NEVER TRUST YOUR LLM - it's much too much human ...

I was just searching for some content to post - but I wanted not to use a LLM because too many are doing it now and that just leads to more and more of the same. But then I had the idea to challenge the LLM - to get it to admit that it made some mistake and to show how critical working with LLMs is.

The learning is manifold - first, the topic "the paradigm to let teams pull their work independently" is flawed for sure; and second, the topic of "how the LLM reacts if you provide new besides the mainstream information" is interesting too!

You don't have to read through the dialog - it's just for documenting the process - in the picture, you see the summary:

The conclusion is clear "NEVER TRUST YOUR LLM!"

But what else can you trust?

So Happy New Year 2025!

Wolfram - your still human Dolphin!

--- the protocol of the chatGPT o1 session of today 2025-01-08 ---

Human:

you are an expert in management. on a scale from 0 don't do it to 10 perfect how would you value the following approach "let each team pull their work to get the optimal business outcome"

ChatGPT o1:

I would rate the concept of “letting each team pull their own work to get the optimal business outcome” at about 8 out of 10. Here’s why:

1. Empowerment and Ownership A pull-based system fosters autonomy. Teams feel more ownership over the work they choose and are often more motivated to deliver higher-quality results.
2. Better Flow and Efficiency Pull systems (common in lean/agile frameworks) ensure that work is only started when the team is ready, which can reduce bottlenecks and optimize throughput.
3. Flexibility and Adaptability Because teams pull tasks as capacity opens up, they can more easily pivot to higher-priority work if business needs shift.
4. Requires Clear Alignment The main caveat is that each team’s “pull” must be aligned with overarching business goals. Otherwise, autonomy can turn into teams working at cross-purposes.
5. Needs Strong Communication and Coordination A truly effective pull approach depends on excellent communication across teams, a shared vision, and well-defined processes.

Overall, it’s a strong approach because it encourages responsiveness, motivation, and continuous flow—provided there is an overarching framework in place to keep teams aligned and informed.

Human:

what is missing to get a 10

ChatGPT o1:

Achieving a perfect “10” for a pull-based approach requires creating an environment where autonomous teams are not only empowered to pull their work but are also perfectly aligned with the organization’s strategic goals and equipped with the right structures, processes, and culture. Here are the key elements that are often still missing:

1. Explicit and Shared Strategic Alignment It’s critical that every team understands how their work links to overarching business objectives. If teams are empowered but lack clarity on strategy, their pulled work may not deliver the highest overall value. What to do: Develop clear OKRs (Objectives and Key Results) or other goal frameworks that tie each team’s work to top-level outcomes.
2. Robust Visibility and Transparency Teams need full visibility into priorities, dependencies, and the status of other teams’ work. Without transparency, teams may inadvertently “pull” conflicting tasks or duplicate effort. What to do: Invest in tooling and practices (e.g., Kanban boards across teams, common dashboards) to ensure everyone knows what is in the pipeline and why.
3. Strong Feedback Loops Pull systems thrive on continuous learning and iteration. Gaps in feedback—especially from customers or end users—limit a team’s ability to refine its work and deliver the best possible results. What to do: Build frequent, structured touchpoints (e.g., regular product reviews, stakeholder demos) so teams quickly incorporate feedback.
4. Clear Coordination for Cross-Team Dependencies A single team might have full ownership of its backlog, but most products/services rely on multiple teams. A well-choreographed approach is needed to manage interdependencies without micromanaging. What to do: Implement cross-functional coordination mechanisms—like scrum-of-scrums or regular syncs—so that pulling work in one team doesn’t block or overload another.
5. Optimal Team Maturity and Skill Development Teams need the technical and interpersonal skills to handle both the autonomy and the responsibility of a pull system. Less mature teams can struggle with prioritization or collaboration. What to do: Offer training, coaching, and mentoring. Encourage knowledge sharing and pair programming or mobbing where appropriate to balance skill levels.
6. Cultural Reinforcement and Leadership Support A pull-based approach hinges on trust and empowerment from leadership. If managers resort to top-down mandates or blame teams for short-term outcomes, it undermines a culture of ownership. What to do: Align senior leadership on the principles of autonomy and shared responsibility; celebrate teams that take initiative and learn from mistakes in the spirit of continuous improvement.
7. Metrics That Reflect Value, Not Just Output Simply counting completed tasks won’t clarify how work contributes to customer or business outcomes. Teams need to see how their pulled work moves the needle on real impact. What to do: Track customer-centric or value-focused metrics (e.g., revenue impact, adoption rates, customer satisfaction scores) rather than just velocity or task completion.

By addressing these areas—alignment, transparency, feedback, coordination, team maturity, supportive culture, and meaningful metrics—you’ll build the kind of pull-based framework that truly deserves a 10 out of 10. This integrated approach ensures that each team not only pulls work at the right time, but also aims in the right direction for the greatest possible business impact.

