The Ripple Effect

The 10x engineer isn't more productive because they squeeze more output in the same time as the 1x engineer. The 10x engineer is more creative and a better problem solver. The person has better judgement.

‍

This is true for every person, in any role.

‍

This is what AI will help people do the most.

‍

AI can improve judgement by giving a person new insights they didn't have before. AI can improve judgement by helping you run experiments and prototypes faster.

‍

Using Rippit is one way to improve judgement — you can get instant insights into a dataset that wasn't possible to analyze before.

‍

Be curious and discover secrets with AI to improve judgement.

‍

Don't focus on incremental improvements. Seek 10x.

In my prior post, I talked about the Model versus Tools. The next interesting point around tools is who is going to be the primary user of them.

‍

Pre-2026, the primary users of these tools were humans. Post-2026, it will be AI Agents.

‍

Let’s say a human currently goes through 10 steps to achieve a goal. AI will eat away at each of those steps so that a human only has to do 8 steps, then 5 steps, and so forth — with the holy grail being 100% automation. 

‍

This is far more complicated than it sounds, but it gives you a sense of where products are pushing.

‍

A key part is connecting a tool to as many different AI agents as possible.

Tactically, Rippit has tools to analyze conversation data. We use our own “brain” to leverage the tools, but we also want to make those tools available to other AI agents in other products. For example, Claude Code or Cursor should be able to use our tools to get customer insights for building features, and Figma, Replit, or Lovable should leverage customer insights to better design user experiences or marketing assets.

There has been infrastructure as SaaS for two decades. Think AWS, Snowflake, Datadog, Heroku, Databricks, Confluent, Stripe, Twilio, Crowdstrike, and more. These tools were built for engineers, extremely fast performance, or very large-scale datasets.

‍

There are also SaaS applications. Think Salesforce, Hubspot, ServiceNow, Workday, Okta, and more. These tools were built for non-engineers.

‍

Some of the above SaaS applications have evolved into infrastructure and AI won’t affect their role in the future as much — for instance, Salesforce is more like Snowflake than it is like Monday.com.

‍

AI is commoditizing, neutering, and sometimes killing thinner SaaS applications that are more workflow than infrastructure.

‍

What AI is not doing is killing software. Software is going to explode in value because of AI — if you turn your SaaS application into infrastructure. 

‍

The CEO of Intercom just did this last week when they released the Fin API. ElevenLabs is “SaaS as infrastructure,” whereas Decagon and Sierra are SaaS.

‍

Hence, AI will force winning SaaS companies into infrastructure.

‍

How SaaS as infrastructure will be different from traditional SaaS

‍

SaaS apps are built with the expectation that a human is the primary user. SaaS as Infrastructure is built for an AI primary user.

‍

When AI is the primary user, there are two implications:

‍

1. The intensity at which AI wants to use a tool is infinitely higher in volume and scale than a human. To meet those demands, you need to build a product that looks more like infrastructure. Software needs to support a button clicked 1000 times per second (AI speed) versus 1 time every 30 seconds (Human speed), as a crude example.
2. Data requirements to make AI useful go up — you need to get world-class at the concept of Context Intelligence. Context Intelligence is a data-infrastructure problem that requires ingesting arbitrary datasets, modeling them in custom ways, and coordinating compute resources to execute arbitrary data jobs.

‍

How SaaS as Infrastructure will be different from infrastructure as SaaS:

‍

Infrastructure as SaaS is built for technical folks. SaaS as infrastructure will be built for non-technical folks, and AI is a critical enabler to give non-engineers the superpowers of engineers. 

‍

I think Replit and Claude Code are two of the best examples of this type of company today. A non-technical user can execute a wide range of large and small tasks. They have built tools, frameworks, and infrastructure where AI is the primary user of “clicking buttons” and the human is expressing intent. 

‍

If you’re unclear on what I mean by tools, frameworks, and infrastructure, I talk about it more in It’s not the Model, it’s the Tools + Environment. You can also ask Claude Chat or Claude Code, “what tools do you have at your disposal?”, and it will give you a list. But it’s the tools, frameworks, and infrastructure that make the human user receive a fast output they can trust.

‍

At Rippit, we’ve been preparing for this future since 2023. We didn’t know it would play out exactly like this, but we knew right when ChatGPT came out that our original product was going to get massively commoditized, and we needed to chase harder technical problems.  

‍

A little bit of foresight and a little bit of luck creates the Rippit opportunity.

‍

Note: This concept is still being refined, but it is a directional set of statements. Please share ideas to push the thinking and refine the logic.

Pick things that compound over time.

‍

Wake up looking forward to the present.

‍

Talk about ideas and projects of the future. 

‍

Reminisce less. The good old days are ahead of you, not behind you.

‍

What ages like wine?

‍

To each their own.

‍

One hack is to have extremely low expectations to start. 

‍

The other hack is to reset your mindset today — no matter your age — and start over. 

‍

There's no better time to start the ascent than today.

‍

This applies to companies too — is tomorrow potentially better than today?

‍

Just Rip It.

Traditional business application software from 2000-2022 (e.g. software sold into HR, Sales, Marketing, etc.) has been primarily workflow automation tools — Salesforce started with SFA (Salesforce Automation), ServiceNow started with IT Workflow Automation, and Workday is HR workflow automation. 

‍

The core value proposition was coordinating large groups of people around a process.

‍

When you wanted to do complex data analysis — crunch lots of numbers, combine data from different data sources, etc. — you would push that data into a data warehouse (Teradata in the 2000s, later replaced by Snowflake) and layer it with a BI solution (like Tableau or, more recently, Sigma/Looker).

