James Raybould

I've recently joined Turing, an AGI infrastructure company, to lead our Turing Intelligence business, building powerful AI systems to answer the question, "How will AI transform my business?" We combine expert human and artificial capabilities to accelerate companies.

I'm a Generalist. After Consulting at Bain pre- and post-HBS, I joined LinkedIn in early 2010 and spent 12+ years moving across functions and leadership roles: I started in Sales Strategy and Analytics before moving to Marketing, Product Management, and, finally, General Management. I then moved to fractional exec roles at Speak, WisdomAI, and StealthAICo, blending product, GTM, and GM skills; I like breadth above all. 

To tell my longer story, 3 dates from 3 decades help:

Sept. 22, 1995: I travelled 5,000 miles from my London hometown to Stanford. I was drawn to studies, sports, sunshine, and a Bay Area culture more focused on creating the future than curating the past. I rowed on the Stanford Varsity Crew team and studied psychology for my B.A. and M.A. And I've stayed close: I can see the Stanford campus from our backyard.

Sept. 13, 2004: I joined Bain Bay Area as an entry-level Consultant. After a few years of racing triathlon and working in sports business, I was drawn to Bain’s entrepreneurial culture and fast-paced learning environment. Looking back, my strategy and comms chops were forged at Bain, as was a lifelong love of Excel, PowerPoint, and Cinnabon.  

March 8, 2010: I left Bain to join a pre-IPO LinkedIn with ~500 employees and ~$100M in revenue. I discovered a vision that deeply resonated - to create economic opportunity for every member of the global workforce - and, second, a set of inspiring leaders teaching me how a culture and values focus could underpin incredible businesses. Over the next 12.5 years, my roles and responsibilities varied: in function from sales strategy, analytics & insights to marketing to product to GMing; in scope from being an IC to leading ~100 person teams; and in scale from helping run our largest, multi-billion dollar recruiting business to jumping in to help lead our acquisitions of Lynda.com (2015) and Glint (2018). 

Outside of work, I'm a long-term elite endurance athlete (rowing > Ironman > marathons > bike racing > marathon swimming) and compulsive reader and film-goer. Here's ALL my books & films (bit.ly/z2D1sX) and I publish a "What's the So What?" newsletter distilling favourite books and experiences into what matters most (bit.ly/3q188jL)

Founding Member & Advisor

I’m excited to be a founding member of Round, a newly launched community for tech’s most thoughtful leaders. Since joining I've benefited in a major way from small "core group" meetings, special events with luminaries, connections with other tech leaders, and more. Starting in early 2023 I've also begun to help the Round leadership with a range of projects to help them grow their impact.

Inspired by my time at LinkedIn, I built a "Relationships Matter" app that shows which of my relationships are heating up or cooling down. Spanning personal and professional, and all my digital channels.

If you've worked at LinkedIn, you'll know "Relationships Matter" well; it's one of the company's foundational values from day one. But actually keeping track of relationships has always required too much manual effort. 

AI advancements and API integrations have changed that.

My app connects to my full digital life: iMessage & WhatsApp, email, phone & FaceTime, LinkedIn messaging and commenting, and my Calendar, including video versus in-person. 

I had fun geeking out on the scoring and features:

- 📞 It weighs the value of different interaction types — an in-person cup of tea vs a FaceTime call vs a quick text vs a 👍 on WhatsApp
- 📉 Diminishing returns on volume — 50 texts in a day isn't 25x more meaningful than 2
- ⏳ Recent interactions count more — that SF stroll last week matters more than a long catch-up two years ago
- ❤️ Personal relationships need more regular contact than professional ones, since I want family and friends to come first
- 🔄 It tracks who's reaching out more — am I more often the initiator or the initiatee?

I’ve no idea if the scoring is right, but it's a fun one to noodle on and super easy to change as I tune it. 

Every person gets a Warmth score and accompanying tier (from Inner Circle down to Dormant) plus a trend (heating up, cooling, or stable).

I've also set targets for folks — whom do I want to be closer to — and the app shows me how I'm tracking and makes suggestions.

