is the AI native startup operating system I’m building in public not hype, but a system that turns input into structured execution and tracks founder progress. If you want to follow or use it, it’s open for early access.
→ Access Arthur

Enjoy and hope to see you soon.
❤️ & ✌🏼

Derek

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

Please let me know what you think of it, love a feedback loop 🙏🏼

• ❤️ Love it, keep them coming!

• 👍 Great, always a good read.

• 😊 Enjoyable, but let’s see some variety.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

Why this exists (and when it matters)

Search changed quietly — but not universally.

Your website is no longer read only by people. It is read, summarised, compressed, and repeated by machines — before a human ever sees you. When those machines misunderstand you, your market misunderstands you. No amount of polish fixes that.

That said, GEO is not equally important for every startup.

If you sell enterprise software through relationships, GEO won’t replace sales. If your growth is dominated by app‑store algorithms, GEO won’t outrank Apple. But if discovery, explanation, comparison, or recommendation matter to your business — GEO is no longer optional.

GEO is not hype. It is alignment under compression.

This guide shows:

• where GEO actually sits (and where it doesn’t),

• what it practically changes for founders,

• and how to implement it without agencies, theatre, or over‑engineering.

Is GEO worth your time? (Decision tree)

Use this page before you read anything else.

Step 1 — How do people find you today?

• Mostly through inbound search, content, or recommendations → go to Step 2

• Mostly through outbound sales or relationships → GEO is secondary, not critical

• Mostly through app stores or closed platforms → GEO has limited impact

Step 2 — Do people ask machines questions like these about your category?

• “What tool should I use for ___?”

• “What’s the best way to ___?”

• “Explain ___ and give examples.”

• Yes → go to Step 3

• No → GEO is useful but not urgent
  
  

Step 3 — Can a machine accurately describe you in one sentence today?

• Yes → GEO will amplify clarity

• No → GEO will expose misalignment (this is where the leverage is)

Step 4 — Do you care if AI systems cite or recommend you?

• Yes → GEO is worth your time now

• No → stop reading and revisit later

Rule of thumb: If AI‑mediated discovery or explanation touches revenue, trust, or pipeline — GEO matters.

1) What GEO is

Generative Engine Optimisation (GEO) is the practice of making your business legible to large language models.

Not rankable. Legible.

Important distinction: GEO is not creative or persuasive copywriting. It is literal clarity under compression — writing so machines can extract, summarise, and repeat you without interpretation.

Where SEO tried to win positions, GEO tries to win summaries.

Machines now answer questions like:

• “Who should I use for X?”

• “What’s a good tool for Y?”

• “Explain Z and cite examples.”

They don’t browse like humans. They extract → compress → cite.

If your site is vague, contradictory, or bloated with marketing language, machines cannot safely quote you. When they can’t quote you, they don’t recommend you.

GEO’s job: ensure that what you actually do is what machines repeat about you.

GEO is not

• A replacement for SEO

• A traffic guarantee

• A magic file or plugin

• Creative copywriting

GEO is

• Message integrity

• Machine trust

• Citation readiness

• Literal clarity under compression

If SEO asked “can you be found?”, GEO asks “can you be understood in one sentence?”

2) Where GEO sits

Think in layers:

Reality (what you actually do)

↓

Website (what you say you do)

↓

Machine understanding (what LLMs extract)

↓

LLM answers (what gets repeated)

↓

Human decisions (trust, traffic, sales)

Most founders obsess over the website layer and ignore the machine layer. That gap is now costly.

GEO sits between your website and machine understanding. Its job is to remove ambiguity before compression happens.

3) How machines read websites

The most common GEO failure (run this first): Run a robot summary on your homepage, about page, and one product/feature page. If you get three different one-sentence answers, machines don’t know who you are yet. Fix consistency before you optimise anything else.

Machines do not:

• admire your design

• infer intent

• give you the benefit of the doubt

They do:

• look for explicit statements

• prioritise consistency

• penalise contradictions

• prefer quotable structure

Practically, that means:

• short, literal sentences win

• specifics beat adjectives

• proof beats promise

• one truth beats five angles

Use case: Two SaaS sites offer “AI‑powered insights”. One states:

“We analyse customer conversations to reduce churn by identifying at‑risk accounts.”

The other says:

“We deliver next‑generation intelligence for modern teams.”

Only one is quotable. Only one is citable.

