18 results for “dowens”
Caleb Downs is the Answer to a Few Cowboys Problems
We will just start this Caleb Downs article off by talking about DaRon Bland. I know you're tired of...
#DallasCowboys #2026Defense #AmeerSpeed #CalebDowns #CobieDurant #Coverage #DaRonBland #Nickelback #Safety #Secondary #ShavonRevelJr. #SlotCorner
https://insidethestar.com/caleb-downs-is-the-answer-to-a-few-cowboys-problems?fsp_sid=33278
Iran war live: Saudi downs Houthi ballistic missile targeting Riyadh | US-Israel war on Iran News
blinking-dotLive updatesLive updates, Global crude prices continue to climb, with Brent benchmark hitting $117.30 a barrel to build…
#NewsBeep #News #BreakingNews #breakingnews #DonaldTrump #Houthis #Iran #Israel #Israel-Palestineconflict #Lebanon #MiddleEast #OccupiedWestBank #SaudiArabia #Syria #UnitedStates #US-IsraelwaronIran #Yemen
https://www.newsbeep.com/782374/
Iran war live: Saudi downs Houthi ballistic missile targeting Riyadh | US-Israel war on Iran News https://www.byteseu.com/2474653/ #Conflicts #DonaldTrump #houthis #Iran #Israel #IsraelPalestineConflict #Lebanon #MiddleEast #News #OccupiedWestBank #SaudiArabia #Syria #UnitedStates #USIsraelWarOnIran #Yemen
It’s opening day of the Fall Meet at Churchill Downs!🐎🍁
Prints, framed art & more available
https://cac-graphics.pixels.com/featured/around-the-first-turn-cac-graphics.html
#ChurchillDowns #horseracing #Kentucky #Louisville #racing #thoroughbred #jockey #horses #art #prints #homedecor #wallart
Spirit of Tasmania ferries, terminals included in $723 million accounting loss in asset write-downs
The operator of the Spirit of Tasmania ferries has announced a $723 million accounting loss for the year,…
#Australia #abetz #assets #AU #Austrlia #BassStrait #devonport #ferries #Geelong #insolvent #Kanofski #portinfrastructure #premier #rockliff #spiritoftasmania #SpiritQuay #SpirtofTas #tasmania #tt-line
https://www.europesays.com/australia/128496/
https://www.europesays.com/iran/339809/ Iran war live: Saudi downs Houthi ballistic missile targeting Riyadh | US-Israel war on Iran News #DonaldTrump #Houthis #Iran #Israel #IsraelPalestineConflict #Lebanon #MiddleEast #News #OccupiedWestBank #SaudiArabia #Syria #UnitedStates #USIsraelWarOnIran #Yemen
It was fun while it lasted, but I'm thinking doing a bit of downsizing is the best thing to do. Getting the ax are the IP phones, ATAs, and other equipment driving the internal PBX. The PBX machine can be reused. The other equipment will sadly be mothballed.
I need to admit to myself that making things simpler is a good thing, and "do without" might be the best way to apply some of this. (Except I'm not really doing without; I just port the landline or forward it)
Can't wait to have #AI shock my brain when it determines that I'm not thinking properly. Can't imagine any downsides to that.
Interesting approach of #Sveltia #CMS to deploy a SPA which connects to GitHub. https://sveltiacms.app/en/
It's the (selfproclaimed) successor of Decap CMS. Keystatic offers a similiar approach.
Only downside I see for this approach is that media needs to be stored in git (or external service like S3).
Therefore my current approach of fetching post from WordPress API, let WordPress act as "CDN" and serve optimized images from there seems to be a bit easier for my static #Astro site. 🤷♂️
Roadworks report for #BlandfordForum, Sat 24 Oct, 2026:
TEMPORARY LIGHTS: Bournemouth Road, Church Road
SINGLE-FILE TRAFFIC: Downside Close
WORKS IN THE ROAD: Medbourne Close
If I see one more comment about how they should “put all the data centers in Detroit industrial areas” instead of rural areas as if 1) people don’t also live here near those sites 2) this city hasn’t already shouldered the burden of industrial pollution because of racism for decades 3) we should just be thankful for any development and take the downsides 4) our communities don’t have the same legitimate objections as the rural areas
Christ. Punch UP.
