When you are experiencing hard times because of your Homeowners Association, you have to learn—or revert to—certain behaviors to stay afloat while you figure out how to get your life out of shambles. Living in a working-class neighborhood should not be so difficult. We go to work our asses off, come home, eat, then sleep. But when you already are in a disadvantaged situation, you are hyper aware that there is a thin line between working-class and below the poverty line.

Although I have been in several hurricanes, I never lost power continuously until Milton. I remember waiting to see if we were going to make it to three days sans electricity so that we could get emergency food assistance from FEMA. And it did take three days for the power to be restored. I immediately applied to FEMA. More and more days passed as I listened to other people I knew (and didn’t know) get their food assistance. But I did not. In fact, I have never gotten any type of assistance from FEMA for any of the five storms since I have lived in my house.

As I began to toss items from the main fridge, the mini fridge, and my camping fridge into the garbage, I wondered if I really had to throw these items away. I mean food guidelines are just guidelines, right? It was a painful process because I have a “thing” about wasting food—I don’t waste anything. I will eat the same meal for a week. I scrape my plates. I bring home scraps from restaurants. And I will eat your leftovers on your plate so that no food is wasted. It took me two weeks to throw everything out because I could not stay committed. My parents had to make me do it.

My refrigerator has never been the same. The storms delayed the start of my job. I missed the date to file for my last unemployment check by one day. All the damage to my home stressed me out and FEMA nor my insurance company were easy to deal with. When I get stressed, I get sick. Of course it didn’t help that my new boss was bitch of the year. Then, I found out my great, long-time friend that I met on base had died. Then my 102 year-old auntie from whom I was trying to learn my ancestry died. All of this affected my income. And as food prices continued to rise, I had trouble restocking while trying to overcome the mental anguish of knowing what the food prices used to be.

I’ve been poor before: foodstamps/EBT, WIC, TANF/AFDC, Medicaid, health departments, sliding scale clinics, Goodwill, and garage sales (never got section 8 in any state because the waiting list was 5 to 10 years!). Up North, I remember a Haitian church that used to feed those in need hot meals twice a week. I did not accept their offer until one day they yanked me inside. And we have some food banks in my area in which I have seen lines of people in the morning while I go to work while thinking,

Damn. I need to be in that line!

Bay Area Legal Services asked me why I was not on foodstamps.

Foodstamps? I can get foodstamps? I didn’t think a “‘homeowner’ with a car” could get foodstamps (well, at least not down here in Florida).

Plus with all the drama with the government shutting down benefits and non-poor people always trying to decide what others should be able to eat (in-group vs out-group, exclusionary…I am going to keep hammering this), I didn’t think it was worth the effort.

I got on public assistance as a teen. And I remember when I got off it and how proud I was that I had graduated from college and gotten my first big girl job. I went to Panera Bread for the first time…and I bought…organic milk! Oh well, that was yesteryear, this is today. At least I can now make meals for my parents that have been feeding me incessantly during this time.

here is the so called elephant text, a pretty good one with a powerful elephant metaphor which came to me, just like that!

I followed an impulse to remove it, because it gave the impression that I was offended, but I was!

And rightly so! I reserve the right at any and all times to be: OFFENDED!

🤌🤌

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My face looks like it’s got the texture of an elephant’s; with wrinkles. That’s a recurring thought which strikes me lately when I see my brightly lit face in the bathroom mirror. It’s been a gradual change which suddenly reaches a certain threshold, and then you see it clearly. But not before!

The kind aunt called me earlier today to tell me I’m wrong about my childhood. Apparently she’s a subject matter expert.

But I’ve become an elephant. Elephants never forget.

I was therefore able to take what she said with a grain of salt fortunately.

She hadn’t seen my metamorphosis.

That was the last time I referred to her as the kind aunt, though.

So everything changes

Kooperation gilt heute fast überall als Schlüsselkompetenz: in Teams, in Organisationen, in Bildungskontexten. Gleichzeitig bleibt oft unklar, was mit guter Zusammenarbeit eigentlich gemeint ist. Reicht es, wenn alle nett sind? Oder wenn Geben und Nehmen fair austariert sind? In meinen Leadership-Trainings und auch im Unterricht beobachte ich immer wieder dieselbe Spannung: Menschen wollen kooperativ sein, fürchten aber, ausgenutzt zu werden. Genau hier setzt die Arbeit von Adam Grant [1] an. Seine Typologie der Kooperation liefert ein überraschend nüchternes Raster, um diese Spannungen besser zu verstehen – ohne moralischen Zeigefinger, aber mit klaren Befunden.