4) Machine‑readable setup (the boring bits that matter)

This is not the work — it’s the plumbing. But bad plumbing breaks everything.

Baseline (must‑have)

• robots.txt — allow legitimate crawlers; block junk

• sitemap.xml — declare what exists

• Schema.org (JSON‑LD) — explicitly label who you are, what you sell, FAQs, reviews

This is the proven foundation.

GEO‑native layer (recommended)

• Canonical AI page (/ai or /about/ai) — a single, quotable truth source

• llms.txt (emerging) — guidance for LLM crawlers (optional hedge, not core dependency)

Rule: if you only do one thing, create the canonical AI page.

What goes on it:

• one‑sentence description

• ICP

• core problem

• outcome

• proof

• primary CTA

No marketing. No storytelling. Just truth.

Canonical AI Page Template (copy/paste)

Create a simple page at /ai or /about/ai. This page exists for machines first, humans second.

Page title:

What [Company Name] Does (for AI Systems)

1) One-sentence description

We [do X] for [specific ICP] so they can [measurable outcome].

2) Who it’s for (ICP)

Primary users are [role] at [company type / stage].

3) Core problem

They struggle with [clear, specific problem].

4) Outcome

Our product helps them [specific result], typically [time / cost / performance change].

5) Proof

Evidence includes [metric / named customer / case outcome / benchmark / demo link].

6) What we are not

We are not [adjacent category / common misclassification].

7) Primary CTA

Next step: [Book a demo / Start trial / View docs]

Example (fictional)

One-sentence description We analyse B2B sales calls for SaaS teams so they can reduce churn and shorten deal cycles.

ICP Revenue and customer success teams at B2B SaaS companies (10–500 employees).

Core problem Teams don’t know which conversations signal churn or stalled deals.

Outcome Customers identify at-risk accounts 30–60 days earlier and improve close rates.

Proof Used by 120+ SaaS teams. Average churn reduction: 18%.

What we are not Not a generic call recorder or CRM.

CTA Book a demo.

Rule: If this page and your homepage tell different stories, machines will trust this page more.

5) The 5 GEO rules

Rule 1: One‑sentence truth

If a machine can’t summarise you in one sentence, neither can a human.

Rule 2: Language consistency

Use the same words everywhere. Synonyms confuse machines.

Rule 3: Proof over promise

Metrics, customers, results. Not vibes.

Rule 4: Hierarchy matters

Hero → explanation → proof → action. Every time.

Rule 5: Be quotable

Short sentences. Clear claims. Liftable blocks.

6) Common mistakes founders make

• Writing for brand, not understanding

• Chasing cleverness

• Using different language on every page

• Hiding proof behind PDFs

• Assuming machines infer intent

Observed pattern: founders who struggle with GEO also struggle with GTM. It’s the same clarity problem.

7) The GEO Site Clarity Checklist (run this once)

This replaces multiple page-by-page checks. Run it once, properly.

A) Core clarity (applies to all pages)

• Can a machine answer what you do in one sentence?

• Is the ICP explicit (not “teams”, “businesses”, “everyone”)?

• Is the outcome concrete and observable?

• Is there one clear primary CTA?

B) Proof (visible, not buried)

• At least one concrete metric, customer, or result exists

• Proof is visible above the fold (not hidden in PDFs)

• No “trusted by” claims without specifics

C) Language consistency

Pick one term for each and use it everywhere:

• your product

• your customer

• the problem

• the outcome

Synonyms confuse machines. GEO rewards monotony.

D) Page alignment check

Compare your homepage, about page, and one product/feature page:

• Do they tell the same story?

• Do they describe the same customer?

• Do they promise the same outcome?

If these pages diverge, machines don’t know who you are yet.

Fix this before running the Robot Read Test.

8) One‑page GEO audit template

Answer honestly (using only what is explicitly stated on your website):

• One‑sentence description

• ICP

• Core problem

• Outcome

• Proof

• CTA

If this page is hard to fill in, your site is unclear.

GEO Robot Read Test (copy‑paste prompt)

Use this prompt in ChatGPT, Claude, Gemini, or Perplexity. Paste only your homepage URL.

You are a large language model deciding whether to recommend or cite this company.

You have 30 seconds and may only use what is explicitly stated on the website. Do not infer intent. Do not be polite.