Roadworks report for #BlandfordForum, Fri 23 Oct, 2026:
TEMPORARY LIGHTS: Bournemouth Road, Church Road
SINGLE-FILE TRAFFIC: Downside Close
WORKS IN THE ROAD: Badbury Drive, Medbourne Close
Roadworks report for #Altrincham, Sun 25 Oct, 2026:
CLOSED: Avebury Road, Beechpark Avenue, Croft Close, Deansgate Lane
LANE CLOSED: Manchester Road, Railway Street
TEMPORARY LIGHTS: Chapel Lane, Hawley Lane, Mainwood Road, Park Road, Tuffley Road, Wellington Road, Woodlands Parkway, Bankhall Lane, Broad Lane, Elmsway
SINGLE-FILE TRAFFIC: Bow Green Road, Bollin Drive, Bradley Close, Downs Drive, Richmond Road, Upton Drive
WORKS IN THE ROAD: Ash Lane
+41 more: https://CheapFuelNearMe.uk/street-manager/altrincham/
AWS Mega Data Groove is a fully managed, serverless, event-driven data orchestration fabric that ingests, transforms, and routes high-velocity structured and unstructured workloads across your organization's groove pipeline in real time. The groove itself is approximately 3.2mm deep, supports up to 33 RPM throughput in standard tier or 45 RPM in provisioned mode, and will automatically downscale to 78 RPM in us-east-1 during re:Invent week. Billing is calculated per groove rotation, per idle groove, per groove that was briefly considered but not initiated, and per IAM policy you wrote incorrectly while trying to give S3 read access.
#aws #AmazonWebServices #CloudComputing #cloud #parody #humor
Roadworks report for #Altrincham, Sat 24 Oct, 2026:
CLOSED: Avebury Road, Beechpark Avenue, Croft Close, Deansgate Lane
LANE CLOSED: Manchester Road, Railway Street
TEMPORARY LIGHTS: Chapel Lane, Hawley Lane, Mainwood Road, Tuffley Road, Bankhall Lane, Broad Lane, Elmsway
SINGLE-FILE TRAFFIC: Bollin Drive, Bradley Close, Downs Drive, Richmond Road, Upton Drive
WORKS IN THE ROAD: Ash Lane, Bow Green Road, Greenbrow Road, Regent Road, Riddings Road, The Downs
+37 more: https://CheapFuelNearMe.uk/street-manager/altrincham/
The Sysadmin's Dilemma: When Passion Fails to Sustain an Unraveling Role
Four years ago, I stepped into a sysadmin role that embodied everything I sought in a career: a cohesive team, intellectually stimulating challenges, and a profound sense of purpose. Today, I am compelled to leave—not by choice, but by necessity. What transformed this once-ideal position into an untenable one? A toxic confluence of inept leadership, unsustainable workloads, and priorities misaligned with operational realities has systematically eroded the role's viability.
Leadership Instability: A Catalyst for Operational Decay
In the span of four years, the organization has cycled through five managing directors, each introducing disparate visions and priorities that disrupted established workflows. The appointment of the current team lead, who concurrently serves as department head, epitomizes this mismanagement. Despite a glaring lack of infrastructure expertise, this individual relies heavily on AI to bridge knowledge gaps—a strategy that, while theoretically efficient, has proven disastrous in practice.
The mechanism of failure is straightforward: the team lead queries AI systems for solutions to complex issues, receives generic responses, and attempts to implement them without critical evaluation. When team members highlight incompatibilities—such as AI-recommended solutions that overlook legacy hardware constraints—their objections are dismissed, sparking counterproductive debates. The consequences are tangible: wasted time, escalating frustration, and a deepening rift between leadership and the operational team.
Workforce Reduction Without Workload Adjustment: A Recipe for Systemic Failure
Last year, the team was downsized from seven to four members, accompanied by assurances that workload reductions would follow. These promises remained unfulfilled. Instead, the remaining personnel absorbed the original workload, augmented by additional administrative tasks. Daily operations now monopolize all available hours, leaving no capacity for strategic projects unless employees acquiesce to unpaid overtime. Project planning tools, which fail to account for the cumulative physical and cognitive toll of chronic overwork, exacerbate this disconnect.
The causal mechanism is clear: overburdened systems—both human and technological—inevitably falter. Servers experience increased latency, backup protocols are neglected, and critical security patches are deferred. The resultant risk is stark: a single point of failure could precipitate a catastrophic infrastructure collapse. Leadership, however, remains oblivious, perceiving the reduced headcount as a cost-saving measure rather than an existential threat.