Die vier Typen der Kooperation

Adam Grant unterscheidet vier grundlegende Kooperationsstrategien. Wichtig ist mir vorab ein Punkt: Es handelt sich nicht um feste Persönlichkeitstypen, sondern um Verhaltensweisen, die stark vom Kontext geprägt sind.

1. Der Nehmer Nehmer handeln konsequent eigennützig. Sie unterstützen andere nur dann, wenn sie sicher sind, mehr zurückzubekommen, als sie investieren. Kooperation ist für sie ein Mittel zur individuellen Vorteilsmaximierung. Kurzfristig können Nehmer erfolgreich wirken, langfristig beschädigen sie jedoch Vertrauen und Beziehungen. Ihre Reputation leidet, und Netzwerke schliessen sie zunehmend aus [2], [3].

2. Die Tauscherin Tauscher orientieren sich strikt an Ausgleich und Gegenseitigkeit. Hilfe erfolgt nach dem Prinzip „Wie du mir, so ich dir“. Fairness steht im Zentrum, nicht Grosszügigkeit. Wer mehr gibt, als zurückkommt, fühlt sich benachteiligt; wer weniger gibt, wird sanktioniert. Laut Grant ist dies die verbreitetste Strategie in Organisationen, weil sie sozial akzeptiert ist und Nehmerverhalten begrenzt. Gleichzeitig verhindert die ständige Bilanzierung, dass Vertrauen wirklich wachsen kann [2], [3].

3. Der fremdbezogene Geber Kluge Geber helfen anderen, wenn ihr eigener Aufwand geringer ist als der Nutzen für das Gegenüber. Sie starten mit Vertrauen, setzen aber klare Grenzen. Wird dieses Vertrauen missbraucht, stellen sie ihre Unterstützung ein. Diese Kombination aus Prosozialität und Selbstschutz erweist sich in Grants Studien als besonders erfolgreich. Kluge Geber bauen starke Netzwerke auf, ohne sich selbst zu überlasten. Sie geben strategisch dort, wo es wirklich wirkt [2]–[4].

4. Die selbstlose Geberin Selbstlose Geber stellen die Interessen anderer konsequent über ihre eigenen, selbst wenn sie ausgenutzt werden. Harmonie und Anerkennung sind zentral, eigene Bedürfnisse treten zurück. Grant zeigt deutlich: Diese Gruppe weist die höchsten Burnout-Raten auf und ist beruflich im Schnitt am wenigsten erfolgreich. Selbstlose Geber werden oft übersehen, ihre Beiträge für selbstverständlich gehalten. Nehmer nutzen ihre Bereitschaft systematisch aus [2]–[4].

Die vier Kooperationstypen nach Grant (eigene Darstellung mit NotebookLM)

Der zentrale Befund ist bekannt, aber dennoch irritierend: Am unteren Ende der Erfolgsskala, so Grant, finden sich selbstlose Geber, im Mittelfeld Tauscher und Nehmer, an der Spitze kluge Geber. Entscheidend ist nicht, ob* jemand gibt, sondern wie.

Adam Grant

Adam M. Grant (*1981) ist Organisationspsychologe und Professor an der Wharton School der University of Pennsylvania. Internationale Bekanntheit erlangte er mit Give and Take (2013, deutsch: Geben und Nehmen), in dem er auf Basis umfangreicher Studien zeigt, dass Erfolg weniger mit Durchsetzungsstärke als mit klugem, begrenztem Geben zusammenhängt [1]. Grant verbindet experimentelle Forschung mit anwendungsnaher Organisationspsychologie. Seine Arbeiten richten sich explizit an Praktikerinnen und Praktiker – ein Grund, weshalb sie in Leadership- und Bildungskontexten so anschlussfähig sind.

Was bedeutet das für die Führung?

