Mark Mandau

Mark Mandau

@markmandau.bsky.social

appraise.mandau.co mandau.co

5 followers4 following83 posts

What's in a name? Can it build better products, serve customers, and reduce costs without compounding risk? Call it what you will. Just make sure the intelligence translates into business value.

For small-business software development, top-tier closed models are no longer required for productive AI engineering. Free cloud models are close enough for much work, while local models are usable in a pinch. lnkd.in/p/gUMqMhTQ for details

New technologies have a history of seeming “gee whiz” while producing underwhelming results & unintended consequences. The executive challenge remains not simply adopting AI tech, but giving it enough context, direction, and guardrails to produce positive outcomes, not fallout.

Market concentration may be an operating story before it becomes an investing one. When aging software consumes the capital meant for tomorrow, product velocity falls. Then a technology problem becomes a capital-allocation problem, and eventually a valuation problem.

AI can process millions of token-layer passes in one interaction. But until that output changes a decision, workflow, cost, or customer outcome, it is still just output. Where has AI produced a measurable real-world result for you?

AI doesn’t just amplify weakness. It can compensate for weaknesses we understand. The bigger risk is unknown unknowns: hidden assumptions, incentives, and product logic that AI can scale before anyone realizes they were wrong.

Protected markets can keep bad product models alive for years. Their software stacks accumulate industry dysfunction while customers pay. Eventually, a better value-to-revenue model takes the whole market.

I find there’s a rhythm to AI context. Some docs stay evergreen. Others go stale as decisions, customers, and systems change. As we build, we patch context gaps before AI starts working from yesterday’s version of the business.

AI works better when it understands the business. Mandau Coexecutives builds AI Context Infrastructure so models connect mission, users, backlog, decisions, and work style to better output, less rework, lower waste, and repeatable execution.

Humans are more than capital. But as an investment, they multiply what is put into them. Workforce chemistry doesn't just happen. Relationships take effort. Sometimes lessons are hard-won. Yet it's never too late to learn how to have better relationships, home and at work.

CEOs approve rewrites because they are pitched as solving platform problems. What they get instead is hard-won product context erased and teams split apart. What they really want is product velocity to grow their business competitiveness, margin, revenue, and market share.

AI coding has a rhythm. Keep enough context alive to make the next ask clear. Tell AI "why". Not every run hits gold. But it doesn't have to. Momentum is not a bug, it's the feature.

What if Jira and Figma merged ... ...to be an Claude Design-like epic prototype builder/tracker which calls out dependencies in a new UI/UX? This could be highly valuable for both product discovery and delivery.

Big Tech PM interviews can miss a core product principle. The problem isn’t frameworks. It’s when frameworks get so heavy they resemble waterfall. Too many steps without audience check-ins increases product-market drift. AI isn’t the only intelligence that hallucinates.

Most software rewrite conversations start before anyone calls them a rewrite. Business: Tired of waiting. Engineering: Platform can’t support growth. Starting fresh may be easier. For CEOs, this can become a lonely decision fast. Mandau Coexecutives exists for that moment.

Is Jira still the right tool when AI agents can help move from idea to prototype faster than a ticket can be groomed? Tickets made sense when coordination was the bottleneck. Now the bottleneck is shifting to vision, product sense, and judgment. Does this apply only to discovery, or delivery too?

"Will companies slow hiring or accelerate it as gen-AI scales? Near term pattern: • AI coding agents → PMs ship real MVPs, engineers focus higher-order • Repetitive work automated • QA automation becomes real • Continuous delivery finally prioritized

Longer term: If unit costs drop → hiring grows More likely near term → disruption + rising AI “rents” Bets and arbs continue. Insight: Velocity rises before org models catch up."

Frustration Friday. For the unsung heroes and teams iterating through ambiguity, rework, and shifting priorities. We see you. Now let’s go ship one thing that matters.

Too often I’ve seen CEOs preside over major rewrites which slid from hope to frustration and alarm. Whole teams got laid off as a result. Many of you reading this have lived it. Perhaps more than once. You doubled down, while others bailed.

I’ve seen startups and middle market companies alike freeze a working product to chase a rewrite. Velocity drops. Parity drags. Burn spikes. The team is capable. But it's running two systems and shipping neither. Most rewrites don’t fail suddenly. They drain momentum over time. #shipgoodproduct

Strong product leadership prevents software rewrite pressure. The key signals of healthy modernization: 1) clear priorities, 2) aligned teams, and 3) continuous customer value. These disciplines protect organizations from the temptation of risky resets. #mission #priorities

Software rewrites succeed when leadership understands the storm they’re entering. Velocity loss is often dismissed as temporary. It isn’t. If you can’t keep improving customer value during the rewrite, growth stalls. Some companies weather it. Others fragment and sink. #rewritesareexistential

When orgs stop evolving their software products incrementally, their Rewrite Gravity Index™ grows. To find out your risk, do a quick appraisal at: appraise.mandau.co It's like a 20-point safety check for your vehicle, but for something much more precious. Your business.

The danger isn’t the rewrite itself. It’s how many risks go live at the same time. Architecture shifts. Data migrations get challenged. Customer transitions stall. Navigating the existential danger means knowing how to reduce unnecessary, compounding risk. #unnecessaryrisk

The danger isn’t the rewrite itself. It’s how many risks go live at the same time. Architecture shifts. Data migrations get challenged. Customer transitions stall. Navigating the existential danger means knowing how to reduce unnecessary, compounding risk. #unnecessaryrisk

LLMs feel intelligent, but they still struggle to stay on task. They reconstruct knowledge, not retrieve it. Strong AI products are built by focusing context, whether by improving models or improving their usage in the product loop. #attention #productsense

Incremental progress compounds gains while dissipating risk. Shipping small improvements regularly creates two advantages: 1) customer trust grows, and 2) risk stays contained. Organizations that preserve momentum rarely need sweeping rewrites. #customerfirst #peakproductdiscipline

The most expensive rewrite freezes progress. When teams stop shipping while waiting for a big-bang release, they lose the compounding value of continuous delivery. Standish Group found a 20-fold increase in success rates when teams ship in small increments. Momentum matters. #continuousdelivery

Sometimes a software rewrite becomes the only remaining option. If that existential moment arrives, success hinges on one thing: continue to give your customers outstanding product value. The goal is not to start over. The goal is to preserve momentum during the change. #nocodefreeze

The strongest software organizations continue to evolve. “We change our [search] algorithm almost every day.” – Amit Singhal, Google Search (2011) Small improvements compound. Disciplined continuous evolution prevents product debt & technical debt from reaching existential levels. #modernization