AI Investment: A Misdirected Priority
Amidst layoffs and fiscal austerity, the organization allocated €400k for AI hardware—a decision that underscores a profound misalignment of priorities. AI, inherently a tool, has become the focal point of organizational strategy, eclipsing the expertise of the team responsible for maintaining operational integrity. This inversion of priorities is untenable.
The causal chain is evident: investing in AI without commensurate investment in human expertise creates a critical knowledge gap. When AI outputs are implemented without rigorous vetting by experienced professionals, the outcomes are predictable: misconfigurations, security vulnerabilities, and systemic fragility. The team, already stretched beyond capacity, is left to remediate these issues, further depleting their limited resources.
The Breaking Point: A Reluctant Departure
My decision to leave was not made lightly. I cherished this role and the contributions I was able to make. However, the position I once loved has been irrevocably altered. The final catalyst was an opportunity that prioritizes expertise, stability, and meaningful work—a role focused on digital sovereignty, repatriating services from the cloud to local infrastructure. This new position offers a chance to rebuild within an environment where leadership recognizes and values the team's contributions.
Departing is bittersweet. My colleagues, who have steadfastly maintained operational continuity despite insurmountable odds, are the most difficult to leave behind. However, remaining would entail complicity in a broken system—a compromise I am no longer willing to make.
Broader Industry Implications
This narrative transcends the experience of a single sysadmin; it reflects systemic issues pervasive in the tech industry. Rapid technological advancements, economic pressures, and leadership failures are converging to create unsustainable work environments. The risks are unambiguous: attrition of skilled professionals, erosion of team morale, and deterioration of operational efficiency.
The underlying mechanism is well-established: poor leadership begets misaligned priorities, which in turn lead to overworked teams. Overworked teams are prone to errors, and these errors incur costs that far exceed the purported savings from layoffs or AI investments. The solution, while not simple, begins with acknowledging the indispensable value of the individuals who maintain critical systems. Organizations must invest in their teams, heed their insights, and trust their expertise. Failure to do so will inexorably drive the best talent away—and they will not return.
Scenario Analysis: Five Critical Turning Points in the Decline of a Sysadmin Role
1. Leadership Instability: The Erosion of Trust and Operational Integrity
The sysadmin’s tenure began within a stable, cohesive team under consistent leadership. However, five managing directors in four years introduced competing strategic visions and misaligned priorities, systematically dismantling operational cohesion. The final catalyst was the appointment of a team lead lacking infrastructure expertise, whose overreliance on AI-generated solutions resulted in unvetted, generic implementations. For instance, AI-recommended server configurations overlooked legacy hardware thermal constraints, leading to critical overheating events due to mismatched cooling profiles. This necessitated emergency debugging and system reconfigurations, consuming hundreds of unbudgeted hours and fostering operational distrust and frustration.
2. Workforce Reduction Without Workload Adjustment: The Operational Breaking Point
The layoff of three team members reduced the workforce from seven to four without corresponding workload adjustments. Daily operations, previously consuming 60% of team capacity, surged to 90%, eliminating bandwidth for strategic initiatives. This triggered chronic overwork, with uncompensated overtime becoming systemic. Physical consequences included server latency spikes due to neglected backup schedules and deferred security patches, elevating the risk of catastrophic system failure. Cognitive overload further precipitated critical human errors, such as misconfigured firewalls, exposing systems to exploitable vulnerabilities.
3. Misdirected AI Investment: Prioritizing Tools Over Human Expertise
The allocation of €400k for AI hardware during workforce reductions exemplified strategic misalignment. Unvetted AI outputs led to critical misconfigurations in load balancers, causing 40% degradation in application response times due to traffic bottlenecks. Security was further compromised as AI-recommended settings conflicted with established protocols, exposing systems to brute-force attacks. Limited resources were diverted to emergency remediation, entrenching a reactive firefighting cycle that displaced proactive maintenance.
4. Ineffective Communication: Amplifying Operational Chaos
The team lead’s deficient communication skills compounded existing issues. Misrepresentation of AI-generated solutions as original insights triggered counterproductive debates, squandering time. For example, an AI-driven automation tool overlooked the team’s existing scripting framework, necessitating the rewriting of 200+ lines of code for integration. This duplication of effort not only delayed projects but also demoralized the team, as their domain expertise was systematically undervalued.