And, NO, I won't be sharing my target tiers or what rank you are on my Relationships Matter list, beyond stating that, YES, my wife is the clear #1 ;)

I built this last weekend in <1 day with Claude Code. A year ago, I would never have even considered tackling this. The barrier to building whatever we want is getting lower and lower.

Still very much WIP. What would you want in a “Relationships Matter” tool?

And if we haven’t caught up recently, consider this your nudge. Reach out or expect a ping ;)

#RelationshipsMatter #JFDI

Traders assume a $30K+/yr Bloomberg Terminal. Recruiters assume a $10K+/yr LinkedIn Recruiter seat. AI tools are next. And the bill will dwarf both.

It's starting with engineers. They're already turning down offers over which AI tools they get: Claude Code and Cursor are becoming a requirement — teams still stuck on Copilot are losing candidates. And token budgets north of $100K/year per engineer are coming fast.

And this won't stay in engineering. As every role builds with AI - PMs, marketers, ops leads, etc., more and more roles will hold the same assumptions.

This changes compensation conversations:
- For individuals: "What's my token budget?" will mean total compensation is salary + bonus + equity + tokens.
- For leaders: we'll need to manage token budgets across teams and roles.

Right now, AI tool spend per employee is perhaps 1-2% of headcount costs. That's heading to 10%. Then 20%. I wonder when the first truly AI-forward companies at scale will cross 50%?

I built a LinkedIn profile analyzer app and accidentally discovered that AI can make people feel deeply understood.

On Thursday, I launched "Profiler," a LinkedIn profile analyzer inspired by Miranda Priestley. I designed it to be accurate and instructive but deliberately snarky and judgmental.

Celebrity profiles were a hit (Stephen Curry is "Formidable (6/7)", Tom Brady only "Flickering (4/7)"), and there was friendly competition — only 12 people out of ~2K profiles analyzed so far are "Unforgettable" (7/7). 

But a surprising number of responses highlighted something I didn't design for. People quoted back the lines where AI read their career and landed on something unexpectedly personal:

🌟 "This phrase may be the best compliment I've ever gotten about my work." - Emilia Varshavsky Shapiro 
😊 "I've had a rough year... and the little quote at the top just made my day." — Eileen Lawless
🙏 "I feel so seen. I guess I've done a pretty good job of intentionally ignoring all the cries and rules around personal branding and stayed true to sharing my open heart and mind." — Todd Lieman
🥲 "Tears. Literal tears." — Lara Frankovitch

If we've been lucky, we've had managers or mentors who said something about our careers that connected dots we hadn't connected ourselves and reflected back something we didn't know was visible. That's a rare gift from a person who knows us well.

An AI tool that this non-coder built over a weekend delivered some of that special feeling in under 20 seconds using nothing but a LinkedIn profile — to complete strangers. I don't know any of the people quoted above.

There's a general perception that AI will surpass us on IQ, but EQ is where humans will maintain our advantage. My weekend project just challenged that assumption. Now imagine what happens when this kind of capability is built with intention — with your full work history, your communication patterns, your actual performance data.

If you haven't tried Profiler yet - it's Desktop only - what it sees might surprise you: https://lnkd.in/drcdRyDc

#AIEmpath #EndBoringProfiles

Three years ago, Nvidia's revenue was flat. Literally flat. $26.9B in FY22, $27.0B in FY23. A rounding error of growth. Then this happened:

- FY24: $60.9B (+126%) 
- FY25: $130.5B (+114%) 
- FY26: $215.9B (+65%)

That's +$189 billion of new annual revenue in three years. An 8x increase. A 100% CAGR (Compound Annual Growth Rate) off a completely flat base.

