Key Point: The AI did not list “Bad Employee” indicators that Elon Musk’s “5 Accomplishment” Email could reveal. I told it that “NO-RESPONSE” was Self-Evident, since “Ignoring-The-Boss” is not acceptable. What else did it miss? I believe that it missed a lot of “Warning Signs” that a Demand for Email Response could reveal.
Wrong-CC-Email-for-Mgr: Everyone has a supervisor or manager, so if the employee does not know the contact for their boss (email), then something is wrong.
Accomplishments-vs-Job-Description: If the accomplishments are not listed in the employee’s job description, something is wrong. Taken one step further - make a summary of accomplishments by all employees in a department, and check to see if all job description tasks are covered.
Active-without-Manager: All Email Accounts should be DISABLED when person does not have a designated Department/Office or Supervisor (check Organization membership in Domain).
My “Theory”: AI handles Omissions, or the Abscence of Data “Very Poorly.” It can “Steal” Other People’s Conclusions, but it’s terrible at detecting a “Half-Truth” (where key factors are missing). On the “Donald Rumsfeld Matrix”, AI makes Really Bad Decisions when faced with “Unknown-Unknowns.”
Was I Being “Unfair” to Chat-GPT-4o-Mini with the “Questions” that I Presented to It? (“Prompts” below)
Elon Musk sent an email from a Department Of Government Efficiency (DOGE) account to all employees of US Federal Agencies asking for a reply by email with a CC to the person’s manager as well. The government workers were asked to reply with five accomplishments they made while doing their job during the previous week, and the message must not contain classified or confidential information. What are 12 results from sending this email which could indicate that the employee has very poor job performance, or fails to do their job at all? Provide explanations of why the results indicate that someone isn’t doing useful work for the US Government. The obvious result which shows failure to perform one’s job is not sending any reply to the email which asks for information.
What if an email response from the worker had a CC email address which did not match the organizational chart for managers in a department? What if a particular task which was listed as top concern or priority in a government office was never mentioned by any of the workers of that department? Isn’t it likely that the managers of departments would have productivity reports with totals for requests processed, and these statistics could be compared to the information found in the employee email replies? Do questions about situations where problems are the result of omissions pose significant obstacles for AI Systems to answer, as compared explicit cause and effect situations?
The “Rumsfeld Matrix” has four situations ranging from Known-Knowns to Unknown-Unknowns. There are two cases with a Known and an Unknown. In one, a person may not know Facts or the State of Something, but the person knows exactly how to determine the facts of a situation, given time or resources. In the other, a person is fully aware of the existence of some situation or activity, but has no ability to understand or predict it, although they can probably find an expert or some other person who can provide them with knowledge. Are AI Systems most likely to “Hallucinate” when given questions which are generally Unknown-Unknowns because of the inability of training data to provide meaningful context? Would the phrase “Crazy Like a Fox” be an example of human mental processes that provide advantages in dealing with Unknown-Unknowns? Elaborate on the “Rumsfeld Matrix” with more details.
Make the case for eliminating the system of Inspectors General in the Federal Government as the means to identify and prevent Fraud, Waste, and Abuses of Power. For their performance, use the Promises of Politicians and Career Bureaucrats in Washington DC compared to the recent Watchdog Reports (to include DOGE) and discoveries of Improper Payments (which may be as high as $1 Trillion over the last 4 years). Provide the number of Agencies in DC which are funded by the Budget Appropriations of the US Congress, and if this number is not available, explain the Civil Liabilities and possible Criminal Charges a CEO may face if they could not identify all of their source of revenue, or all of the accounts where corporate money was transfered to. Describe the reliability of US Business Accounting, Audits, and Fraud Prevention Practices.
In a Free Market, someone who makes a product trades it to another for something of value by mutual agreement where both parties consider the transaction to be beneficial. When services are the basis for commerce, the same motivation for mutual benefit in transactions exists. In contrast, the actions of a Lawyer may be taken under the premise of “It’s The Law”, and others are treated as subordinate by government authority, or inferior by lack of education. Actions of Bureaucrats generally increase time required for businessmen to generate wealth, and add to the costs of production as well. Processes directed by regulation are the typical reason that Bureaucrats act with little concern for what affected people may find to be excessively burdensome. Provide a dozen observations in quotes by Economic Philosophers who advocate for Capitalism over Centralized Authority which can lead to Tyranny in Society.
No data… My “Question”: How would the Replies to Elon Musk’s Request in Email Reveal that Employees (probably) have Poor Performance?
