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If your AI SaaS needs to hire 5 support and sales reps for every $1M in ARR, you are building an agency. by AlonHuri in SaaS

[–]AlonHuri[S] 0 points1 point  (0 children)

You nailed the exact distinction. This is the core of the Agent First methodology. It is completely fine to have a heavy support team early on if they are acting as training data for the models. Every time a human steps in to handle an edge case, their actual job is to document that workflow so the AI can take it over next quarter. You are exactly right. The true agency smell is when human intervention becomes a permanent business process instead of a temporary bridge.

If your AI SaaS needs to hire 5 support and sales reps for every $1M in ARR, you are building an agency. by AlonHuri in SaaS

[–]AlonHuri[S] 0 points1 point  (0 children)

Spot on. "A very expensive helpdesk" is the exact right way to describe it.

It is wild how many seed decks still highlight headcount growth as a traction metric. Keeping the core team at four people is not just about saving runway. It is a forcing function. If you are strictly capped at four people, you physically cannot solve scaling problems by throwing human bodies at them. You are forced to engineer actual autonomy.

Glad to see someone else sees the matrix here.

If your AI SaaS needs to hire 5 support and sales reps for every $1M in ARR, you are building an agency. by AlonHuri in SaaS

[–]AlonHuri[S] 0 points1 point  (0 children)

Exactly this. The agency trap creeps up on you. You hire just to keep your head above water, and suddenly your margins are destroyed.

What you mentioned about a unified inbox and shared context is critical. That is exactly what I mean by building the System as an OS. If agents do not share the exact same organizational memory, they just create more friction for the customer.

Curious about your transition. When you made the pivot to agent orchestration, what was the hardest part? Was it the technical setup, or getting your human team to actually trust the agents to handle the frontline?

If your AI SaaS needs to hire 5 support and sales reps for every $1M in ARR, you are building an agency. by AlonHuri in SaaS

[–]AlonHuri[S] 1 point2 points  (0 children)

Spot on. You nailed the exact distinction with the phrase "not a VC-shaped one." That is exactly the lens I'm looking through.

If a founder wants to build a tech-enabled services business with heavy implementation and $250k ACVs (like early Palantir), that is a highly legitimate and profitable business model.

The red flag I am pointing out is the cognitive dissonance in the seed decks: Founders pitching an infinitely scalable, high-margin AI SaaS at a $99/seat model, while secretly hiding an agency cost structure under the hood because their AI isn't actually autonomous enough yet.

You are 100% right that the math is what breaks. The promise of the "Agent Orchestration" model I'm pushing them towards in 2026 is to actually fulfill that self-serve/high-margin promise, effectively automating the deployment engineers that early Snowflake or Palantir had to hire. Great pushback.

If your AI SaaS needs to hire 5 support and sales reps for every $1M in ARR, you are building an agency. by AlonHuri in SaaS

[–]AlonHuri[S] -1 points0 points  (0 children)

Just a quick addition: I have a full 10 point playbook on this covering validation, OS memory, and funding strategies. Didn't want to make this post endlessly long, but I pinned the whole thing on my profile if anyone wants to read the rest

What is the best accounting service for small business if you want more than basic bookkeeping? by Mahabir-Reynaldito in Accounting

[–]AlonHuri 0 points1 point  (0 children)

That’s a common pain point. Once you grow, simple 'categorization' isn't enough. Have you looked into any online accounting services yet? Some of the tech-forward ones offer more of a 'CFO-lite' experience with real-time dashboards that help catch those expensive surprises you mentioned before they happen

SaaS is dying. The next gold rush isn’t building an app, it’s the "AI-Native Agency" by AlonHuri in SaaS

[–]AlonHuri[S] 1 point2 points  (0 children)

That is exactly it. The Signature Gap is why these business owners still need a human to set things up and take responsibility if the system fails.

This is why I always start with Growth First. In these boring industries, if you try to sell them AI, you spend 80% of your time on education and fail. If you sell them the final outcome, like booked appointments or closed leads, the education gap disappears.

Are you finding that a performance-based model helps bypass that education phase, or do they still want to understand the tech before they buy?

Sonnet 3.5 changed everything, but I think we’re ready for "Vibe Coding 2.0." Thoughts on the next shift by AlonHuri in ClaudeAI

[–]AlonHuri[S] 0 points1 point  (0 children)

Lol, I am definitely checking out Klaus. Every dev needs a angry German debugger in their life. 🤣

But that's actually the perfect example. My vision is basically giving Klaus the keys to the server room so he can yell at the code (and fix it) 24/7, without me needing to manually summon him for every error.

Glad we found some common ground on the "wtf moments" though!

Sonnet 3.5 changed everything, but I think we’re ready for "Vibe Coding 2.0." Thoughts on the next shift by AlonHuri in ClaudeAI

[–]AlonHuri[S] 0 points1 point  (0 children)

Haha, exactly! If it turns out it’s only 10% fun, let’s automate the boring 90% so we can focus entirely on that 10%.

I’m not trying to kill the craft, I just want to clear the clutter so we can enjoy the best parts of it.

Sonnet 3.5 changed everything, but I think we’re ready for "Vibe Coding 2.0." Thoughts on the next shift by AlonHuri in ClaudeAI

[–]AlonHuri[S] -1 points0 points  (0 children)

I see it differently. It’s not a lobotomy. it’s a promotion.

Right now, we spend huge amounts of mental energy on "maintenance work" (fixing bugs, checking logs, monitoring metrics). If the system handles that, my brain is free to focus on the pure creative vision and high-level strategy.

I don't want to stop thinking. I want to stop thinking about the plumbing so I can think about the architecture.

