Jim Larrison

Serial founder (Dynamic Signal, Firstup, now building new start-up Larridin) | Early exec @ comScore & Adify | Scottsdale based 🌵

[Jim Larrison @jlarrison]

BIG DAY here at @Larridin! We just announced our $30M Series A, led by @pelion_vp, with continued support from @a16z and @GradientVC , and new investors including GFT Ventures, W23 and others.

Big news for sure, but what really excites me is what it means for our customers & partners. The investment goes directly into products & services, with a focus on ensuring they all see value from AI.

We have been lucky enough to work with leading companies across biotech, media, construction, tech, consulting, government, and plenty of other industries. Their businesses are different, but the question we hear is remarkably consistent: Is AI actually making our people and our business better?

They all deserve a partner who cares about what happens after the AI investment is made...

That’s what we’re building at @Larridin. Providing true visibility into what's working, where people need support, and where AI can make a meaningful difference in how work gets done.

[Jim Larrison @jlarrison]

Thanks to the @BusinessInsider team for sharing our story. I am looking forward to sharing with the world all the amazing things we are doing with our customers and partners everyday!

[Business Insider @BusinessInsider]

"We said, look, there's going to be a ton of AI spend," Larridin CEO Russ Fradin said. "There's going to be a third-party audit measurement company, and let's try and be that company." https://t.co/cMozvNiV1E

[Dr. Abdul El-Sayed @AbdulElSayed]

This is Tommy. Tommy had a question about my allegiances. Listen to the end to hear about his.

[Ameya Kanitkar @aktwits]

How engineering leaders are changing the org chart for an AI-native SDLC:

Our survey:
• 40.8% moved to product/outcome teams
• 19.4% changed seniority/role mix
• 15.3% added roles in verification, agent platforms and AI enablement

https://t.co/LhiBlWo9fC

[Jim Larrison @jlarrison]

@RGIII @NUFBFamily @OuttaPocketRG3 Hecky’s write up to check out. He was an amazing man and the food is the best!!

[The Daily Northwestern @thedailynu]

It surely must be the sauce! Hecky’s Barbecue received the Chicago Tribune’s Readers’ Choice Award for the best Chicago-style BBQ. #EvanstonNews
—
Reported by @katewalter03 https://t.co/4YkQKyiQBs

[Jim Larrison @jlarrison]

@RGIII @NUFBFamily @OuttaPocketRG3 Since you will be in Evanston you MUST hit up Hecky’s bbq it is amazing. Get the Rib Tips!!!

[Jim Larrison @jlarrison]

Love to see @CALEBcsw tackling Case after such a great game! This is real support for your teammates! @BarstoolBigCat

[Jim Larrison @jlarrison]

@wasram Well we get to see how “amazing” Tyson Bagent will be 😂

[Jim Larrison @jlarrison]

@djchumlee Humm. Losing?!?

[Pat McAfee @PatMcAfeeShow]

"The history of football is defined more than anything by the great quarterback and coach combination..

I love Ben Johnson and Caleb Williams..

I think Ben Johnson is brilliant" ~ @Espngreeny

#PMSLive

[mentalium @mentalium_]

Wall Street paid Jordan Belfort $750,000 a year to sell like this and now you can watch him do it live for free

22 minutes. Real prospect. Real close. The Straight Line method in action from the man who invented it

Nearly 8 million views for a reason. Save it while you can ⭣

[Jim Larrison @jlarrison]

@WhiskeyJimbo @SagedUpRadio I always thought it was Scumbag Wormy not Scumbag Worm Meat 😆

[Jim Larrison @jlarrison]

There is a layer of enterprise AI measurement that most organizations haven’t yet built, and it may be the most important one.

Tool-level visibility tells you what is being used.

Spend visibility tells you what it costs.

Neither tells you how AI is changing the way work actually moves through an organization.

Workflow visibility is the missing piece.

Which processes have been restructured around AI. Which ones are running the same way they did two years ago, but with an AI tool bolted on.

Where are handoffs getting faster. Where are they getting stuck.

Without this layer, you can report adoption, and you can report spend, but you cannot answer the question that matters most:

Is AI changing how we operate, or is it just adding new costs to the old status quo?