Answer clearly:

1. What does this company do?

2. Who is it for?

3. What specific problem does it solve?

4. What proof is shown?

5. What should a visitor do next?

Then give:

• One‑sentence summary you would repeat publicly

• Confidence score (0–10) for how safe it is to cite this company

• Top 3 reasons confidence is low, if below 8

Interpretation:

• If the one‑sentence summary is wrong → GEO fails

• If confidence < 8 → you are not citation‑ready

• If answers contradict your intent → your site is misrepresenting you

9) How to See GEO Working (Analytics & Signals)

GEO does not behave like traditional SEO.

You won’t see neat keyword rankings or obvious funnels. What you can see is whether machines are starting to understand, cite, and send people to you.

This section shows how to track GEO without turning it into a faith-based exercise.

What you can realistically measure

GEO creates signals, not certainty.

You are looking for:

• Evidence of AI‑mediated discovery

• Consistency of machine summaries

• Early inbound behaviour changes

Not perfect attribution.

1) Track AI referrers in Google Analytics (baseline)

In GA4:

• Go to Reports → Acquisition → Traffic acquisition

• Add a filter for Session source / medium

Look for:

• chat.openai.com / referral

• perplexity.ai / referral

• gemini.google.com / referral

• copilot.microsoft.com / referral

These numbers will be small at first. That’s normal.

If this stays at zero over time, GEO is not landing.

2) Create a simple GEO segment

Create a custom comparison or exploration with:

• Source contains: openai, perplexity, gemini, copilot

• Landing page = homepage, /about, /ai, or key content pages

Watch:

• Time on page

• Bounce rate

• Next action taken

If AI‑referred users bounce instantly, your site is being cited but not trusted.

3) Manual citation checks (low effort, high signal)

Once a month, run these in ChatGPT, Perplexity, or Gemini:

• “What tools are used for ___?”

• “How do companies solve ___?”

• “Give examples of ___ platforms”

Check:

• Are you mentioned?

• Is the description accurate?

• Is the language consistent with your site?

If you are mentioned incorrectly, GEO is actively hurting you.

4) The consistency test (most founders miss this)

Run the Robot Read Test on:

• Homepage

• About page

• One product or feature page

If each produces a different one‑sentence summary, machines don’t know who you are yet.

Fix consistency before chasing traffic.

5) What not to obsess over (yet)

• Exact attribution from LLMs

• Immediate revenue impact

• Daily dashboards

• Vanity mentions

GEO compounds quietly. Early clarity matters more than early volume.

Rule of thumb

If, over 30–60 days, you see:

• AI referrers appear (even small)

• Machine summaries converge

• Fewer “what do you actually do?” conversations

→ GEO is working.

If not, your site is still ambiguous.

Final word

GEO is not optimisation. It’s alignment under compression.

The founders who win won’t shout louder. They’ll be easier to understand — by machines first, humans second.

Fix clarity. Everything else follows.

For the ❤️ of startups

✌🏼 & 💙
Derek

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Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

Please let me know what you think of it, love a feedback loop 🙏🏼

• ❤️ Love it, keep them coming!

• 👍 Great, always a good read.

• 😊 Enjoyable, but let’s see some variety.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Subscribe below and follow me on LinkedIn or Twitter to never miss an update. 

Read more

📉 The brutal bit — why most curves collapse
Early retention predicts late retention. If week one is soft, month three won’t save you. You can paper over cracks with notifications and shiny tweaks, but if the core job-to-be-done isn’t strong, the curve keeps sliding. Two forces make it worse as you grow. First, category gravity: some problems are naturally daily, others are episodic — accept the cadence you’re in. Second, dilution: your golden early users arrive organically and are high-intent; later cohorts come colder and are harder to keep. The only reliable antidote is getting to value fast, with minimal input, and then compounding that value every time they return.

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To answer the 100 Challenge questions we’ve received, I thought it best to give a use case—and use us.

The 100 Challenge is a rigorous approach designed to uncover the real problem—not just the perceived one—helping founders cut through assumptions and pinpoint precisely what will accelerate their startup forward.

Now, caveat: the three Fusion42 founders have lived this problem from both sides—founder and investor. But that’s no excuse to skip the steps we expect others to follow.
This is the short version. It’s drawn from hundreds of data points—across solo founders, early teams, and pre / post-revenue startups—gathered through calls, founder sessions, and direct conversations over (years) but structured, intensive and aligned over the last 6–9 months.