The most obvious way: Employee Ignores Email from “Boss” - which is not doing “work” as asked.
ChatGPT-4o-Mini did not “notice” that If “Accomplishments” of Employees Do NOT Match Job Description Tasks - then something is wrong (probably a “Bad Manager”).
I’m not covering “Many” of the Problems which could be discovered through an Email Reply (or Lack Thereof), but it seems that the “Thinking” of the AI ONLY Considered Contents-of-an-Employee-Email-Reply.
Actually, IP_Address on the Email Routing could reveal something about an Employee (but I don’t expect “FREE ChatGPT” to be that detailed).
Does it make sense now? Each “Bullet Point” (at the bottom) was a Single-Line Prompt to ChatGPT.
I’m interested if the responses you would get on a Paid License are more “Insightful” than the answers I got. I did think the Dozen Quotes which were returned from ChatGPT were Good Choices.
Did you understand the reference of having “Unknown-Unknowns” in a “Hypothetical Case” which is presented to ChatGPT for an analysis?
If Musk’s mass firing at Twitter offers any clues, this over-the-top kind of rhetoric appears to serve three purposes.
The blue-haired, true believers in progressive values intent on sabotaging the government and enabling corruption will quit in protest. Malignant narcissists often lack emotional control or the ability to see the consequences of actions (which require wisdom).
If statistics hold true, the 20% who do 80% of the work will welcome the opportunity to highlight their hard work and finally receive some recognition. The other 20% will find themselves receiving severance.
Employees on paper only or employees who don’t do any work will quickly be exposed as the result of nepotism or corruption.
Now, as you noted, an AI is bad at seeing through fake accomplishments, verifying who did what work, finding red flags when they require validation across several domains, or distinguishing when there are half-truths.
What do I think? I agree you identified some problems like reasons for a non-response other than “isn’t working”, like incorrect manager email.
But I do think this wasn’t an “With this email, I will be able to tell if this is a goog employee”. I think it was simply “is anybody home” and that all the managers were going to get a list of “not home” people to validate they had a good reason to miss having replied.
This is similar to the Social Security checks being sent out to 140yr olds. It’s a sanity check.
Now, for fun, I had the new Claude 3.7 analyze your post, and then write a new prompt that might have elicted a better response from the AI to your original question.
Claude 3.7 analysis
Analysis of the Critique of Elon Musk’s Email Request
Strengths of the Critique
Identifies non-obvious indicators - The critique goes beyond the obvious “no response” scenario to identify subtler indicators of potential workplace issues.
Systems thinking approach - The critique considers organizational structure and alignment (email chains, job descriptions, account management) rather than just individual performance.
Raises valid data integrity concerns - Issues like incorrect manager email addresses could indeed reveal organizational dysfunction.
Highlights potential for cross-referencing - The suggestion to compare accomplishments against job descriptions and department priorities shows analytical depth.
Identifies a potential AI limitation - The observation about AI struggling with omissions or absence of data raises a legitimate point about current AI capabilities.
Weaknesses of the Critique
Assumes malicious intent - The critique frames the email primarily as a “gotcha” exercise rather than considering legitimate efficiency or accountability purposes.
Overinterpretation - Some conclusions drawn (like “something is wrong” if manager email is incorrect) may have simpler explanations (typos, recent reorganizations, etc.).
Lacks political context - As a political analysis, it misses the broader implications of a private sector figure directing federal employees.
Conflates multiple issues - The critique mixes specific email analysis with broader philosophical questions about AI capabilities and government oversight.
Ideologically charged language - Terms like “Career Bureaucrats” and framing government workers negatively suggests a predetermined viewpoint rather than dispassionate analysis.
Lacks proportionality - The critique jumps from email responses to sweeping conclusions about government efficiency without establishing the connection.
Improved Prompt for Better Analysis
As a dispassionate political advisor, please analyze Elon Musk's email to federal employees requesting "5 bullets of what you accomplished last week" with CC to managers. Consider:
1. The political and administrative implications of a private sector appointee directly contacting federal employees
2. The potential legitimate purposes this data collection might serve
3. The potential unintended consequences or risks of this approach
4. How the data collected might be used constructively versus punitively
5. Historical precedents for similar accountability measures in government
6. How different stakeholders (career civil servants, political appointees, Congress, the public) might perceive this action
7. Alternative approaches that might achieve similar goals with fewer drawbacks
8. Legal and ethical considerations regarding chain of command and federal employment practices
Please provide a balanced assessment that considers both potential benefits and concerns from multiple perspectives.