‍

SaaS apps that lived in the world of Security and Infrastructure were more likely data/infra apps from the beginning — companies like Rubrik, Crowdstrike, and Datadog fit this mold.

‍

The type of engineers and cultures you attract to build workflow apps versus data apps is materially different.

‍

AI requires a higher percentage of SaaS apps to become data apps. 

‍

99% of apps will die in this process because they have to build a fundamentally different engineering culture. Only 1% will survive.

‍

Rippit realized this three years ago and embarked on this journey — partly through our instinct of how SaaS needed to transform, and partly through a little bit of luck. 

‍

This also transforms my role, and I’m going through my own journey of survival. As a non-engineer, being the founder of a workflow app is significantly less technical and more S&M-oriented than being the founder of a data app. 

‍

My Monday morning meeting used to be the Go-To-Market kickoff and now it’s the Engineering kickoff. I’m working very hard to become as effective as I can at lower levels of product decisions — my ceiling here directly affects Rippit’s ceiling.

When you use Claude Code, you see the same model (ex. Opus 4.6) is used across Claude Code, Claude Cowork, and Claude Chat. Then why does it perform differently? The tools and environment.

‍

Think of the model as the brain and the tools as the arms, hands, legs, and feet.

‍

Claude Code has many coding-specific tools. 

‍

What you need for your task will require specific tools, environments, and optimizations too.

‍

Designing the best tools and environments is going to be the name of the game.

‍

For example, if you want to build an analytics system, you connect Claude to Snowflake. In that case, Snowflake is the tool. Snowflake is the hard part of the problem and 99% of companies don’t have the engineers to build products as technically difficult as Snowflake.

‍

This has become more obvious since the code behind Claude was leaked the other week and people studied it - but it’s not obvious to the regular person blown away by Claude Code and “Vibe Coding”. When you experience a crazy, mind-blowing output, it's hard to separate the 'AI' from the tools and systems doing the mission-critical heavy lifting.

‍

The world of Tool Design is going to go through a lot of trial, iteration, and realization — we have our differentiated POV but the playbook is being written.

Our product vision is unique and high-potential, but it’s the hardest thing we have ever set out to build.

‍

In our All Hands on Tuesday, I expressed to the team: 

‍

What we’re trying to build is the hardest thing we’ve ever tried to build.

We have a 60% chance of building our vision at a high enough quality.

But if we do — it is going to be an amazing product.

‍

Oh, and the world is moving so fast that we have less than 12 months to prove a couple of key technical milestones.

‍

That’s how it should be — it is meant to feel scary and exciting at the same time.

Watch out for AI companies that make money the more you spend on AI. 

‍

They are charging some hard-to-tell upcharge on AI, which they package up as AI credits.

‍

When a company charges this way, they are not incentivized to build cost-efficient AI because the more you spend on AI, the more money they make.

‍

For example, they might use more expensive AI models when they could use less-expensive ones.

‍

They could have the AI run for 15 seconds longer, in what they call “Thinking Mode,” and make more money in those 15 seconds.

‍

When the AI makes a mistake, as long as it’s not so often that you cancel the contract, they are less motivated to fix the error rate because every mistake actually earns them money.

‍

In a world where AI is becoming like electricity, and everyone has access to the same AI models, we believe software companies should not upcharge for AI directly.

‍

This ensures the company’s incentives are aligned with customers' best interest.

‍

I am not sure how most Conversation Analytics solutions charge because pricing is not transparent on the website.

‍

However, if a Conversation Analytics solution does not have usage-based pricing for AI, that is a different red flag. What this suggests is you will not have the required flexibility to do all the AI analysis you might want.

‍

When a solution is not charging explicitly for AI, they need to make sure that if you pay them $10, you don't consume more than $10 worth of AI. They probably need to make sure you don’t consume more than $4. So, they will throttle AI usage and impose constraints to ensure costs stay below that threshold — which can result in lower quality intelligence.

‍

We think it’s critical that customers can pay for AI on a usage basis, but to avoid the first trap, they should charge for AI at cost or as a pass-through.

‍

I wrote this post before Clay announced their pricing change, and I originally intended to use them as a poster child for the pricing trap they found themselves in. However, kudos to the Clay team for changing their pricing model the right way. Read about it as another example of what I’m articulating above and similar to our approach:

‍

Clay Pricing Change Public Announcement

Clay Pricing Change Internal Memo

I was moderating two panels at our customer conference in San Francisco the other month — one with Checkr and one with SpotOn.

‍

The day before, I was finalizing my questions and deck, and I realized my notes from my prep calls with the customers had some logical gaps.

‍

I went into Rippit, filtered our Gong table to all Checkr calls, and started asking questions using AskRippit. I did the same with SpotOn.

‍

Afterward, I asked the marketing team a simple question: “Where else could I analyze 50 Gong calls in one shot as easily as Rippit?”

‍

If you downloaded the 20 calls out of Gong 1 by 1 and then uploaded it into Claude, you have a chance, but for reasons I’ll share later — you lose accuracy.

‍

That led me to think about how I prepared my deck. I used Gamma instead of Google Slides. I tried Google Slides first, but the AI wasn’t good enough — so I was willing to pay for Gamma. For me, in my role, I’ll pay for whatever helps me deliver the best presentation.

‍

The same goes for conversation analysis for my use cases — I’d rather pay for Rippit than try to hack it in ChatGPT or Claude.