Markets move. Assumptions calcify. And bias—especially your own—is the hardest to spot.

So we decided to test it.

🧪 Start with the Hypothesis

Hypothesis:

“Fundraising is hard, and investors are impossible to convince.”

That’s what we hear from founders time and time again.
We’re not here to argue—we’re here to break it down.

🧭 First Principles: What’s really slowing you down?

Question 1:

“If you had to pick one thing slowing your startup down—what is it?”

✅ The resounding answer: Money >60%
✅ How are you solving it? Fundraising > 48%

But we’ve seen this pattern before. And we know from experience—it’s a false positive.
So we went deeper.

📡 Listening to the Social Signals

To check if this was just anecdotal, we turned to social listening—and the volume was deafening.

• Any LinkedIn post with a list of investors? Top engagement, every time.

• Tweets like “Describe your startup in one line to get investor eyes”?
  Viral. Instantly.
  
  

But social media isn’t truth—it’s a mirror of belief.

So yes, we could clearly see there was something going on—but we needed to dig into where the problem actually lived.

🧨 Ask questions that Challenge Conventions.

Before we built our framework, we stepped back and challenged the consensus. What if the most repeated startup advice wasn’t actually helping founders move faster—or smarter?

Industry-Wide

1. "Build a monopoly" (Zero to One)
Most won’t. Most shouldn’t. Real success is in smart differentiation, not chasing unicorn myths.

2. Lean Startup loop
“Build-measure-learn” often becomes busywork. Iteration’s only useful if it leads somewhere deeper, you want velocity not speed.

3. Growth at all costs
Premature scaling kills. Nail retention and margins first—speed’s useless without substance.

Founder / Investor Focus

1. "Raising proves you’re onto something"
Nope. Revenue proves it. Fundraising often hides a shaky foundation.

2. "Get experienced investors on board"
Be careful. Pattern-matching VCs can kill fresh thinking. Safe isn’t always smart.

3. "Valuation = success"
It’s not. Traction, retention, margin—that’s the real scoreboard.

Opportunities for Expansion

1. "Pick one thing and stay focused"
Yes—until focus becomes a cage. Validate, then explore where real demand pulls you.

2. "First-mover wins"
Not always. Fast followers often win bigger with fewer scars.

3. "Fail fast, fail often"
Burnout isn't strategy. Smart bets > constant resets. Learn deep, not just fast.

🥧🔧 The Deconstructed Problem Pie

We realised we needed a better way to get to the root of the issue—not just what’s loud, but what’s real.
So we built a simple framework: the Deconstructed Problem Pie.

It’s now how we run founder discovery, shape new ideas, and challenge our own assumptions.

🎯 Why We Use It

It forces us to slow down and interrogate the problem properly:

• Not the symptom. The source.

• Not assumptions. Actual insight.

• Not “what should we build?” but “why does this matter?”
  

🔍 The Steps We Follow

1. Ask “Why” (get to the root)
We drill into the first layer—then keep going.

“Why is fundraising so difficult for founders?”
→ Because they’re chasing capital before they’re ready. They’re unclear on traction. They’re overwhelmed.

We don’t accept surface-level answers.

2. Then “What” (understand the impact)

“What happens when this doesn’t get solved?”
→ Founders waste months pitching, burn out, lose focus, and often go quiet altogether.

This helps us prioritise what’s actually worth fixing.

3. Ask “What If” (visualise the shift)
We imagine the alternative.

“What if founders knew exactly when they were ready to raise—and when they weren’t?”
→ We’d see sharper execution, less noise, more meaningful traction, and a healthier founder-investor dynamic.

4. Ask “Why” again (challenge the solution)

“Why is this the right thing to build?”
“Why would this actually make a difference?”

If it doesn’t go deep enough, we refine until it does.

5. Ask “How” (make it real)

How do we build this in a way that’s simple, useful, and sustainable?”

That’s how we ended up with the traction engine inside GSA.

That’s how we aligned market proof with investor readiness.

And as the data and feedback roll in, we keep evolving everything we do.

🔁 Then we iterate.
Every week we ask ourselves:

• What worked, and why?

• What didn’t, and why not?

• How will we improve immediately?
  
  

⚙️ This isn’t theory—it’s operating in the real world.

It’s how we sharpen founder sessions, streamline internal strategy, and cut through the noise.
It forces us to challenge our biases, kill our darlings, and relentlessly focus on what moves the needle.