[Jim Larrison @jlarrison]

There is a question that boards are increasingly asking, and most orgs cannot answer it with confidence:

What is our AI investment producing?

The challenge is that answering this question requires connecting three layers of data that typically live in separate systems.

Spend data lives in finance. Adoption data lives in IT. Outcome data lives in business units.

Bringing them together into a coherent view requires infrastructure that most organizations haven’t built.

The result is that executives walk into board meetings with partial answers. They can report how much was spent. They can report how many people have access.

They struggle to report what those people produced with that access, and whether the output justified the cost.

This is becoming more visible as AI spend grows. When the AI line item on the budget was small, partial answers were acceptable. As it becomes material, boards expect the same rigor applied to AI investment that they expect for any other significant capital allocation.

[Jim Larrison @jlarrison]

Median AI proficiency across enterprise deployments, based on measured output rather than self-assessment, sits at approximately 58.5 out of 100. Just above the midpoint at 50.

Organizations are investing in tools, running training programs, and reporting strong adoption, while the typical employee is still operating at a basic level of capability.

The gap between adoption and proficiency is where value disappears.

A team that has access to AI, but cannot apply it effectively, is not just less productive. Under consumption-based pricing, that team is also more expensive.

Every misfired prompt, every vague instruction, every additional, iterative exchange to get a usable output costs money.

Self-reported proficiency surveys cannot surface this problem, because people genuinely struggle to assess their own capability.

The only way to see it is to measure actual output quality and value.

[Jim Larrison @jlarrison]

read more here: https://t.co/9M5tNVLe9W

[Jim Larrison @jlarrison]

Gartner recently made an observation about AI coding costs that deserves attention from engineering and finance.

Their assessment: token discipline will not emerge through developer choice alone.

Developers optimize for speed, quality, and convenience, which is appropriate for their role.

Cost efficiency is not their primary objective, nor should it be.

Under consumption-based pricing, which is now becoming the norm, every interaction consumes tokens. So usage patterns that made sense under flat-rate pricing become expensive at scale.

Ungoverned autonomy in agent-driven workflows, bloated context windows, and the absence of structured feedback mechanisms to optimize usage: these are the common failure modes.

The implication is that cost management cannot be delegated to individual developers.

It requires a governed operating model with visibility into consumption patterns, escalation policies, and mechanisms for surfacing inefficiency before it compounds.

[Jim Larrison @jlarrison]

There is a question that boards are increasingly asking, and most orgs cannot answer it with confidence -

What is our AI investment producing?

The challenge is that answering this question requires connecting three layers of data that typically live in separate systems.

Spend data lives in finance. Adoption data lives in IT. Outcome data lives in business units.

Bringing them together into a coherent view requires infrastructure that most organizations haven’t built.

The result is that executives walk into board meetings with partial answers. They can report how much was spent. They can report how many people have access.

They struggle to report what those people produced with that access, and whether the output justified the cost.

This is becoming more visible as AI spend grows. When the AI line item on the budget was small, partial answers were acceptable. As it becomes material, boards expect the same rigor applied to AI investment that they expect for any other significant capital allocation.

[Jim Larrison @jlarrison]

Process discovery is the ability to map how work moves through an organization and identify where AI is changing that flow.

The value is the ability to find automation candidates that would otherwise remain invisible.

Most workflow improvements happen because someone on a team notices an opportunity and raises it. That approach depends on individual initiative and surface-level observation.

It misses the patterns that only become visible when you can see work moving across functions and systems.

Organizations that implement automated process discovery infrastructure will find opportunities their competitors cannot see.

They will be able to answer questions like: Where are the highest-value automation candidates? Which workflows have been restructured around AI? Which are running unchanged?

Also: Where are the bottlenecks that AI could address, but has not?

[Jim Larrison @jlarrison]

Brandon Veiseh, CEO of MindFort, said his six-person company would have needed twenty employees pre-AI.

The difference went to tokens.

His framing: "We have to weigh our token-to-people ratio."

This is a new workforce metric: the ratio of token spend to human labor required to produce a given output.

The implications extend beyond startups.

Every company is now implicitly making this tradeoff, whether they have named it or not.

Are they making it deliberately or by accident?