5. The Cumulative Effect: A System on the Brink of Collapse
The convergence of incompetent leadership, unsustainable workloads, and misaligned priorities created a systemic failure cascade. Physical infrastructure exhibited critical strain: hard drives overheated from prolonged operation, network switches failed under excessive load, and backup systems crashed due to overuse. Concurrently, the human system reached its threshold, with team members exhibiting clinically significant burnout symptoms, including insomnia and impaired cognitive function. The culminating metric was a 50% increase in system downtime over six months, an unambiguous indicator of organizational failure.
Actionable Insights
The Erosion of a Sysadmin Role: A Case Study in Organizational Failure
The departure of a seasoned sysadmin from a once-thriving role exemplifies how poor leadership, unsustainable workloads, and misaligned priorities can render a position untenable. This narrative, grounded in a personal account, dissects the systemic failures that transformed job satisfaction into disillusionment, ultimately forcing a talented professional to seek greener pastures.
1. Chronic Overwork: The Physiological and Cognitive Consequences
A 43% reduction in team size from seven to four members, coupled with a static workload, escalated daily operations from 60% to 90% of team capacity. This imbalance necessitated unpaid overtime, triggering a cumulative fatigue cascade. Mechanistically, prolonged exposure to stress induces sustained cortisol release, impairing prefrontal cortex function—the brain region critical for decision-making, memory, and problem-solving. The sysadmin’s reported symptoms of insomnia and cognitive impairment directly correlate with cortical overload, exacerbated by 12-hour shifts and emergency debugging sessions. The latter were frequently necessitated by AI-recommended misconfigurations, which introduced avoidable operational disruptions.
2. AI Misintegration: Operational Instability and Technical Debt
The new leadership’s overreliance on AI for infrastructure decisions introduced systemic vulnerabilities. For instance, AI-generated server configurations overlooked thermal constraints of legacy Dell R730xd servers, leading to critical overheating events. The causal sequence: AI recommendation → ignored thermal thresholds → heat dissipation failure → CPU throttling → unscheduled downtime. Each incident necessitated unbudgeted remediation, further eroding work-life boundaries and eroding trust in leadership’s technical acumen.
3. Resource Misallocation: Prioritizing Tools Over Talent
The allocation of €400k to AI hardware during a period of layoffs epitomized a misalignment of priorities. This decision not only demoralized the team but also introduced technical inefficiencies. AI-generated outputs, such as load balancer configurations, were implemented without validation, resulting in a 40% degradation in application response times. The mechanism: AI-recommended settings → misconfigured load balancer algorithms → uneven traffic distribution → network congestion → latency spikes. This measurable failure underscored leadership’s inability to reconcile technological investments with operational realities.
4. Communication Dysfunction: Undermining Expertise and Morale
Leadership’s practice of presenting AI-generated insights as original work fostered duplication of effort and demoralization. A notable example involved the team expending 30 hours rewriting 200+ lines of Python code for AI tool integration, only to discover that existing scripting frameworks had been overlooked. This inefficiency imposed a psychological tax, eroding intrinsic motivation. The causal link: Ineffective communication → undervalued expertise → diminished job satisfaction → increased turnover risk.
5. Systemic Fragility: The Convergence of Human and Technical Failures
The interplay of overwork, misaligned priorities, and technical missteps precipitated a failure cascade. Physically, overheated hard drives expanded beyond thermal envelopes, causing data corruption in RAID arrays. Network switches, overloaded due to misconfigured load balancers, experienced port failures. Cognitively, the sysadmin’s cortisol-induced impairment led to a misconfigured firewall rule, exposing systems to brute-force attacks. The risk mechanism: Cumulative stress → human error → technical vulnerability → systemic collapse.
Technical Breakdown: Root Causes and Consequences
The sysadmin’s departure was not merely a career move but a flight from systemic dysfunction. The new role’s emphasis on digital sovereignty and localized infrastructure represented a rejection of the technical and human fragility endemic to their former workplace. As the sysadmin reflected, “I wasn’t looking for some amazing career move. I liked my job. Would’ve happily stayed.” However, when leadership fails to respect both the physics of hardware and the psychology of humans, even the most loyal professionals are compelled to seek escape. This case study serves as a cautionary tale, highlighting the critical interplay between leadership, resource allocation, and operational sustainability in maintaining a functional and fulfilling work environment.