Für #Führung – bewusst breit verstanden – sind Grants Befunde relevant, weil sie zwei weit verbreiteten Annahmen widersprechen: erstens, dass Wettbewerb Leistung steigert, und zweitens, dass bedingungslose Hilfsbereitschaft per se wünschenswert ist:

• Erstens zeigt sich, dass stark wettbewerbliche Kulturen Nehmer- und Tauscherverhalten fördern. Wissen wird zurückgehalten, Unterstützung strategisch dosiert. Vertrauen bleibt fragil [2], [3].
• Zweitens ist selbstloses Geben kein tragfähiges Ideal. Führung, die permanente Verfügbarkeit und Hilfsbereitschaft implizit erwartet, produziert Überlastung und lädt Nehmer geradezu ein [4].

Produktiv wird Führung dort, wo kluges Geben möglich ist: Vertrauen als Ausgangspunkt, klare Grenzen als Korrektiv. Das zeigt sich auch im Führungsverhalten selbst – etwa beim Delegieren von Verantwortung, beim Zulassen von Kompetenzgefällen oder beim bewussten Verzicht auf permanente Kontrolle. Führung wird damit weniger zu einer Frage der Macht, sondern der Rahmensetzung.

Was heisst das im Unterricht?

Auch im Unterricht, insbesondere in der Erwachsenenbildung, begegnen mir die vier Typen regelmässig. Gruppenarbeiten, Peer-Feedback oder kollaborative Lernformate sind ideale Beobachtungsfelder.

Selbstlose Geber übernehmen oft zu viel, erklären alles, tragen Gruppenarbeiten. Tauscher achten genau darauf, wer wie viel beiträgt. Nehmer profitieren davon – zumindest kurzfristig. Ohne didaktische Rahmung kippen kooperative Settings rasch in Schieflagen.

Didaktisch interessant ist daher nicht, alle zum Geben zu motivieren, sondern kluges Geben zu ermöglichen: transparente Erwartungen, begrenzte Aufgaben, klare Verantwortlichkeiten. Lernende sollen erfahren, dass Kooperation sinnvoll ist, ohne Selbstaufgabe zu verlangen. Gerade in der Erwachsenenbildung ist das auch ein implizites Leadership-Learning.

Mein Fazit

Was mich an Grants Typologie überzeugt, ist ihre Nüchternheit. Sie romantisiert Kooperation nicht, verteufelt Eigeninteresse aber ebenso wenig. Überrascht hat mich vor allem, wie klar die Daten gegen selbstloses Geben sprechen – ein Ideal, das in vielen Organisationen und Bildungskontexten immer noch hochgehalten wird. Ich habe gelernt, dass die Frage nicht lautet „Wie bringe ich Menschen dazu, mehr zu geben?“, sondern „Wie schaffe ich Bedingungen, unter denen kluges Geben rational und nachhaltig möglich ist?“

In Führung wie im Unterricht geht es nicht darum, Nehmer auszumerzen oder Selbstlosigkeit zu belohnen. Entscheidend ist, Kontexte zu schaffen, in denen kluges Geben sichtbar, begrenzt und wirksam ist. Kooperation ist dann keine moralische Pflicht, sondern eine kluge Strategie.

Drei Handlungsempfehlungen

1. Unterscheide klar zwischen Geben und Selbstaufgabe. Beispiel: Setze in deinem Team oder Kurs explizite Limits für Verfügbarkeit – etwa durch Sprechstunden statt permanenter Erreichbarkeit. Mache deutlich, dass Nein-Sagen nicht egoistisch, sondern professionell ist.
2. Gestalte Rahmen statt Appelle. Beispiel: Statt an Teamgeist zu appellieren, führe transparente Dokumentationspflichten für Beiträge ein (z. B. in Projekten oder Gruppenarbeiten). So wird sichtbar, wer was leistet – und Tauscher wie Nehmer müssen ihre Strategien anpassen.
3. Thematisiere Kooperation als Lerngegenstand. Beispiel: Diskutiere zu Beginn eines Projekts oder Kurses offen die vier Typen. Frage: „Welches Verhalten wollen wir hier fördern? Woran merken wir, wenn jemand ausgenutzt wird?“ Das schafft ein gemeinsames Vokabular und senkt verdeckte Konflikte.