This improved prompt:
Maintains political context
Encourages multi-stakeholder analysis
Avoids leading the analysis toward predetermined conclusions
Balances potential benefits with concerns
Asks for constructive alternatives rather than just criticism
Grounds the analysis in legal/ethical frameworks
Encourages historical context for better perspective
As a dispassionate political advisor, please analyze Elon Musk’s email to federal employees requesting “5 bullets of what you accomplished last week” with CC to managers. Consider:
The political and administrative implications of a private sector appointee directly contacting federal employees
The potential legitimate purposes this data collection might serve
The potential unintended consequences or risks of this approach
How the data collected might be used constructively versus punitively
Historical precedents for similar accountability measures in government
How different stakeholders (career civil servants, political appointees, Congress, the public) might perceive this action
Alternative approaches that might achieve similar goals with fewer drawbacks
Legal and ethical considerations regarding chain of command and federal employment practices
Please provide a balanced assessment that considers both potential benefits and concerns from multiple perspectives.
Analysis of Elon Musk’s Email to Federal Employees
Political and Administrative Implications
The direct communication from Musk, a private sector appointee leading DOGE, to federal employees represents a significant departure from traditional government protocols. This approach:
Bypasses traditional management chains - Typically, directives flow through department heads and supervisors rather than coming directly from political appointees to all staff.
Establishes precedent for private sector leadership styles in government - Musk’s approach mirrors tech industry practices of flattened hierarchies and direct accountability.
Signals the administration’s commitment to its efficiency mandate - The directness demonstrates the seriousness with which the administration views its government reform agenda.
Creates tension between political and career structures - This direct approach may undermine established administrative processes designed to insulate career civil service from political pressure.
Legitimate Purposes for Data Collection
This approach could serve several constructive purposes:
Rapid assessment of workforce productivity - Provides a quick snapshot of activity across agencies without lengthy formal reviews.
Identification of redundancies or inefficiencies - May reveal duplicate efforts or misaligned priorities across departments.
Increased transparency and accountability - Creates a culture where employees articulate their contributions explicitly.
Baseline data collection for future efficiency initiatives - Establishes a starting point for measuring improvement.
Cultural shift toward results-orientation - Signals a move from process-compliance to outcome-focused government.
Potential Unintended Consequences and Risks
This approach carries several significant risks:
Chilling effect on employee morale and trust - May be perceived as surveillance rather than improvement-oriented.
Incentivizes “busy work” reporting rather than meaningful accomplishment - Employees may focus on generating impressive-sounding bullets rather than substantive work.
Potential security concerns - Despite the warning about classified information, employees might inadvertently share sensitive details in their rush to comply.
Administrative burden - Processing and analyzing thousands of responses creates its own inefficiency.
Inconsistent data quality - Without standardized reporting formats, the resulting information may be difficult to analyze systematically.
Undermines existing performance management systems - May conflict with established evaluation processes.
Constructive vs. Punitive Uses of Data
The collected data could be used in various ways:
Constructive Applications:
Identifying best practices that could be shared across agencies
Recognizing high-performing individuals or teams
Uncovering structural barriers to productivity
Informing resource allocation decisions
Developing targeted training programs
Potential Punitive Applications:
Targeting specific agencies or individuals for staff reductions
Public shaming of departments deemed underperforming
Creating “hit lists” of employees who provided unsatisfactory responses
Justifying predetermined budget cuts
Historical Precedents
This approach has both parallels and departures from previous government efficiency initiatives:
Clinton-Gore “Reinventing Government” initiative (1993-2001) - Focused on streamlining processes but worked primarily through agency leadership rather than direct employee engagement.
Bush Administration’s Program Assessment Rating Tool (PART) - Systematic program evaluation but conducted at program rather than individual level.
Obama Administration’s Campaign to Cut Waste - Emphasized identifying specific inefficiencies but relied on traditional management structures.
Trump Administration’s Office of American Innovation - Similarly led by private sector figures but operated more at policy level than direct employee engagement.
Musk’s approach is notably more direct and individual-focused than these historical examples.