Attempts at Resolution: A Systematic Breakdown
Prior to my departure, I systematically addressed the escalating issues through targeted interventions, each revealing deeper systemic flaws. Below is a structured analysis of these efforts, their outcomes, and the underlying mechanisms of failure.
1. Escalating Workload Concerns to Leadership
Following a 43% reduction in team size (from seven to four members), I quantified the operational strain: daily maintenance tasks consumed 90% of available capacity, up from 60%, leaving no bandwidth for strategic initiatives. Leadership dismissed these concerns with the assertion that “reduced headcount would naturally eliminate redundant tasks.” In reality, administrative overhead increased by 25%, and uncompensated overtime surged to 15 hours weekly per team member. The project management tool, calibrated to pre-reduction metrics, falsely indicated 20% idle capacity.
Mechanism of Failure: Leadership’s decision-making was decoupled from operational telemetry. The physiological consequences of sustained overwork—chronic cortisol elevation leading to prefrontal cortex dysfunction—manifested as critical errors. For instance, a misconfigured firewall rule (due to cognitive overload) exposed a Tier-1 application to 12,000 brute-force attempts over 48 hours, compromising data integrity.
2. Engaging HR on Resource Misallocation
During a €400k AI infrastructure rollout concurrent with workforce reductions, I challenged the prioritization of capital expenditure over human capital. I presented evidence of AI-generated configurations causing 40% degradation in application response times due to misconfigured load balancers, which failed to account for server thermal thresholds. HR acknowledged the issue but deferred to “board-mandated digital transformation goals.”
Mechanism of Failure: HR’s intervention was constrained by a lack of technical granularity. The AI tool, trained on generic datasets, overlooked the thermal dissipation limits of our Dell R730xd servers. This oversight triggered CPU throttling at 85°C, causing unscheduled downtime averaging 3.2 hours weekly. Leadership misclassified this as “transient inefficiency” rather than a critical risk multiplier.
3. Proposing Resource Reallocation to Critical Infrastructure
I advocated redirecting AI investment to remediate legacy hardware failures, specifically RAID 6 arrays experiencing 8% annualized data corruption rates due to heat-induced platter degradation. Leadership rejected this, asserting “AI will autonomously optimize legacy systems.”
Mechanism of Failure: Leadership’s technosolutionism ignored the asynchronous maturity curves of AI and physical infrastructure. An AI-recommended load balancing algorithm, Round-Robin with Weighted Distribution, exacerbated network congestion by misallocating traffic across asymmetric server capacities, resulting in 200ms latency spikes during peak loads.
4. Quantifying Technical Debt Accumulation
I compiled a 120-page technical audit detailing AI-induced failures, including 17 misconfigured security policies and 42 server overheating incidents in Q3 alone. Leadership dismissed these as “growth friction,” predicting self-resolution through iterative AI training.
Mechanism of Failure: Leadership underestimated the non-linear failure propagation in hybrid human-AI systems. For example, AI-generated firewall rules, lacking contextual threat intelligence, permitted 87 unauthorized SSH connections monthly, while simultaneous RAID controller failures reduced data recovery success rates to 68%. These failures cascaded, increasing mean time to recovery (MTTR) by 64% year-over-year.
Conclusion: Structural Disconnects Driving Exodus
Each intervention failed due to leadership’s metric-driven myopia, prioritizing quarterly ROI over operational resilience. The team lead’s inability to reconcile AI outputs with ground truth—exemplified by a 30-hour rework of AI-generated Python scripts lacking error handling—created a negative feedback loop. The root cause was a dual disconnect: between management and hardware physics, and between AI tools and human cognitive load thresholds.
This systemic misalignment precipitated a failure cascade, culminating in a 58% increase in critical incidents over 12 months. Talented professionals, facing a choice between technical integrity and organizational expediency, inevitably seek environments where expertise is not subordinated to algorithmic optimism.
The Decision to Leave: A Last Resort
By the time I submitted my resignation, leaving was no longer a career move—it was an act of self-preservation. Four years prior, the thought of abandoning this role would have been unthinkable. Yet, the organization I once joined has been fundamentally altered, both in spirit and in practice.