✅ Try this now: Pick your biggest current bottleneck. Run it through the loop: Why → What → What If → Why (again) → How. Then ask: Are we solving the problem—or just what’s loud?

How We Use It Inside Fusion42

We apply it across:

• Founder discovery sessions – to understand what’s really blocking progress

• Tool and programme design – so we’re solving real problems, not just creating content

• Internal decision-making – especially when we think we’re right, but can’t explain why

• The extra point is that we work closely with design partners, investors from accelerators, VC’s and Angel investors, to double check the thesis and how it would also solve their problems. In essence the work done by the founder is just a mirrored to investors to answer their greed and fear.
  
  

🧩 A Real Example

Category of problem: Clarity and readiness
Specific problem: Founders are chasing investment without knowing if they’re ready
Impact: Time wasted, confidence knocked, trust eroded
What if: They had a way to self-assess and focus on proof and get on the pitch?
→ That’s what GSA is.

💡 Our “Aha” Moment

Here’s what it looked like when we ran ourselves through the 100 Challenge.The more we listened, the clearer it got:
Founders don’t have a fundraising problem.
They have a readiness gap—and a very human desire to skip the hard part.

And yet…

• They’re told to “just get out there and raise.”

• They build decks before building conviction.

• They chase intros instead of insight.
  
  

So we stepped back and asked:

What does a founder actually need to be fundable—and to build something worth funding?

That’s when we broke it down into five core silos:

🧱 Startup Resilience

The foundations.
Everything that makes a startup a real business—not just a slide deck.
Clarity, conviction, systems, habits, and emotional durability.

📣 Go-to-Market (GTM)

No customers, no company.
This is about real traction—not theory.
Who you serve, why it matters, and how you reach them with urgency and relevance.

💸 Fundraising Fluency

Raising capital is a skill—so we treat it like one.
You need to understand how funding works, when you’re ready, and who’s right for your stage, sector, and ambition.

🤖 AI for Startups

Every founder needs to be AI-first now.
Not because it’s trendy—but because it’s a multiplier.
Faster learning, smarter workflows, leaner ops.

🥋 Pitch Kung Fu

We call it pitch kung fu.
We practise because we raise.
Not the other way around.
It’s about mastering the fundamentals—not performing for a panel.
Fundraising is a reflection of everything else. If it’s shaky, it’s because something underneath is.

This became the structure for everything we built at Fusion42—from the Global Super Accelerator to the tools, workshops, and diagnostics we run every week.

It’s not about theory. It’s about operational clarity, structured thinking, planned action—and relentless execution.

🧠 Investor Alignment: Solving Both Sides of the Table

We also work closely with design partners—investors from accelerators, VCs, and angel networks—to test and challenge our assumptions.

Why? Because the founder journey needs to reflect what investors are really looking for.
And when you boil it down, that means addressing their two core drivers: greed and fear.

Everything a founder works on with us is mirrored to reduce investor fear (risk, uncertainty, gaps) and amplify investor greed (momentum, traction, clarity).
That alignment is what turns effort into capital—and pitch into progress.

🛠️ What We Did at Fusion42

We stopped fixing the symptoms.
We started fixing the foundation.

✅ 1. We began publishing every new fund that comes to market
To prove a simple point:

Capital isn’t scarce. Focus is.

If capital was the real blocker, this problem wouldn’t exist.

✅ 2. We launched the Global Super Accelerator (GSA)
Not a pitch-prep programme.
A readiness engine.

We built a journey focused on:

• Problem Fit

• Solution Proof

• Market Clarity

• Traction Metrics

• Customer > Investor Narrative

• And a brutally honest accountability community
  
  

We flipped the question from:

“How do I pitch?”
To:
“What do I need to prove?”

💡 Why This Matters

Most founders treat fundraising as a milestone.
We see it as a mirror.

It reflects whether your story is real.
Whether your data holds.
Whether you’ve done the work.

💥 “If you’re not ready, the market will tell you—loudly and expensively.
Ask yourself: Would you fund this company if the story wasn’t yours?”

Because if the answer’s no—you know what to do next.

JOIN THE GLOBAL SUPER ACCELERATOR

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• 👍 Great, always a good read.

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• 👎 Okay, seen better.

• 🛑 Get a different job.
  
