We’re not good enough (yet)

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

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

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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.

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

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

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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.

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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.

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

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

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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.

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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.

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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.

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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:

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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.

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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.

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

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

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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.

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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.

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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.

Every software company is building AI into their product. As we embark on the Rippit journey, something we have spent a lot of time thinking about over the last 2-3 years is: what are the attributes of great products? 

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In the technology industry, there has been a lot of talk about Forward-Deployed Engineers (FDE). These are people who work with the customers to customize the product to a customer's needs. 

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What they are primarily doing is building prompts for LLMs on behalf of customers.

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This sounds like a great deal for the customer, and it often leads to customers giving their business to the company that offers more services and help.

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Our pre-Rippit experience suggests that this is a death trap for the company and customer within 24-36 months.

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When a software company is small, early customers get the best employees as Forward-Deployed Engineers, and because the company is desperate for early customers, they also under-charge for the human resources they provide. Both of these things have to change for the software company to succeed over time.

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The average customer loses the best FDE involved in their success. The average customer has to pay more for people resources. 

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Because the company has committed to a strategy where humans fill in product gaps, the product gets harder and harder to use over time.

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What ends up happening is only the most important customers have good customer experiences.

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I’ve lived this. I don’t think you can offer the FDE model to deliver a high quality customer experience unless the customer is paying $500,000/Year (±$250,000). It’s very hard to hire enough high-quality Forward-Deployed Engineers to match your growing customer count.

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This is why I think Great AI Products have to be easy enough to use that a customer could learn them on their own. It is the most important criterion when judging a Great AI Product.

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That’s why we’ve committed to this strategy at Rippit — we’re not where we want to be yet, but we are making progress.

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This also seems to be the defining attribute of the best software products we use internally, like Figma, ChatGPT, Claude, Ramp, Cursor, Snowflake, AWS, and more.

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We felt even higher conviction when we evaluated the alternatives for Voice of Customer software, Quality Assurance software, Experience Management software, and Conversation Intelligence software. All of them require FDEs — the signal was that they all had minimum price points of $25,000 to $50,000. That is way too much friction for many potential customers. 

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I’m not sure a single one lets you sign up for the product without talking to a human, which is often another signal of a complex product. The ones I researched were Qualtrics, Medallia, Enterpret, Chattermill, Unwrap, Loris, Level AI, Observe.AI, Cresta, MaestroQA, Balto, and unitQ.

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I think Great AI Products have to be so easy to use that you can sign up and learn them yourself within 10 minutes.

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Not everyone in the industry agrees with me — in fact, I think most won’t.

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Some will argue it’s different for products selling to B2B or certain industries.

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Some will argue that companies with FDEs will use the learnings from customers to build an easier product. The reality is it’s very hard to keep the plane flying just as fast while building an easier-to-use product — and it requires a different product culture. It requires a founder to potentially slow growth down and rearchitect everything, and only the exceptions have the courage to do that. 

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Everyone will admit that deploying AI in all situations will get easier over time — similar to how making a website got easier from the 1990s to 2020.

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There might be an exception but I’m not letting the exception be the rule.

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Lastly, similar to how it’s harder to write fewer words than many words to get a point across, it’s harder to make easy-to-use software than hard-to-use software. I think people who argue for complexity are often scared to step up to the engineering challenge.