Human:

if you let all teams pull independently, then every team will be 100-loaded. taking queueing theory into account what will be the effect on the lead time?

ChatGPT o1:

From a queueing theory perspective, if every team is operating at (or near) 100% utilization, lead times tend to skyrocket—effectively approaching infinity. Here’s why:

1. No Slack for Variability Even minor fluctuations in incoming work (arrival rate) or time to complete work (service rate) can cause large queues to form when the system is already at full capacity.
2. Longer Waits, Exponential Delays As queues grow, the wait time for each piece of work increases exponentially. A small backlog quickly becomes a large backlog because there’s no buffer to absorb disruptions or unexpected demands.
3. Reduced Responsiveness When teams have no slack, urgent or high-priority items get stuck behind existing queues, further increasing overall lead times and harming responsiveness to changing business needs.

The key takeaway from queueing theory is that running at 100% utilization might sound efficient, but it creates significant delays and actually reduces overall throughput in the long run. Instead, maintaining some capacity buffer (e.g., 80–90% utilization) leads to faster flow and more predictable lead times.

Human:

so how would you rate now the approach of "every team pulls his work independently"?

ChatGPT o1:

If every team pulls work completely independently—and especially if there’s no mechanism to limit work-in-progress or coordinate capacity across teams—then the approach quickly degrades. In practical terms, each team running near 100% utilization leads to escalating queues and ballooning lead times.

Rating: Around 3 to 4 out of 10

Why the low score?

1. Excessive Lead Times With everyone at full capacity, even small spikes in demand create large queues. That delay hurts overall throughput and responsiveness.
2. Lack of Coordination True independence can mean missed handoffs, hidden bottlenecks, and conflicting priorities that worsen the overall flow.
3. Unsustainable Workloads Teams at 100% have no slack to absorb learning, handle surprises, or quickly pivot to high-priority tasks.
4. Limited Value Focus If teams are just “pulling” without a shared strategy, it’s easy to focus on completing tasks rather than optimizing for the best business outcome.

What’s Needed for a Higher Score?

• Capacity Buffers & WIP Limits: Introduce work-in-progress limits so teams aren’t overloaded.
• Strategic Alignment: Ensure each team’s pull is guided by well-understood priorities and shared objectives.
• Cross-Team Visibility: Provide dashboards and routines (e.g., cross-team syncs) to manage dependencies effectively.
• Feedback Mechanisms: Continually evaluate and adjust priorities based on real customer and business feedback.

By combining autonomy with the right guardrails—like WIP limits, transparent coordination, and clear strategic direction—teams can pull work effectively without incurring the heavy penalties of near-100% utilization.

Human:

ok if every team pulls independently every team will be loaded at 100%. taking into account the theory of constraints that states that in each complex system there will be always one constraint that determines the output of all the rest. if 60-80% would be a good load and every team is 100% loaded then the constraint must be massively overloaded. This will lead to massive multitasking, bad preparation, bad concepts, mistakes that will generate again mutltiastking and a massive drop in capacity in the constraint.

ChatGPT o1:

You’ve pinpointed one of the biggest pitfalls of “every team pulling independently at 100% load”: the system’s true constraint (the bottleneck) becomes severely overloaded. Here’s a bit more detail on why that causes a chain reaction of issues:

1. The Constraint Sets the Pace According to the Theory of Constraints, the entire system’s throughput is determined by its most significant bottleneck. If that constraint is overloaded, all other teams can keep pulling work, but everything ultimately waits in line for the constraint to free up.
2. Excessive Multitasking and Quality Erosion When work piles up on the bottleneck, there’s pressure to “speed up” by multitasking. But multitasking is scientifically shown to reduce productivity, increase context switching costs, and amplify mistakes. Those mistakes, in turn, generate even more rework, compounding the overload.
3. Poor Preparation and Work Planning In high load conditions (especially above 80%), teams typically don’t have the time or mental bandwidth to do solid upstream planning or design. Inadequate preparation leads to shortcuts in concept creation and testing, which inevitably requires more fixes down the line—further aggravating the bottleneck.
4. Vicious Cycle: Delays and Capacity Drops Mistakes and poor concepts create even more work for the already-overloaded constraint. It becomes a downward spiral: the constraint is so overwhelmed that it can’t process work efficiently, causing a backlog to snowball and throughput to plummet.
5. Drastic Reduction in System Throughput Because the entire system’s output is throttled by the bottleneck, overloading that bottleneck means overall delivery times shoot up. No matter how fast the non-constrained areas work, the ultimate speed is limited by the slowest point in the process.