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💬 Kommentieren (nur für write.as-Accounts)

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Quellen [1] A. Grant, Geben und Nehmen: Warum Egoisten nicht immer gewinnen und hilfsbereite Menschen weiterkommen, München: Piper, 2013.

[2] J. Beil, „Karriere: Mit diesem Verhalten steigt die Chance auf beruflichen Erfolg“, Handelsblatt, 28. Jan. 2026. [Online]. Verfügbar: https://www.handelsblatt.com/karriere/karriere-mit-diesem-verhalten-steigt-die-chance-auf-beruflichen-erfolg/100007985.html

[3] Redaktion Personalwirtschaft, „Tauschen ist das neue Nehmen“, Personalwirtschaft, o. J. [Online]. Verfügbar: https://www.personalwirtschaft.de/news/hr-organisation/kollaboration-tauschprinzip-verhindert-echtes-teamwork-103566/

[4] D. Schmid, „Kooperation: Diese 4 Team-Typen gibt es in jedem Unternehmen“, impulse, o. J. [Online]. Verfügbar: https://www.impulse.de/personal/kooperation/7310209.html

Bildquelle Paul Klee (1879–1940): Liegend, Detroit Institute of Arts, Public Domain.

Disclaimer Teile dieses Texts wurden mit Deepl Write (Korrektorat und Lektorat) überarbeitet. Für die Recherche in den erwähnten Werken/Quellen und in meinen Notizen wurde NotebookLM von Google verwendet. Die Infografik zu den vier Typen wurde von NotebookLM basierend auf meiner Inhaltsangabe generiert. Ergänzender Prompt: „Verwende einen typischen Whiteboard-/Flipchart-Stil und stelle die 4 Typen anschaulich dar.“

Topic #Erwachsenenbildung | #Coaching

The image above is a post I made on all social networks that I use. The picture above is a screenshot from Threads.

I've been sent a cancer screening kit from the NHS. As I say in the post this is not a task I am looking forward to. The post on BlueSky got no response. The post on Mastodon got a couple of replies. The post on Threads though has almost a hundred replies at the time of writing this. It's become almost a support network of people who have also got to do theirs and people who have done it supplying advice.

Most people who know me on social media know I post bog standard boring day to day stuff. I thought this post was exactly the same, but it seems to have struck a chord with some who are heading off to do the same thing. It is also amazing that those that have been through this and got the results that nobody wants have also commented and encouraged.

As I say I thought I was posting a boring everyday thing. I was also kind of not wanting to go ahead and do the test. My uncle passed away a few days ago from cancer. My Dad has had a six year long battle with a couple of cancers. With that in the back of my mind of course I'm here thinking the writing is on the wall for me.

Anyway I am not making any points here, I just wanted to get the words out of my head. If a small post like mine can have people conversing about cancer in a healthy way then all's good.

The ironic thing is the results from this free NHS cancer test will come back quicker than the paid for Ancestry DNA test. Our NHS is amazing.

14 January 2026

Flat light (working title): The light bulb in my flat, my flat through the light bulb. Hard to say if it's working or not yet. Have been looking at Artschwager's Intersect (1992) aquatint/drypoint work of a dog in a corner a lot this week. That monochrome approach to sitting at some essential point where vision both understands an essence and fails to differentiate between its constantly changing parts felt (and still feels) like something related to why I keep approaching light. And so I painted a corner of my room through an unilluminated light bulb. Mixed colors instinctually this time (as opposed to from a reference work), and while I did not intend this, it occurred to me after I finished working how the hues and tones seem to relate directly to the amalgam of visual sensations I've absorbed in my room in the three plus weeks since I moved in.

Hah, I made a mistake. In my post, New Apple Watch Sleep Tracker Results, I forgot to increase the counter. The last ... posts #3 and New Apple Watch Sleep Tracker results have the same #48. Which is wrong. This post here should be #98 but is instead 99!

I was checking my general post count on write.as and saw that the overall count is not the same as I expected. I've clicked through the posts and located the issue. Editing all the post would make to much work, so I decided to write this post instead as a clarification.

The next post will be the last in this round of #100DaysToOffload.