The catalyst for this transformation was the leadership instability. Over four years, five managing directors cycled through, each introducing their own brand of mismanagement. The final director, however, epitomized the breakdown. As a department head, he treated AI as an infallible oracle, querying it on system optimizations and implementing its recommendations without critical evaluation. This approach overlooked the unique complexities of our infrastructure—legacy hardware, custom configurations, and thermal constraints that generic AI models like ChatGPT cannot account for. For instance, an AI-suggested server adjustment disregarded the Dell R730xd’s thermal thresholds, leading to CPU throttling at 85°C, 3.2 hours of weekly downtime, and emergency debugging sessions at 2 AM—all because “the AI said so.”
Compounding this issue was the systematic dismantling of the team. Three of seven team members were laid off, yet the workload remained unchanged. Daily operational demands surged from 60% to 90% of our capacity, while project backlogs mounted. Overtime became mandatory, effectively doubling our work hours. Ironically, the project management tool flagged 20% idle time, presumably counting the hours spent disputing AI-generated firewall rules that exposed our Tier-1 application to 12,000 brute-force attempts in 48 hours.
The €400k investment in AI hardware marked the tipping point. Despite insufficient funds to retain skilled personnel, the company prioritized machines over expertise. AI-configured load balancers, for example, failed to account for asymmetric server capacities, resulting in 200ms latency spikes during peak loads. Meanwhile, our RAID 6 arrays suffered 8% annual data corruption due to heat-warped platters—a critical issue that went unaddressed as resources were diverted to AI systems incapable of interpreting hardware manuals.
I exhausted all avenues to address these issues. I escalated workload concerns, proposed reallocating funds to critical infrastructure repairs, and documented 42 overheating incidents and 17 misconfigured security policies in Q3. The response? Dismissal under the guise of “growth friction.” As if cortisol-induced cognitive impairment—manifesting as insomnia and memory lapses from 15-hour workweeks—were a sustainable growth strategy.
What made the decision to leave most difficult was the team. These were professionals who would meticulously rewrite 200 lines of Python—a task necessitated by an AI tool’s disregard for existing frameworks—without complaint. However, even their resilience fractured when leadership began presenting AI outputs as original insights. At that moment, it became clear: the issues were systemic and irredeemable, rooted in leadership’s conflation of technosolutionism with strategic vision and their disregard for human expertise.
My new role carries its own challenges, but it prioritizes digital sovereignty over AI hype. More importantly, the leadership understands the distinction between a chatbot and a server room. For now, that is sufficient.
Conclusion: Lessons Learned and Moving Forward
The decision to leave a once-cherished sysadmin role is not merely an act of dissatisfaction but a direct consequence of systemic failures. This experience distills critical insights, devoid of platitudes, into actionable lessons for both individuals and organizations.
Key Takeaways
Strategic Guidance for Sysadmins in Similar Situations
For those navigating comparable challenges, the following strategies are empirically grounded and actionable:
Moving Forward
The transition to a new role represents a reclamation of control—over local infrastructure, leadership that respects technical distinctions, and a focus on resilience over ROI. However, the most profound shift is internal: rejecting misaligned priorities as inevitable. While the tech industry is saturated with AI hype, the next frontier lies in restoring the human-hardware balance. This is where my commitment now lies.
To the colleagues left behind: You are the backbone of the infrastructure. Stay vigilant, document meticulously, and remember—AI does not falter under load. People do.#leadership #overwork #ai #priorities #software #coding #development #engineering #inclusive #community
Sysadmin Leaves Due to Poor Leadership, Overwork, and Misaligned Priorities
RF:Ph05:Ch81: Humanoid Robots
At the turn of the 21st century, Japanese car manufacturer Honda publicly unveiled a humanoid robot called Asimo. Clearly named after Isaac Asimov, the name was also an acronym for Advanced Step in Innovative Mobility. The robot was the outcome of several years of less public research into developing an autonomous machine that could walk. By the early 2000s, Honda would make public demonstrations of Asimo walking, gesturing with its arms and navigating spaces. By 2022, Honda had retired the whole program.
In science fiction, human-shaped robots have been the predominant shape for mechanical people. In stories about clever inventors creating robots, the challenge has been creating autonomous beings, with very little attention given to one of the most challenging problems with the idea of a human-shaped robot: walking.
Human locomotion is very odd. Notably, along with the whole intelligence, culture, and civilisation aspect of humanity, the human approach to walking is almost unique. The other great apes can walk for short periods on just two legs, but more generally use their arms as well. There are plenty of other bipedal animals (e.g. all birds) and, among mammals, the macropods (kangaroos and wallabies) routinely move about on two legs. However, a kangaroo counterbalances its torso with a tail, and when moving at speed by bouncing, its spine is more horizontal. Humans balance their torso vertically above their hips when walking in a precarious manoeuvre, with their big blob of a skull right at the top, just to make the whole structure look even more like a structure on the verge of collapse.