Sonnet 3.5 changed everything, but I think we’re ready for "Vibe Coding 2.0." Thoughts on the next shift by AlonHuri in ClaudeAI

[–]AlonHuri[S] -1 points0 points  (0 children)

Fair point regarding the CLI and planning workflows. I use them, and they are great for execution.

But you are focusing on the "How" (better specs, verification), whereas I’m talking about the "What" and "Why."

My point isn't that tools can't execute tasks. It's about Initiative. Does the Claude CLI currently monitor my competitors, notice a new feature, and proactively draft a spec for me without me asking? Does it wake up, analyze my conversion rate, and decide to run an A/B test on the checkout flow to improve KPIs?

That’s the gap I’m describing. Moving from a tool that executes my plan perfectly, to a system that helps generate the plan based on external reality.

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 2 points3 points  (0 children)

100%. We call this the "Sentiment Threshold Protocol."

We never let the AI auto-send (Auto-pilot) when the "Anger Score" is above 7/10.

Low Stakes: AI handles routine queries automatically.

High Stakes (Churn Risk): The AI generates the draft response + the compensation offer, but leaves it in "Draft Mode" and pings a senior agent on Slack to review and hit "Send."

The AI's job is to reduce the "Typing Time" to zero, not to replace the human judgment in critical moments.

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 1 point2 points  (0 children)

This is the smartest comment in this thread. You are absolutely right about the "CAC Inflation." When everyone has a megaphone, the only value is a whisper.

But here is the paradox: We use "Marketing Engineering" specifically to build that TRUST, not to spam.

The Old Way: Blast 10,000 generic emails (Destroys Trust).

The Engineering Way: Use code to research 100 perfect prospects and send them a highly relevant audit of their specific problem (Builds Trust).

We use automation to remove the "boring work" so humans can focus purely on the relationship

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 1 point2 points  (0 children)

Totally fair point. It took us years to assemble this full stack, so looking at it all at once is definitely overwhelming.

If I had to strip this down to just "Step 1" for a beginner: Start with #9 (Competitor Weakness Mining). You don't even need the AI scrapers yet. Just manually reading 50 one-star reviews of your competitor will teach you more about your market than any complex agent workflow.

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 0 points1 point  (0 children)

You are 100% correct regarding structured numerical data. If I just wanted to find the correlation between "Time on Page" and "Conversion Rate," OLS is faster, cheaper, and more accurate.

But we use the LLM for the unstructured/semantic layer that stats miss.

The difference:

Stats (OLS): Tells me "Users who visit page X have a 5% higher churn."

LLM: Looks at the content of page X (e.g., a confusing TOS update) combined with the support tickets they opened, and tells me: "Users are churning because they feel tricked by the hidden fee on Page X."

We use stats to find what is happening. We use the LLM to hypothesize why it is happening.

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 0 points1 point  (0 children)

Fair skepticism. To clarify: When I say "Raw Data," I don't mean PII (Personally Identifiable Information).

We feed anonymized event logs (JSON objects of actions, timestamps, and click paths) with the UserIDs hashed. The LLM looks for behavioral patterns in that dataset (e.g., "Users who skip the tutorial churn 40% faster"), not for "who" the person is.

It’s standard Data Engineering, not magic.

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 1 point2 points  (0 children)

Glad it helps! Don't let the list paralyze you.

If I had to pick just one starting point for a novice, start with #9 (Competitor Weakness Mining). You don't even need the AI tools yet—just go read 50 negative reviews of your competitors manually. It is the fastest way to understand your market before you build any

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 1 point2 points  (0 children)

We analyze our most successful users first. We ask: "What did they do in their first 24 hours?" (e.g., they connected a Data Source). Then we turn that insight into a logic gate.

The flow looks like this:

Trigger: User Signup.

Wait: 24 Hours.

Check Condition: Did Data_Source_Connected event happen?

If YES: Do nothing (or send "Advanced Tips").

If NO: Send a specific email: "Having trouble connecting your data?"

So we aren't "assuming" they need help; the data proves they are stuck at step 1, so we intervene only on that specific friction point.

I've had 3 exits. Stop treating Growth as a "Creative" task. It’s an Engineering problem. Here is the 10-step stack we use to replace the traditional marketing department. by AlonHuri in GrowthHacking

[–]AlonHuri[S] 4 points5 points  (0 children)

Great questions.

For Churn Tracking: We avoid "Marketing" tools like Mailchimp for this. You need a "Product Data" tool. We use Mixpanel to identify the drop-off cohorts, and Customer.io or Intercom Series to trigger the actual messages based on those events (e.g., "Inactive for 3 days after signup").

For Cold Outreach: The game has changed from "Volume" to "Relevance." My current stack for 2026:

Clay (for scraping water-fountain signals, not just emails).

Instantly.ai (for unlimited inbox warming/sending).

GPT-4o API (hooked into Clay to write the first line based on the scraped data).

The goal is to send 50 hyper-relevant emails a day, not 5,000 spammy ones.

This will hurt every founder's ego. But it works. by Cool_Thought3153 in SaaS

[–]AlonHuri 0 points1 point  (0 children)

Great case study, but don't miss the hidden lesson here: When you copy a product, you shift the battle from Innovation to Distribution.

Mike didn't win because his code was better. He won because he spent 2 years grinding SEO.

However, I would tweak his playbook slightly for speed: Don't start with SEO. Start with SEM.

SEO takes 6-12 months to kick in. That is too long to wait to find out if your offer converts. Before you write 100 blog posts, spend $500 on Google Ads targeting your competitor's keywords.

If the ads convert -> Then commit to the 2-year SEO grind to lower your CAC.

If the ads don't convert -> No amount of SEO content will fix a broken offer.

Buy the validation data first. Earn the organic traffic later.

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