The former Bain consultant in me got nerdy about this, so I went looking for comparisons. I pulled the best 3-year revenue stretch I could find across the major tech companies:

1. Amazon (2018-2021): $233B to $470B; +$237B, 26% CAGR 
2. Amazon (2022-2025): $514B to $717B; + $203B, 12% CAGR
3. Nvidia (FY23-FY26): $27B to $216B; +$189B, 100% CAGR
4. Apple (FY19-FY22): $260B to $394B; +$134B, 15% CAGR
5. Alphabet (2018-2021): $137B to $258B; +$121B, 23% CAGR 
6. Alphabet (2022-2025): $283B to $403B; +$120B, 12% CAGR
7. Apple (FY09-FY12): $43B to $157B; +$114B, 54% CAGR 

Most folks consider Apple's iPhone + iPad era (#7 above) to be tech's defining growth story, but that was +$114B and 54% CAGR. Nvidia nearly doubled it in both absolute dollars and growth rates. WTF. 

One level nerdier: Nvidia has grown by $85B in the past year (+65%). This is #3 all-time for absolute growth: Amazon put up +$106B (+38%) from 2019 to 2020 (peak COVID) off a $281B base, and Apple put up +$91B (+33%) from FY20 to FY21 off a $275B base. 

And going all-in nerd: Nvidia's +$189B revenue growth in 3 years was larger than today's total revenue for each of JPMorgan Chase (founded in 1799), Ford (founded in 1903) and GM (founded in 1908) ;)

You can debate whether AI is overhyped. You can't debate 8x growth in 3 years. 

#WhatWouldJensenDo

"How did you actually build that?" Here’s 1,521 Claude Code messages to answer the most common question I get these days.

Broken into 6 stages, I've pasted every exchange verbatim with Claude (Stage 1: Define and Stage 6: Launch) and Claude Code (Stages 2-5) to build Profiler, a LinkedIn profile analyzer tool:

- The full transcript: https://lnkd.in/dqhUdy9m

In the spirit of "Show, Don't Tell," I hope this helps show the reality of building software with AI in February 2026. A taste of what's inside:

🎭 Defining the voice: "Make sure that it's as though Miranda Priestley gave up running Runway and decided that she wanted to run a LinkedIn profile makeover boutique."

🧪 Keeping Claude code on its toes: "If it failed last time, are you confident this will fix the problem?" Claude Code: "Honestly — no, I'm not fully confident."

😊 Open, honest, and complimentary: “It looks great, nice job.” … “I love it!” … “I think you're right.”

😤 Open, honest, and less complimentary: "Your advice to post it and see what it looks like is absolutely terrible"

🤬 Open, honest, and furious: “I'm IRATE. The bug happened again.” … “WTF”

Overall, building with AI constantly cycles between magic and maddening. From “I can’t believe that 1-hour human task took only 10 seconds with AI” to “My 11-year-old could tell you that makes zero sense.” 

That’s our reality in early 2026, and I hope sharing the full, unfiltered version is helpful for those dipping their toes into the AI building waters.

Tomorrow: why the skills execs already have are their biggest advantage when building with AI.

#ShowDontTell #JFDI

Everyone says AI requires reskilling. They're only partially right.

The skills that matter most when building with AI aren't technical. They're precisely the skills execs have spent their entire careers developing:

1️⃣ - DEFINE the goal
2️⃣ - SCOPE the boundaries
3️⃣ - PROBLEM-SOLVE when stuff breaks
4️⃣ - REFINE with judgment
5️⃣ - MARKET & SELL from day one

Execs aren’t behind on AI. We’re ahead. We've spent decades building the judgment to manage teams; whether you're directing people or AI, the job is the same.

Same skills, different audience.

The barrier isn't technical ability. It's the belief that there is one. You don't need new skills. You just need to start building.

Full article below. #3 in my ongoing “AI for Execs” series.

#AIForExecs #JFDI

The skills that make execs effective leaders are exactly the same skills needed to build with AI. Execs aren't behind — we're ahead. To show, not tell

Open article: AI For Execs #3: Why Execs Are Ahead, Not Behind by James Raybould • 10 min read

AI For Execs #3: Why Execs Are Ahead, Not Behind

In my triathlon days, the people who got fast weren't the ones who prepared the most. They were the ones who sweat the most. AI is no different.

My LinkedIn feed is littered with AI tips, tricks, and debates about whether to use Replit or Lovable. People preparing to build. Not enough folks sharing what they've built.

Worse, all that preparation isn't neutral. It creates a false sense of progress as you feel like you’re getting better at AI, while in reality, the builders are pulling away from you. 

