Serge Doubinski

Building ai workflows and agent orchestration.
Prev. Product at @Rakuten @Upwork

[Serge Doubinski @sergedoub]

@andrewmccalip People did this with banner ads in mid 90s.
Some things never change.

[Serge Doubinski @sergedoub]

@john_ssuh It’s that decision traces conversation again.
You’re very correct on difficulty of converting existing orgs to this. Too many retrofits.

https://t.co/z0pIeAOitZ

[Jaya Gupta @JayaGup10]

https://t.co/uPXcTUEsnc

[Serge Doubinski @sergedoub]

@marty_kausas What do you think about model routing and some oversight on using cheap ones for simpler tasks?

[Serge Doubinski @sergedoub]

@Steve_Yegge Nice strategy, though hard to pitch in an org. Planning for next model without “visible” output. Many teams wouldn’t consider 0 code sprints as progress. Too uncomfortable.

[Serge Doubinski @sergedoub]

@bcardarella @_asadmemon tbh he’s just ahead. what’s normal to him will sound unreasonable, questionable or threatening to others.

[Peter Steinberger 🦞 @steipete]

Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore.

You should be designing loops that prompt your agents.

[Serge Doubinski @sergedoub]

Internal enablement. Takes time to stand up. Needs champions. Measurement. It’s not an On switch.

[Hiten Shah @hnshah]

I’m increasingly convinced this is the missing layer in AI adoption.

Access is not enough. Organizations are giving people tools, connectors, and blank chat boxes, then wondering why ROI feels uneven.

The advantage comes from teaching AI how the company already does its best work. That starts with capturing the patterns of your best people and turning them into reusable skills.

I’m aligning every business and product I’m working on around this approach.

The article is about skill libraries. But read it through a different lens.

It's really a story about why some organizations will get massive ROI from AI while others won't.

The advantage comes from turning your organization’s unique expertise and proven ways of working into repeatable systems that everyone can use.

[Serge Doubinski @sergedoub]

@mattyp thanks for sharing. is there'a path to automated updates? having a bit of fomo that for 3rd party skills they might be getting improved while stale on local.

[Serge Doubinski @sergedoub]

@shl @karloscodes haha honest and accurate

[Serge Doubinski @sergedoub]

@lumendriada That’s awesome news. Require resume sounded like a possible path, glad to see it worked!

[Serge Doubinski @sergedoub]

Very good observations. Most of the appeal in AI solution buying is about capabilities, but few have done the end to end implementation to understand change management.

Even frontier labs don’t live the cost reduction utopia. Teams forecasting efficiency for next few quarters in “regular” IT orgs are fooling themselves.

[Maurizio @themgmtconsult]

Been a management consultant for 20 years.

Made Partner in my 30s.
Led teams of 100+ people.
Run 9-figure client portfolios.
Lived and worked in 4 continents.

Typically, corporate IT investment would follow a common script.

Capital spent on software means a shrinking payroll.

As boards map out their strategies for the coming quarters, they are operating under the comfortable assumption that this way of thinking still holds true for AI.

But I think a fiscal reckoning is brewing there, because within the next few quarters, the current prevailing narrative of AI as a headcount killer (which we all know is vastly exaggerated) will give way to a far more punishing reality.

Instead of a clean capital-for-labor swap, executives are about to watch their IT infrastructure costs and their personnel expenses balloon simultaneously 🚀🚀🚀

It may not be fun.

First, this whole idea that generative AI can operate autonomously will shatter as early deployments attempt to scale.

Because LLMs remain inherently prone to hallucination and error, companies cannot simply fire the analysts; they will be forced to retain them (or hire new talent) to serve as high-vigilance editors.

Furthermore, because AI makes it effortless to generate code, reports, marketing collateral, etc etc organizations will soon find themselves drowning in internal output. Managing, auditing, and securing this massive influx of AI-generated material will require an unprecedented wave of human oversight....

This will ultimately EXPAND corporate bureaucracy rather than trimming it (remember the 'Scaled Agile' saga??).

Even in scenarios where entry-level automation does succeed, the math of headcount reduction will fail to balance out on the ledger.

In the coming quarters, the wage differential of the AI era will trigger *severe* skill inflation.

Replacing 5 mid/entry-level programmers does not result in a net savings of 5 salaries. Instead, it requires hiring a premium-tier AI architect whose single salary frequently eclipses the combined wages of the workers they replaced (plus tokens cost).

Companies will trade high-volume/low-cost labor for scarce/ultra-premium talent, driving TCO UPWARD despite a leaner organizational chart on paper.

Jevons' Paradox again...

AI slashes the time and cost required to draft a legal brief, design a graphic, build a software feature, and therefore executive appetite for those outputs will skyrocket.

Management will demand 10x the volume of data analysis or continuous product iterations. Because the corporate demand for output will scale far faster than the technology's efficiency gains, departments will find themselves forced to expand their human teams just to handle the sheer velocity of these new AI-driven initiatives.

Until AI achieves absolute, unmonitored autonomy (if ever), it will function not as a replacement for human labor, but as a hyper-amplifier of it.

If ungoverned, the corporate balance sheets will show that the AI boom made running the business vastly more expensive.

[Serge Doubinski @sergedoub]

@lennysan Next is 0 bananas for 2 months 🤷‍♂️

[Serge Doubinski @sergedoub]

Claude aura is bit weakened by phrases that come off as skittish or over-compensating. @trq212 has this feedback come up?

I had claude review last few dozen sessions with common use of these phrases. The model really tries to convince me it did good work.

[Serge Doubinski @sergedoub]

@seraleev Good to hear stories like this. Keep going!
Thanks for sharing along the way.

[Serge Doubinski @sergedoub]

@trq212 I have an Agent look through everything you and team post every day and try to synthesize 😅

Also stored as wiki and applied contextually during projects to bring in the new practices.

[Serge Doubinski @sergedoub]

@mgonto You’re thinking since c suite does own vendor eval and don’t rely on enterprise sales as much?

[Serge Doubinski @sergedoub]

@SIGKITTEN @OpenAI @ajambrosino Awesome. Congrats.

[Serge Doubinski @sergedoub]

@xsteenbrugge @claudeai @bcherny Saw this happen with a session where I asked for less guessing and more observing. Then Claude got obsessed with observing.

[Serge Doubinski @sergedoub]

@mfishbein So few people can easily describe the steps for what they do. Process mining will be in much demand.
Have clients been very open to voice agent discovery step?

[Serge Doubinski @sergedoub]

@tylerangert Different take is to consider it as a replacement for the nav bar. An entry point that doesn’t have to end up as bare bones chat thread.