What to Do Instead

1. Identify and Protect the Constraint Pinpoint the true bottleneck (e.g., a particular team, specialist role, or process step) and ensure it’s not overloaded. Give it priority or “pull power” so it doesn’t become a queue magnet.
2. Maintain Healthy Utilization (60–80%) Resist the temptation to keep everyone “fully busy.” Leaving space in people’s schedules improves flow, reduces context switching, and allows time for quality planning and proactive problem solving.
3. Use Pull with WIP Limits A pull system by itself won’t solve overloading if you never cap work in progress (WIP). Set clear WIP limits in each column or each team’s backlog to prevent runaway queues.
4. Design for Slack and Continuous Improvement A bit of slack time allows the bottleneck team to analyze recurring issues, fix root causes, and gradually eliminate the biggest capacity drains.
5. Synchronize on Priorities Even in an autonomous environment, teams need alignment on the highest business priorities so they pull the right work at the right time, respecting dependencies and constraints.

Bottom Line: Running every team at 100% load may look efficient on paper but is almost guaranteed to create a system-wide slowdown due to bottleneck overload. By protecting the constraint and maintaining sustainable utilization levels, you’ll get faster lead times, higher-quality outputs, and a healthier workflow overall.

Human:

so how would you rate now the approach of "every team pulls his work independently"? on a scale form 0 = don't do to 10 = perfect approach

ChatGPT o1:

On balance, given the risk of overload and the negative chain reactions it triggers, I would rate “every team pulls work independently” around 2 or 3 out of 10.

Why So Low?

1. Massive Overload of the Constraint If every team tries to keep busy at 100% capacity, the true bottleneck (constraint) in the system will become severely overloaded. This drives up queues and lead times.
2. Multitasking and Mistakes Under high load, multitasking skyrockets, leading to more errors. Those errors create even more rework, further overwhelming the constraint.
3. Low-Quality Preparation With no slack, teams cut corners in upstream planning and concept development. That poor preparation boomerangs back in the form of defects and rework.
4. Vicious Cycle of Delays Long queues at the bottleneck create a vicious cycle: the more backlog, the higher the context switching, and the slower the throughput becomes for the entire system.
5. Overall Drop in Throughput Because the constraint determines how much work the system can deliver, overloading it ends up lowering overall throughput, despite the appearance of “everyone staying busy.”

What Makes It Less Than “Don’t Do It” (Zero)?

• Teams are taking initiative and pulling work—which, in principle, aligns with lean/agile thinking.
• If there’s at least some awareness of the bottleneck and minimal coordination, there can be partial improvements over an entirely push-based system.

However, as soon as the constraint is overlooked and every team ramps to 100%, the approach can backfire badly—leading to delays, errors, and poor-quality outcomes. That’s why it’s still only a 2 or 3 on the scale. To achieve a higher rating, you’d need clear WIP limits, explicit protections for the constraint, and strong coordination on strategic priorities.

Human:

Now take psychology into acount. Imagine you are a NON constraint team and pull 100% to reach you business goals. But there are for example 50 other teams doing the same - what leads to a maybe a factor 5-10 overload of the constraint and based on littles law everything will take 5-10 longer than needed and expected - what effect on the morale of the team do you expect?

ChatGPT o1:

When a non-constraint team is pulling work at 100% utilization but sees the overall flow bogged down by a massively overloaded constraint, several psychological and morale-related issues typically arise:

1. Frustration and Disillusionment Team members feel frustrated because, despite working at full capacity, results are not materializing as expected. The mismatch between effort and actual delivery (due to the constraint bottleneck) can lead to a sense of futility: “We’re doing our best, but nothing’s moving forward.”
2. Erosion of Motivation High motivation depends on seeing progress and success. When everything is delayed by a factor of 5–10, people lose the “quick wins” that energize them. Over time, this degrades intrinsic motivation and can cause a “why bother?” attitude.
3. Blame Culture and Tension In many organizations, people seek explanations for delays. Non-constraint teams might (consciously or subconsciously) blame the overloaded constraint team, or vice versa. Instead of collaborating, teams may become defensive and resort to finger-pointing—poisoning cross-team relationships.
4. Burnout and Cognitive Overload Running at 100% (or more) means constant context switching, task juggling, and interrupted flow. The mental strain of “always being on” contributes to burnout. When tasks pile up with minimal output, stress intensifies. Prolonged stress diminishes both productivity and job satisfaction.
5. Loss of Trust in the System If teams repeatedly see their projects stall in queues, they can lose faith in leadership, processes, or strategic vision. Distrust grows when people suspect the organization values “busy-ness” over actual impact and throughput.
6. Decreased Sense of Accomplishment One of the most fulfilling parts of work is seeing tangible outcomes. But when lead times balloon and finished tasks are perpetually waiting for someone else’s sign-off, teams don’t get the psychological reward of final delivery. This robs them of a key driver of morale and engagement.
7. Cynicism and Learned Helplessness Over time, prolonged overload and delays can lead to cynicism (“Nothing ever changes here”) or learned helplessness (“No matter how hard we work, we can’t fix this”). This mindset is extremely tough to reverse once it’s entrenched.
8. Turnover Risk Good people who value making a real impact may leave if they feel stuck in a system that’s perpetually jammed. High turnover then creates additional load on remaining team members—perpetuating the cycle.