I've seen this same pattern play out three times before:
1. 🏃 Triathlon: So many bought the gear, read the plans, watched the videos, debated the shoes. Did everything except regularly sweat. And then wondered why they hobbled most of the marathon.

2. 📊 Excel: Most of the Excel experts reading this did a stint in banking or consulting. The 17th VLOOKUP or v21 of the model is where it became muscle memory. We don't need to pull all-nighters to learn to build with AI, but it's a whole lot more fun than deal models and decks ;)

3. 👶 Parenting: the jury is still out on whether our 8- and 11-year-old kiddos become upstanding citizens, but my wife and I didn't learn from YouTube or books. We just showed up every day. Imperfectly but constantly.

And if you're not into endurance sports, Excel, OR parenting — why are you seeing my LinkedIn updates? :)

We learn by doing, not by preparing to do. The gap between "I should use AI to build something" and "I built something" is primarily a sweat gap.

I've learned more from weeks of hands-on building than years of reading.

So stop scrolling and fire up Claude Code this weekend alongside me.
Get sweat on your keyboard.
Even better, embrace Wispr Flow and skip the keyboard altogether.

#JFDI

For a non-coder building solo, AI is already so good that it's almost gotten out of the way. What's left is the part that's always been hard: figuring out what to build and why.

I've shipped ~10 projects over the past few months. So far, the tech hasn’t been the bottleneck on any of them. 5 recent examples:

🔍 Profiler: https://lnkd.in/drcdRyDc — a LinkedIn profile evaluator with the personality of Miranda Priestley. The hard part wasn't anything technical, it was making a fictional character funny and helpful at the same time. That's a voice-and-tone challenge.

🎬 My J-Raytings site scoring every film I’ve seen: https://lnkd.in/drNjqRyz (plus /books and /shows). The hard part was design restraint. With 1,700+ films, there’s endless metadata to show, so the challenge was figuring out where more became less. That’s a UX challenge.

⭐ Kiddo Points — an app for my 8 and 11-year-olds to reward a range of habits. The hard part was making sure the points don't become the point — you want kids doing the right thing because they want to, not because they're chasing a score. That's an incentives challenge.

🤝 Relationships Matter — an app to track and strengthen personal relationships. The hard part was building a scoring model for something as complex as friendship. How do you compare a 10-minute text thread yesterday to a 60-minute lunch 9 months ago — and which one matters more for the relationship? That’s a scoring challenge.

🌍 Finally, I advised our 11-year-old on building a geography app: https://lnkd.in/dWCkYQWU.  His problem? How to teach kids about countries and their culture and characteristics, not just quiz them on where they are on a map. That’s a content challenge.

Every example above is a product decision, not a technical decision. We still need designers and engineers to build for scale, but the barrier to shipping something solo is now almost non-existent.

Last night, I built a working AI tutor for my kids in 90 minutes. They’re my beta testers this weekend. Again, the hard part isn’t the tech stuff. It’s deciding how to blend learning and testing and ensuring the tutor’s tone is engaging. Spoiler alert: you can choose from 8 tutor styles, including the perpetual positivity of Leslie Knope from Parks & Rec and the melodramatic menace of General Zod from Superman II.

Yes, there’s a learning curve with tools like Claude Code to do “scary” things like install Python and Node.js or figure out when to use Vercel and Supabase, but 95% of what I’m finding hard are the same product decisions I wrestled with as a PM for years.

The key difference is that waiting months or quarters to ship has become minutes or hours.

If you’re a PM and you haven’t made Claude Code your BFF yet, get rolling. It’ll remind you why you became a PM in the first place.

And if you’re not a PM, it’s the same deal because AI doesn’t care about your job title. It cares whether you know what problem you’re solving.

#JFDI

Specialties: cloud computing, SaaS, enterprise IT, management consulting, business strategy, goal setting and financial planning, quantitative analysis, sales strategy and operations,  customer reference programs,  field enablement, sales training, B2B marketing, brand strategy, customer research and segmentation, offer design, social media, field marketing

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Balakrishna Narasimhan

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