Daniel Evans

CEO at @OptikkaCorp. Helping businesses close the gap between creative capacity and operational needs. Design as code.

[Daniel Evans @danielpevans]

@pmarca Trend

[Optikka @OptikkaCorp]

One of the more interesting conversations happening in enterprise creative right now has nothing to do with AI. It's about files.
For the last twenty years, we've built creative operations around the assumption that every new campaign, athlete, sponsor, language, platform, or destination requires another file. As organizations scale, those files compound into massive DAMs, complex folder structures, endless versioning, and workflows built around managing assets instead of activating them.
We're seeing this firsthand in collegiate athletics. Schools already have the data needed to create personalized content for every athlete, every milestone, every recruiting campaign, and every sponsor activation. What's holding them back isn't creativity. It's the operational burden of maintaining thousands of individual files. As design becomes code, that equation changes. Instead of managing thousands of assets, organizations can manage a design system connected to data. The design remains intact, while the content flexes dynamically. That's a fundamentally different model for creative production, and one we believe will define the next generation of enterprise design infrastructure.
This is our CEO, @danielpevans, talking about that shift.

[Daniel Evans @danielpevans]

The change management and knowing when to implement agents and when to simply use the LLMs to accelerate development of coded services is key.

[Aaron Levie @levie]

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again.

A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work.

As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done.

You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more.

Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps.

Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure.

Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation.

Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation.

And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player.

Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

[Daniel Evans @danielpevans]

How many NFL teams are assembled with the very best players in the world; known rules, plays, strategy, millions of people cheering them on; and still finish winning less than half their games? No playoffs. No championships. Huge let down.

Does the owner shut down the team in embarassment and leave the business? Every business is a team. Either it comes together around the right strategy, stays healthy, gets a bit of luck at ideal times and wins. Or you retool and rebuild.

True founders will always be back for the next season. They have no idea how to exist outside that journey.

[sourcery @sourceryy]

.@mlevchin spent "zero minutes" introspecting on his failed companies:

"I kept going because I realized I liked the journey as much, if not more than the destination."

"The day my co-founders and I declared our first company dead, I found myself thinking, 'What will be the next one?'"

"I took exactly zero hours or minutes contemplating, 'Is this the right thing for me to do?'"

[Daniel Evans @danielpevans]

Optikka serves this role on the creative side. No control for token usage, agents get lost and spin, and determinism is a non-starter outside of ideation and amateur (meaning fun or non-commercial) projects.

Add the tools only where they deliver unique value.

[Chamath Palihapitiya @chamath]

Microsoft pulling Claude is the first, but not the last. The issue isn't that the tool isn't useful.

The issue is that without context and oversight, the tool can spin forever and generates an enormous cost burden that, when cascaded across an entire employee population, makes using the tool economically untenable.

8090's Software Factory is the control plane that is becoming increasingly used by Enterprises to get the job done but do it in a smart and scaleable way.

[Daniel Evans @danielpevans]

Ready for the show then the clouds rolled in. X live it is.

[Daniel Evans @danielpevans]

@pmarca classic

[Daniel Evans @danielpevans]

@kevinolearytv Slacks? I've enjoyed doing hits in pajama pants and blaming Mr Wonderful. Let's not make this a habit, sir.

[Daniel Evans @danielpevans]

@OliMarmol Love!

[Daniel Evans @danielpevans]

One of the genuinely good dudes in the show. Happy to have crossed paths with him in the business world and get to cheer this on as a friend.

[Front Office Sports @FOS]

Cardinals manager Oli Marmol said he would "do whatever I need to do" to get the shirtless fans that cheered them to an 11th inning walk-off back to more games Friday.

Now, he's bought all the tickets for the "Tarps Off" section for today's and tomorrow's game.

(🎥 via @KMOV)

[Daniel Evans @danielpevans]

Looking forward to catching up with everyone next week in New York. We took a table this year to let you see our creative infrastructure operate live. Spreadsheet to thousands creative outputs in minutes.

[Optikka @OptikkaCorp]

Headed to New York City for @SBJ Tech Week next week!

@danielpevans and Mike Kelley will be talking with teams across sports, media, and broadcast about real-time creative workflows, deterministic systems, and what scalable production infrastructure actually looks like.

If you’re there, let’s connect!

[Jason Carman @jasonjoyride]

It's been 2+ years since S3 last featured @AstroMechanica — they've been quietly building a new vision of supersonic passenger travel.

By building electric adaptive engines (efficient at every stage of flight) to reimagining airline economics itself, @ianbrooke and team's next step... is taking flight.

[Daniel Evans @danielpevans]

@pmarca Why the monks retreated to the monasteries.

[Daniel Evans @danielpevans]

Orchestration is generally assembling disparate things into a unified process to deliver an outcome. The underlying should be swappable or modular but how often are processes that deliver their expected outcome updated?

Question to illustrate: When we have personal robots doing housework, does the robot fill the dishwasher or clean dishes by hand?

[Daniel Evans @danielpevans]

@Karanzz13 @Austen People are hesitant to turn it off, but at some point that dam cracks.

[Daniel Evans @danielpevans]

@yoheinakajima 100% this. I don't know it's a third party as much as built into the pipes. We rolled our own.

[Daniel Evans @danielpevans]

@landon20s

[Daniel Evans @danielpevans]

@bhalligan We wrapped every process in an order system that automatically calculates process time and compute cost. AI then reviews for variance. Anything outside of % tolerance, pulls the code and analyzes for ways to optimize/improve, deploys to sandbox, alerts engineering for review.

[Daniel Evans @danielpevans]

@levie See the same when you ask about politicians. ~9% have confidence in Congress, but local reps run high 40s in favorability.

[Daniel Evans @danielpevans]

@PalmerLuckey Not sure what these are but I see an underwater drone, a giant light saber, a storm trooper landing pod, and a death star transport.