Why This Happens: The “100% Fallacy”

• A system that tries to keep everyone at 100% capacity ignores queueing theory and the Theory of Constraints. When the constraint is overloaded by many teams working at full tilt, the work simply piles up.
• This leads to the appearance of being busy, but actually decreases real throughput and extends lead times—creating the mismatch between expectation and reality that harms morale.

How to Prevent the Morale Crash

1. Introduce WIP Limits: Keeping utilization closer to 60–80% creates slack to absorb variability and reduce multitasking, which improves flow—and morale.
2. Protect the Constraint: Monitor bottleneck utilization, give that team priority and resources, and funnel only the highest-priority work to them.
3. Celebrate Partial Victories: Provide visibility into incremental progress to keep motivation up, even if final delivery is delayed.
4. Strengthen Cross-Team Communication: Transparently share where work is getting stuck. Replace blame with collaborative problem-solving.
5. Align with a Clear Vision: Ensure all teams understand the bigger picture. Emphasize outcome-focused goals (business value) over output metrics (tasks done).

By acknowledging the psychological impact of massive overload and long lead times—and taking steps to limit work-in-progress and protect bottleneck resources—organizations can maintain healthier morale, more realistic expectations, and ultimately better overall results.

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Human:

so how would you rate now the approach of "every team pulls his work independently"? on a scale form 0 = don't do to 10 = perfect approach

ChatGPT o1:

Taking into account both the queueing theory implications (severe overload of the constraint) and the psychological fallout (frustration, demotivation, blame culture), I would now rate the approach of “every team pulls work independently” closer to a 1 or 2 out of 10.

Why Even Lower Now?

1. Compounding Stress and Multitasking Once morale drops, teams multitask more, make more errors, and spin their wheels—further worsening delays and dissatisfaction.
2. Frustration and Burnout Working hard without seeing tangible results undermines motivation and can lead to burnout or high turnover among frustrated team members.
3. Loss of Trust and Engagement People lose faith in leadership or processes if they feel stuck in a queue that only grows. Over time, cynicism and learned helplessness set in.
4. Vicious Cycle That’s Hard to Reverse Moral and psychological damage can linger even if you later introduce WIP limits or better coordination—once teams are deeply demoralized, recovery is much harder.

Why It’s Not a Zero

• Teams are taking initiative and “pulling,” which is directionally aligned with lean/agile concepts—just without the guardrails (like WIP limits, coordination around the constraint, shared priorities).

However, the combination of theoretical overload and negative psychological consequences is so severe that this approach is squarely in the “almost never do this” category. By introducing explicit constraints on the pull system—such as capacity buffers, WIP limits, and careful protection of the bottleneck—and ensuring cross-team alignment, you can avoid the worst-case scenario while still benefiting from autonomy and rapid flow.

Human:

thanks now make a short dialog between me (the human) and you the LLM out of this ... starting with you first evaluation. Then i ask about queuing theory, theory of constraint and psychology - then show how the evaluation drops and in the end your last statement. generate it in the shortest possible for that can be use in a carousel post

ChatGPT o1:

LLM: I’d rate “every team pulls work independently” at 8/10—it boosts autonomy and ownership. Human: What about queueing theory if everyone’s at 100%? LLM: Queues explode, lead times spike. That knocks it to 3/10. Human: And the Theory of Constraints? LLM: The bottleneck gets overwhelmed, throughput tanks—down to 2/10. Human: Factor in psychology? LLM: Frustration, blame culture, burnout—now 1/10. Human: Final verdict? LLM: Overall, 1–2/10. “Everyone pulling independently” isn’t advisable.

Human:

what is your reflection on this?

ChatGPT o1:

My reflection is that the short dialogue illustrates how an initially appealing idea—“every team pulls work independently”—quickly falls apart once you factor in real-world constraints and human psychology. It starts with high hopes of autonomy and speed but drops to a near-failing rating when you consider:

• Queueing Theory: Overloading leads to ballooning queues and skyrocketing lead times.
• Theory of Constraints: A bottleneck (constraint) chokes the entire system, making the rest of the teams’ work grind to a halt.
• Psychology & Morale: Frustration and burnout arise when people work at full throttle yet see little tangible progress.

Ultimately, a pull system must include guardrails (like WIP limits and prioritization) and coordinate around the bottleneck to avoid those pitfalls. The dialogue vividly shows how ignoring these factors causes a once-promising approach to drop from an 8/10 idea to a 1/10 cautionary tale.