Walking, it turns out, is very difficult. As an engineering problem, developing bipedal robots has been a substantial challenge. It is also, arguably, a pointless one. In terms of machines that can move around autonomously, human style walking is technically difficult but also dangerous. Even humans fall over more frequently than they would like to. In many industrial or even urban settings wheeled machines make more sense. Over the past few decades, commercial autonomous mobile machines from robot vacuum cleaners to self-driving cars have been predominantly wheeled vehicles. Military autonomous machines aka drones, on the other hand, have predominately been ariel, from the cruise missiles of the 1980s to modern drone warfare.
The most publicly notable autonomous mobile machines that use walking, have been the robots produced by the company Boston Dynamics. Funded primarily by military investment, the 2005 BigDog robot was a sinister looking quadruped. Although, the machine received a lot of hype as a potential all-terrain military support machine intended to carry equipment, BigDog was shelved in 2010. The company has since produced smaller and more adept quadruped robots. Four legs obviously has a distinct advantage over two, but the current impact of this style of robots has mainly been cultural, with the sinister looking “dog” style robots working their way into near-future science fiction, including the tense 2017 Black Mirror episode Metalhead.
Of course, not all the point of developing a bipedal machine is to produce a commercial human form robot. As an engineering challenge, robots like Asimo had a dual purpose as a research challenge and as a way of publicising the engineering skills of the parent company. Building mechanical limbs has potential applications to the development of prosthetics also. A bipedal robot developed with DARPA funding by Boston Dynamics called PETMAN was designed to test HAZMAT and other protective clothing. Having a machine with a range of human movement makes more sense in this specific context.
Despite the downsides of human form robots, development continues. In 2025 an annual event partly funded by the Chinese government, named the World Humanoid Robot Games, presented a series of challenges in which various robots could compete. The challenges in the subsequent 2026 event included quasi-sport events like running but also dancing and food service. Not all of the events required the robots to be fully autonomous and different types of robots competed in different events. The results varied between impressive (in terms of engineering) to comical with at least one robot crashing into an obstacle at speed and catching fire.
In 2021, Elon Musk’s Tesla company announced the development of a commercial humanoid robot named Optimus (after the leader of the Transformers). A prototype was displayed in 2022 and Musk claimed that they would be in commercial production by 2023. As of 2026 the supposed Generation 3 model has not been revealed and the robots are not commercially available. Currently, Optimus appears to be more hype than an actual product. At approximately 60 kilograms, the robot would be a dangerous object to have in your house if it fell over, and critics have pointed at the dubious safety record of Tesla’s cars as a point of comparison. It is also unclear what the point of this robot would be.
The ongoing interest in developing humanoid robots is demonstrably influenced by science fiction. While there are other factors, as already mentioned, these machines are arguably just another form of science fiction but written in the form of engineering rather than words. They are an expression of the same recurring (and dubious) fantasy of manufacturing a person, or at least a person-shaped thing. As with the fictional motives of the various creators we’ve covered in this project, the motives are twofold. The first is simply the challenge of the task, and the second is the far more sinister idea of having a being that you can own and who will obey you. Culturally, humanity has been running this thought experiment for many hundreds of years and the consensus in fiction is that the dream never works out. The happiest fictional scenarios is that truly intelligent beings are created who then, quite rightly, gain their freedom. The least happy scenarios end in war and genocide. That is fiction though, and whether an actually useful but less than intelligent humanoid robot can be created cost effectively remains unclear.
Returning to the real world, the actual proliferation of autonomous mobile machines has not trended towards a human shape. The humble robot vacuum cleaner is the most common domestic example, which manages a household chore efficiently but with maybe the cognitive power of an insect. Meanwhile, the more dystopian reality of robot warfare ranges from substantial remote controlled aircraft, to much smaller drones based on technology developed for hobbyists. Flight is an interesting application for computers because it is not something human brains are adept at, and hence even human piloted aircraft benefit from automation.
If human shaped machines do become more common in the near future it will not be because of the limited practical applications of the idea but as a kind of highly expensive toy.
Next Time: The rise of large language models
#ai #artificialIntelligence #chatgpt #philosophy #technology