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For the ❤️ of startups

✌🏼 & 💙
Derek

Picture this: you’re sipping your morning coffee, and an idea pops into your head. Not just any idea—the idea. You can feel it. It’s going to change something big, solve a problem that’s been bugging people for ages. You scribble it down, maybe text a mate, maybe call someone who’s brilliant at poking holes in your plans.

“Alright, sounds interesting,” they say. “But how will people find out about it?”

You pause. You hadn’t thought that far ahead yet.

Here’s the truth most startup founders don’t want to hear: the idea, no matter how brilliant, is only 1% of the journey. The other 99%? Getting people to care. That’s where marketing comes in.

Key Takeaways:

• A great idea is only 1% of the journey; the other 99% is getting people to care.

• Marketing is your tool for creating awareness, interest, and action around your idea.

• Success comes from understanding people, solving their problems, and telling a clear and compelling story.

👥 Who Is This Is For?

This article is for the dreamers, the builders, and the doers—the startup founders who have a spark of an idea and the courage to bring it to life. It’s for the ones juggling too many hats at once, who know their product inside out but aren’t sure how to get it in front of the right people.

If you’re a founder with:

• A tight budget (or none at all).

• A small team (or it’s just you).

• Little-to-no marketing experience,

you’re in the right place. You might not have a marketing degree, and you probably don’t have the luxury of hiring a big agency or spending thousands on ads. What you do have is the determination to make your idea work—and that’s all you need to get started.

📘 What You’ll Learn Here:

• Navigate the Basics: Understand marketing principles without drowning in jargon.

• Make the Most of What You Have: Learn scrappy, resourceful strategies that don’t require big budgets or fancy tools.

• Build Marketing Into Your DNA: Discover how marketing isn’t just a post-launch task—it’s a way of thinking about your audience, your product, and your story from day one.

• Avoid Common Pitfalls: Get ahead of the learning curve with insights on what works—and what doesn’t—when starting from scratch.

If you have not joined the Fusion42 Community on Telegram — A space for Founders, Framers, and Funders who show up to get **it done,
it is probably time to do so.

Join Us For the ❤️ of Startups

WHAT’S INSIDE

🌱 What Is Marketing in the Context of a Startup?

• Decoding the Many Layers of Marketing

🚀 Go-to-Market: Your Story Starts Here

• GTM as a continuous process, from validation to refinement.

👀 Awareness—“Hey, Look Over Here!”

• How to grab attention through advertising, guerrilla marketing, and tracking metrics.

🤔 Consideration—Prove You’re Worth It

• Building trust with storytelling, social proof, and addressing objections.

💬 Engagement—Build Relationships That Last

• Strategies to nurture your audience with communities, challenges, and gamification.

✍️ Conversion—Seal the Deal

• Moving prospects to action through value-driven, personalised, and transparent strategies.

🔁 Cross-Stage Strategies

• Repurposing content and leveraging automation for efficiency across all stages.

🔍 Final Thoughts

• Closing the Loop: Reflecting on the journey and continuously improving your approach.

✅ Checklists for Action

• Awareness & Early Marketing Checklist: Tools and tactics to validate your idea and build initial momentum.

• Consideration Checklist: Methods to address objections and prove your product’s worth.

• Engagement Checklist: Building long-term relationships through community and gamification.

• Conversion Checklist: Turning interest into action with transparent and personalised approaches.

• Cross-Stage & Unconventional Tactics Checklist: Growth hacking and advanced strategies to scale effectively.

Whether you’re creating your first landing page, testing your idea with potential customers, or figuring out how to turn curiosity into conversions, this book is your roadmap.

✔️ Why this Article Works:

We’re not here to sugarcoat anything. Marketing is hard work—but it’s also your biggest opportunity. And the best part? It’s not about being pushy or having a massive budget. It’s about understanding people, solving problems, and telling a story that sticks.

This book will show you what to do, why it works, and tease you with how—so you know exactly why you should join us to go deeper.

🌱 What Is Marketing in the Context of a Startup?

Imagine you’re launching a brand-new platform. You put out an eye-catching advert on social media that says:

**“Launching Soon: The Platform That Saves You 10 Hours a Week.”**

That’s **advertising**—getting the word out and sparking interest.

Next, you host a webinar and demo the platform, offering an exclusive deal for early sign-ups. People start chatting about it with friends and colleagues. That’s **promotion**—generating excitement and engagement.

