IIIMPACT Design

08/13/2026

We developed a UX / AI agent algorithm system so powerful it's too dangerous to share publicly. Let me know if you want in.

(Hey don't hate the playa...works for anthropic/openAI...maybe might work for us).

06/25/2026

We're compiling code faster than trust.

AI amplifies software engineering experience. Seniors gain the most; juniors use more tokens with less results.

AI agents are the new jr. engineers/designers but they need guard rails.

Cap your daily agent output to what you can actually review and verify

Tech/Design debt is cheap to take care of with AI - so use it to clean up your product.

Right now it's too easy to let AI write the code or design the app while you skip the learning. The bug or problem gets fixed but your mental model doesn't move

Spend more time thinking and understanding as we are trading future capability for present-day speed, and these tools won't make us do otherwise.

From he age old saying - if you have 60 min to solve a problem, you should spend 50 min understanding the problem.

06/24/2026

Slop buries the people who care.

- more, longer, pull requests
- most devs have code remove fatigue, so just say 'Looks good to me!' and don't remove it.
- just a small number of devs push back on AI slop
- devs who care are being overwhelmed
- some of these devs are getting fed up, burning out

CEO of Opencode - "We're shipping way more hacks (code quality is low)... where we should have just rethought the whole system from the ground up, or redesigned it to make it more flexible...so I think our judgement is just off."

"It sure feels like software has become a brittle mess, with 98% uptime becoming the norm instead of the exception...and user interfaces have the wierdest bugs that you'd think a QA team would catch. I give you that that's been the case for longer than agents exist. But we seem to be accelerating."

Don't know how many enterprise software companies I've spoken with over the past 2 decades that were just hacking crap code with people...now they are doing it with AI.

Doing this without the expertise to know how to fix or find anything in the code because they've laid off or their best talent has left, is going to sink their software ship faster.

06/24/2026

An AWS exec said something recently that stuck with me:

"Organizations that are moving faster and maturing do something different. They begin with a business outcome and work backwards, building an agentic system that reduces decision handoffs, increases learning, and removes friction, all while maintaining quality. AI maturity comes from applying AI to the system*, not to the individual."

Meta was awarding devs that used as many tokens as possible, which they just recently removed this poor incentive structure.

Here's what we're seeing on the ground:

Organizations are generating more AI slop, more bugs, and more tech and design debt, faster than ever. Their QA teams can't keep up with the output, so now "the devs do QA." They've turned people loose with AI in the name of speed, with no governance underneath it.

That's the exact opposite of working backwards from an outcome. It's a recipe for disaster.

Our team has been working relentlessly with organizations to do the counterintuitive thing: slow down to speed up. Break down internal processes. Build the AI harnesses and governance that keep UX and product strategy from becoming an afterthought, or a one-shot prompt in your AI tool of choice.

"But I can one-shot a design that's close enough."

So can your competitors.

The edge isn't speed. It's discipline. It's building data-driven agents with feedback loops, pulling from customer service data, user behavior, analytics, that give your prompting-happy PMs a more rigorous path to a better product.

Start with the outcome. Build the system. Everything else is just slop at scale.

06/10/2026

New Anthropic policy: "To ensure we're responsibly deploying Mythos-class models, we are requiring limited data retention and review as part of our safety work. Prompts submitted to, and outputs generated by, Mythos-class models are retained for 30 days for trust and safety purposes, on every platform where these models are offered. "

If you use fable/mythos - they collect your data ...no exceptions even for enterprise partners.

Even if your organization previously negotiated zero-data retention agreements, you are now subject to this new 30-day window.

Advanced power now comes with a new set of data governance trade-offs.

Anthropic is like an abusive parent who buys you the best toys so you just deal with it.

Claude is Anthropic's AI, built for problem solvers. Tackle complex challenges, analyze data, write code, and think through your hardest work.

05/30/2026

Stop looking at cost per API call. Measure the Cost Per Successful Outcome.

Moving faster in the wrong direction, is far more expensive than moving slowing in the right direction.

05/30/2026

When Compute Costs More Than Headcount

We are officially seeing AI compute expenses actively outweigh the personnel costs they were supposed to replace.

The biggest mistake happening right now? Treating AI like a static SaaS license. AI is a variable expense. Poor usage governance recently cost one enterprise $500M in a single month because they didn't cap employee access...but hey you all have to move faster and use "more AI".

The token costs for running AI agents are now exceeding what they were paying the employees they fired.

When the tokens run out, the AI stops. Just stops. No continuity. No workaround. Just a spinning wheel where your workforce used to be.

You fired humans to save money and bought a subscription that bills you into a corner.

The employees you let go knew what to do when things broke.

05/29/2026

Large language models are backward-looking by design. They train entirely on historical data. They do not invent. They synthesize what already exists.

When you rely on an AI model for product strategy or creative direction, you are generating derivative work. You are producing a clone.

Clones do not command a premium.

Every company right now is making a fundamental choice about their market position.

You can use AI to optimize your ability to copy. It is faster and cheaper than ever to replicate what your competitors are doing. If you choose this route, you will permanently play catch-up. You will trade market leadership for operational efficiency.

Or you can prioritize forward-looking human creativity. You can build the concepts that simply do not exist in the training data yet. By the time the rest of the market trains their models on your work, you will already be on the next iteration.

Market Leader > Market copier

05/29/2026

Software engineers are slowly losing the ability to navigate their own codebases.

When developers write code natively, they build a mental model of the architecture. They know where things are, how systems interact, and why certain fragile decisions were made years ago.

When developers transition to simply prompting an AI to generate code, that mental model degrades.

They are outsourcing the implementation, which means they are outsourcing their understanding of the system.The problem surfaces when something breaks. A developer who hasn't written the code no longer knows how to fix it natively.

They are forced to continue prompting their way out of the problem, guessing at solutions because they no longer possess the deep, structural knowledge of how everything is wired together.

We are trading deep expertise for temporary speed.

05/28/2026

The tech industry is walking blindly into a massive pricing vulnerability with AI coding tools.

Developers are rapidly transitioning from writing code to prompting it. As a result, engineering teams are losing their native familiarity with their own software architecture. They are becoming entirely dependent on external AI models to generate, debug, and maintain their codebases.

Uber blew through a 12-month AI budget in exactly four months.

Microsoft watched API costs climb and forced its own engineers to abandon Anthropic's Claude Code, despite those engineers preferring it over Copilot.

If AI providers execute a rug pull and raise token prices, engineering teams will be fuct. They cannot simply revert to writing and maintaining the code manually.

The developers will have already lost the deep institutional knowledge required to navigate their own systems natively.

When your team relies entirely on an external AI model to maintain your product, you are no longer paying for a software tool. You are paying rent on your own codebase.

The AI landlords WILL raise token prices because they need to appease shareholders when they go public.

When this happens, and you've let go a significant portion of your team that had institutional knowledge, you competitors that retained their experienced teams with proper AI governance... they will be able to surpass your overworked, short-staffed design/dev teams with ADHD trying to manage 5 agents to just keep up.