Richard Ling on Mastering Technical Sales

Richard Ling is on the GTM team at Groq, a $2.8B AI inference company that dramatically reduces the cost and latency of running AI models. With an engineering background and two startups under his belt (including one exit), he brings a systematic and refreshing perspective to technical sales in the AI space. Richard sat down with Luke from Modern GTM to explore the disciplined practices and problem-solving mindset of the best sales people.

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On asking the right questions over pitching solutions

I've found that in my best selling processes, it's 99% me asking the right questions and 1% presenting the solution.

Solving hard problems is about asking the right questions. Honestly, this goes for everything in life - it's all correlated to asking the right questions.

So when I hop into calls, I'm laser focused on figuring out what their number one business priority is from an AI standpoint.

And my best 30-minute calls go like this: I spend 20 minutes asking questions, almost until they have to go, and then at the last minute I talk about Groq to showcase how our solution actually solves their problem.

In my best selling processes, it's 99% me asking the right questions and 1% presenting the solution.

On going five layers deeper than surface qualification

On the surface, some clients look like they're not even close to a good fit for your solution. But when you dig down one layer deep, you find some ray of light. Then another layer deep, and another…and maybe five layers deep of questioning, you find that this is actually a great fit.

For me, this is the most gratifying part of sales. Getting into the weeds of things. Trying to solve a big hairy problem.

And every few clients, you find some real gold you can help them with.

On disarming prospects through radical honesty

The moment prospects realize you're a salesperson, they're immediately alert. So you gotta make sure they know you're not here to force a solution on them.

I straight up tell clients: "My job isn't to figure out how Groq could work for you. It's actually to figure out how it could not work for you. If we can quickly get those red flags out of the way, maybe we disqualify this altogether and don't waste our time."

This really disarms them because they know my interests are aligned and that I'm not trying to push a solution to a nonexistent problem.

And once they put down their guard, they can actually open up and allow you to help them.

My job isn't to figure out how Groq could work for you. It's actually to figure out how it could not work for you.

On the importance of doing the work

Most people are afraid to do the work at the early stages, which gives a distinct advantage to those who want to do that work.

For example, people don't expect your cold outreach to be written manually, and with genuine, real thought behind it. So when someone does that, it's obvious and appreciated.

I'll go really deep on someone's website and ask myself: "What can we do with this company?"

If the answer is nothing, I don't bother reaching out.

But if the answer is something, I'll dig in.

It takes extreme effort and shouldn't be done for everyone. But if you're really bullish on certain prospects, nothing hits the same as using their product yourself and understanding their pain points as a genuine user.

People don't expect your cold outreach to be written manually, and with genuine, real thought behind it. So when someone does that, it's obvious and appreciated.

On understanding your product fundamentally

In sales, we often think of targets in terms of title or industry. But it's really important to think in terms of the actual problem you're solving.

Take AI customer support agents — there could be customer support agents for hospitals, car dealerships, SaaS companies, and much more.

If you just looked at industry, we'd never think Groq can serve a car dealership startup. But if you map it to the problem statement of needing ultra-fast AI responses, then it absolutely fits.

We need to go into an era where we're not just mapping based on superficial tags, but actually the core problems we're solving. That's one big promise of AI — we can semantically match and find better leads that actually match the problem we're solving.

We need to think in terms of the actual problem you're solving, not just industry or title. That's where AI can help us semantically match better leads.

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Richard Ling's approach to technical sales demonstrates that the best salespeople are problem solvers first, solution pitchers second. By focusing on deep questioning, radical honesty, and genuine understanding of both prospects and products, technical sales professionals can build the trust and insight necessary to create meaningful business outcomes.