Stakeholder Perceptions
Different stakeholders likely view this initiative through different lenses:
Career Civil Servants:
May perceive it as intrusive micromanagement
Could see it as devaluing their expertise and professional judgment
Might worry about political motivations behind data collection
Some may welcome the opportunity to highlight their contributions
Political Appointees:
Likely view it as demonstrating decisive action on campaign promises
May appreciate the data for making resource allocation decisions
Could see it as a tool for identifying resistant bureaucracy
Congress:
Oversight committees may question the authority for such direct intervention
Budget committees might welcome data to inform appropriations
Some members may criticize as overreach, others as necessary accountability
Public:
Those favoring smaller government likely applaud the accountability measure
Those concerned about government expertise may worry about undermining institutions
Media coverage will shape public perception of whether this represents reform or disruption
Alternative Approaches
Several alternative approaches might achieve similar goals with fewer drawbacks:
Phased implementation - Begin with pilot agencies before government-wide rollout
Work through existing management structures - Have supervisors collect and synthesize accomplishment data
Focus on team/unit accomplishments rather than individual reporting
Provide more structure and guidance on what constitutes meaningful accomplishments
Combine with positive recognition for exemplary work to balance accountability with motivation
Establish clear purpose and feedback loop so employees understand how data will be used
Integrate with existing performance management systems rather than creating parallel processes
Legal and Ethical Considerations
This approach raises several legal and ethical questions:
Authority boundaries - Whether DOGE has authority to directly task federal employees
Merit system principles - Potential conflicts with civil service protections designed to prevent political interference
Labor agreements - Possible violations of collective bargaining agreements with federal employee unions
Privacy considerations - How employee response data will be stored, shared, and protected
Chain of command integrity - Bypassing supervisors may undermine management authority
Reasonable accommodation - Whether the uniform requirement accounts for employees with disabilities or special circumstances
Balanced Assessment
Musk’s email represents an innovative but potentially disruptive approach to government efficiency. Its directness may yield valuable insights and signal serious commitment to reform, but risks creating resistance, administrative burden, and unintended consequences.
The success of this initiative will largely depend on:
How the collected data is actually used
Whether employees perceive it as improvement-oriented or punitive
If it complements or conflicts with existing management systems
The administration’s willingness to adjust based on feedback
A more collaborative approach that works through existing structures while still maintaining accountability might achieve similar goals with greater buy-in and less disruption. However, the current approach does send a powerful signal about the administration’s commitment to challenging bureaucratic norms and demanding tangible results.
I will have to take more time to do a Point-by-Point Rebuttal to “Claude’s Analysis” - but I’ll provide a “shorter take” on the approach to “THE PROBLEM.”
President Reagan (1981-1988) observed that there was Fraud, Waste, and Abuse in the US Government, and came to the conclusion of “Do Not Spend the Money which Goes to Activities which Are Wasteful (etc).” This establishes the amount of time that “Government Tried to Solve Problem.”
Dear AI, Current National Debt has increased to $36 Trillion - Catalog EVERY Method which DC Politicians Proposed to Solve Fraud, Waste, and Abuse Problems (with a Spending Component). Now Mark Each of Those Methods as “Non-Solutions” which will ALWAYS Fail, no matter how many times they are tried.
Note - Common Sense: When a Group of People Have CREATED A PROBLEM, Asking Them to “Solve” the Problem They Created Usually Makes Things WORSE.
Logic: If EVERY POSSIBLE SOLUTION has been tried by “The Politicians” - then the Solution to the $36 Trillion Debt is “KILL ALL THE POLITICIANS.”
I suppose “you” could ask the A.I. - Of all Methods Used by DC Politicians … Make a List for Each Method which Descibes “Failure to Perform Sanity Checks” throughout the Steps for the Method. This requires the analysis of 35 years of Washington DC Behavior, so this could take a while.
I hope not. I am not endorsing Claude’s analysis. It was just an example of who I prompt and the results I got.
You do make a good point - push back is important. You will push back differently than me. AI is great for that, they don’t have convictions and opinions.
I did some research for a financial friend of mine using ChatGPT DeepResearch on a question he gave me. And yet, without him pushing back to challenge the AI to think like HE does, he’ll never see the true possibility.
Actually, IP_Address on the Email Routing could reveal something about an Employee (but I don’t expect “FREE ChatGPT” to be that detailed).
I did think the Dozen Quotes which were returned from ChatGPT were Good Choices.
Did you understand the reference of having “Unknown-Unknowns” in a “Hypothetical Case” which is presented to ChatGPT for an analysis?
When a Group of People Have CREATED A PROBLEM, Asking Them to “Solve” the Problem They Created Usually Makes Things WORSE.
You probably know format for “The Joke(s)” where it gets to the point…
If we do that, ______ will happen!!!
You say