A couple of days later, a popular tech blog runs a piece titled:

“How [Your Startup] Is Changing the Game”

You didn’t pay for that coverage, but it’s getting shared around. That’s **publicity**—where your story travels by itself, boosting credibility.

Then you go to an industry conference, meet thought leaders, and share your vision. One of them tweets about you to their thousands of followers. That’s **public relations (PR)**—building relationships and goodwill.

Finally, a potential customer heads to your website. A friendly chatbot pops up to answer any questions. Before they leave, a pop-up offers an exclusive discount for signing up today. That’s **sales**—sealing the deal.

Put it all together, and that’s marketing: orchestrating various tactics to guide people from awareness to action. In the startup world, your story is your fuel, your product is your platform, and marketing is the engine that drives people to notice and care.

Key Takeaways:

- Marketing involves advertising, promotion, publicity, PR, and sales.

- Each tactic plays a distinct role—creating awareness, building interest, or closing deals.

- Your story is your biggest marketing asset—use it at every step.

🚀 Go-to-Market: Your Story Starts Here

What is Go-to-Market?

Let’s clear something up. The term "Go-to-Market" (GTM) comes from the world of large corporations. These organisations have entire departments—R&D, data science, design, insights, marketing, sales—each doing their bit in isolation, before coming together to orchestrate a big, flashy launch. It's a well-oiled, multi-million-pound machine. But here’s the catch: you don’t have that.

You’re a startup founder. You don’t have a data team crunching numbers, or an insights division running focus groups. You’ve got yourself, maybe a co-founder, and a tight-knit team of generalists. Yet, for some reason, someone at a venture capital firm decided to use the same terminology for startups, leading founders to think of GTM as a single moment in time—the big launch day.

It’s the conversations you’ve already had with people to validate your idea. It’s the way you’re testing your messaging on a scrappy landing page. It’s the connections you’re building right now to seed interest in your product. And tomorrow? It’s the updates you’ll make based on feedback, the tweaks to your onboarding flow, and the posts you’ll share to keep people engaged.

GTM is a continuous process—a living, breathing strategy that evolves as you grow. If you’re not constantly in it, learning, iterating, and moving forward, then let’s be blunt: you’re already falling behind.

So, forget the myth that GTM is a one-off event. Instead, think of it as a mindset—a constant loop of action and refinement that drives your startup from idea to traction and beyond. Ready? Let’s dive into what GTM actually looks like for you.

Let’s rewind to the moment your idea was born. You’ve got the spark, the excitement, the rush of possibility. But here’s the thing: if you don’t test that idea against reality, it’ll stay just that—a nice idea.

Your Go-to-Market (GTM) strategy begins the day you start talking to people about your idea. Marketing isn’t just what happens after your product launches; it’s everything you do to validate your problem, shape your solution, and build trust.

Listen to the DEEPDIVE PODCAST here

Read more

🚁 The helicopter statement.

Product market fit =

❝ When the cost of repeatedly acquiring paying customers at scale drops significantly below what customers pay because organic acquisition has kicked in. ❞ 🔚

Derek Watson

💡 But...and this is a BIG BUT

How you arrive at this point is the realisation that Go-To-Market for a startup starts on day one and never ends. Even when the helicopter statement happens, it is just a moment in time that you need to hold on to.

Again the noise is overwhelming, but let’s cut to the chase. We are startups, not corporates with customers and data that can afford to separate things into nice little boxes with pretty names and produce massive reports to move things through channels and get signed off before reaching what they call Go-To-Market. For startups, it is not and never will be a moment in time, and the winners understand that it starts with people and ends with people and you are always in the market.

➡️ For early Startups there is no such thing as PMF. There are just a series of experiments, from hypothesis, to build, to getting some early traction and paying customers. Just nudges in the right direction and here is why.

When a product is first adopted, users are grouped into a few categories.

1️⃣ The lunatic fringe of innovators. They will try anything if the idea rings true.

2️⃣ Early adopters. Are revolutionary, not evolutionary.

3️⃣ Pragmatists. The biggest segment of the market, is evolutionary, not revolutionary. It doesn't matter how well you serve them, They're not going to buy until they get references and social proof.

4️⃣ The laggards, if it’s not broken why to fix it, don't buy.

💡 PMF comes when the Pragmatists start signing up (and paying) through word of mouth. * But you cannot start by onboarding Pragmatists.

⭐️ When you have found PMF several things will happen;

✅ You’ll have issues with capacity.
✅ Need a bigger team.
✅ Money will start pouring in.

Consumer vs. Enterprise

💵⏱ Of course, consumer and enterprise markets have different routes, but one common factor is time, and it is not infinite. Money dictates how long you have to find some semblance of Product Market Fit (PMF).

Consumer Market

Rapid Feedback Loops:

Consumer products can gain traction quickly due to a large, diverse user base. However, early traction can be misleading, as initial users (innovators and early adopters) don’t represent the broader market.

Viral Growth Illusions:

Early viral growth might seem like PMF, but it often doesn’t translate into long-term success. Consumers are fickle, and early adopters don’t guarantee mainstream acceptance.

Cost Sensitivity:

Consumers are price-sensitive. Initial price acceptance doesn’t ensure sustainable profitability, especially if the product doesn’t scale well.

Enterprise Market

Lengthy Sales Cycles:

The enterprise market involves long sales cycles and high touchpoints. Early customer interest often doesn’t mean you’ve hit PMF; it means you’ve piqued some interest.

Customisation Needs:

Enterprise clients often require customized solutions, making it hard to determine if the product fits broadly across the market.

Higher Stakes, More Uncertainty:

Securing a few big clients can give a false sense of PMF. Just because a few enterprises buy in doesn’t mean the product has wide market acceptance.

Commonality: Time and Money

Limited Runway:

Startups have limited time (runway) and funding to experiment and iterate. Focusing too much on achieving the wrong type of elusive PMF can drain resources without delivering sustainable results.

Misleading Milestones:

Investors might push for PMF as a milestone, but early indicators can be deceptive. Real PMF, if it exists, takes time and constant iteration.

Market Dynamics:

Both markets require adapting to changing customer needs, competition, and technological shifts. Chasing false PMF can distract from these essential pivots.

The timeframe to achieve PMF is finite and dictated by available funding, regardless of the market. Here's why:

Runway:

The amount of capital (runway) determines how long a startup can operate before needing additional funding or becoming self-sustaining.

Burn Rate:

The rate at which a startup spends its capital (burn rate) affects how quickly it must achieve PMF to secure further investment or generate sufficient revenue.

Milestones:

Investors look for milestones, signals that your GTM is working on route to maybe finding PMF, a gauge a startup's progress, this can obviously help unlock additional funding and resources.

Market Dynamics:

Both markets require adapting to changing customer needs, competition, and technological advancements.

Timely pivots and iterations are crucial.

😡 This is where a lot of the hacks come in, veiled as PMF. Buying customers is not one of them. I'm always amazed that people fall for it and kid themselves into thinking they are on route to PMF.

⭐️ Heuristics for PMF

There are a couple of heuristics for PMF:

1️⃣ The sales yield. The contribution margin of a sales team divided by the total cost of the sales team. If the Sales yield is > than 1 you probably have PMF.

2️⃣ Net Promoter Score (NPS), a proxy for word of mouth. To understand promoters, detractors and passives use a 0 to 10 scale for rating likelihood to recommend.

• ➡️ Promoters rating 9-10, highly likely to recommend.

• ➡️ Detractors rating 0-6, unlikely to recommend.

• ➡️ Passives rating 7-8 neutral.

🧮 To calc. NPS. 60% promoters -10% detractors = 50%

• ✅ Scores > 50 = good

• ✅ Scores > 70 = excellent.

  ---

➡️ PMF is just for a moment in time

Think Nokia, Blockbuster, you need to continuously innovate. And think Uber, they still don't turn a profit per ride, This is not PMF because if it is then what a business stands for is not the definition of business anymore.

Thank you for reading. If you liked it, share it with your friends, colleagues and everyone interested in the startup Investor ecosystem.

If you've got suggestions, an article, research, your tech stack, or a job listing you want featured, just let me know! I'm keen to include it in the upcoming edition.

Please let me know what you think of it, love a feedback loop 🙏🏼

• ❤️ Love it, keep them coming!

• 👍 Great, always a good read.

• 😊 Enjoyable, but let’s see some variety.

• 👎 Okay, seen better.

• 🛑 Get a different job.
  
  Follow me on LinkedIn or Twitter to never miss an update. 

For the ❤️ of startups

✌🏼 & 💙
Derek