🎉

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99 of #100DaysToOffload
#log
Thoughts?

Rayleighův prd je fyzikálně-dědkovský jev, ke kterému dochází, když jeden dědek prdne na druhého bez vyvolání nasraného stavu. Nedochází k agresi, odvetě ani k brblání — pouze k indukovanému vyprdnutí. Zasažený dědek nevypustí vlastní originální prd, ale modifikovanou kopii původního prdu, lehce posunutou vůní, tónem a délkou doznívání. Jde o čistý přenos prdelní informace. Klíčové je, že prd se nezesiluje, ale přenastaví. Stejně jako Rayleighův rozptyl mění barvu světla bez jeho zničení, Rayleighův prd mění charakter prdu bez emoční excitace. V Dědolesu se tento jev považuje za známku vysoké prdelní vyzrálosti — dědek, který podlehne Rayleighovu prdu, je klidný, stabilní a prdelně kompatibilní s okolím.

Ramanův prd je naopak neelastický prdelní rozptyl. Původní prd sice zasáhne cílového dědka, ale část prdelní energie se přenese do jeho emoční struktury. Dědek se excituje, zpravidla se nasere, začne funět, zrudne a výsledný prd už není kopií, ale zcela nový stav. Má jinou frekvenci, jinou pachovou stopu a často i delší dozvuk s verbálním doprovodem typu: „No to si děláš prdel?!“ V Dědolesu se Ramanův prd používá opatrně. Je to mocný nástroj, ale nebezpečný — může rozjet řetězovou reakci nasranosti, kdy se z jednoho prdu stane prdelní konflikt. Zatímco Rayleighův prd je znakem harmonie a klidu, Ramanův prd je počátek dramatu, legend, hádek u lavice a někdy i týdenního ticha.

She got me a framed photo of us after one of our early surprise dates. I’m so happy.

I am working now, don’t have time to write

I have slept poorly it is however THURSDAY soon the weekend will be upon us!! Take heed!

I feel my soft yoga body and I would like to think that all is good

I wrote a really strong elephant post yesterday but was stricken by an impulse to delete it

Not entirely sure why?

I will write it again some day

Maybe it felt too personal but that hasn’t stopped me before?

I just say ”fuck it” and post; that’s why everyone loves this blog !

Anyway I better get back to work now, I am sure Windows updates are through

raindrops like berries on the winter morning hawthorn

Yesterday, I was trying to build an igloo with my oldest. As a child, every time it snowed, I was so excited to get outside to build an igloo. I simply just started. I grabbed a shovel and made a massive pile of snow. When the way was too long to the igloo, I made huge snowballs and rolled them onto the snow pile. After compacting everything, I just dug a hole into the pile and was done.

Now as an adult, I’ve tried to overengineer the whole thing. My son just wanted to start, but I was not ready. I wanted to have a plan. A proper way to build this thing. I would rather not make a massive pile of snow and then dig into the snow. (I am a grown-up now; I can't lie on the ground and dig a hole into a pile of snow.) I wanted to build a good igloo. So I searched for ways to accomplish that. It took far too long. My oldest was bored and did other stuff meanwhile.

We lost some time, and it gets dark early in the winter. 😅 We created the first half of the igloo. From here on, my plan was not working out anymore. So I had a new thought. Why not create blocks of snow and do it the Minecraft way? (My son has been into Minecraft for some weeks now.) He approved it, and we started filling buckets with snow, compressed them, and placed them in a line.

Now the “blocks” are waiting to be assembled.

I wish I could be as carefree as a child again. Just do things without planning them to death. But then, you have moments where the “wisdom” of an adult has prevented the child from frustration. (Still, frustration is a good thing for learning.) It is really strange. Both worlds have their pros and cons. Somehow, we need to align to get the best of both.

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97 of #100DaysToOffload
#log
Thoughts?

CeCe is Normal

#nsfw #CeCe

I remember the relief washing over me when CeCe actually agreed to get help. After that eye-opening moment in our dorm, where I'd seen the DMs and realized how deep her obsession ran, I gently suggested she talk to someone—a counselor, maybe, through the college's free services. To my surprise, she nodded, her fingers still idly tracing patterns on her inner thigh. “Yeah, okay, Tasha. If it'll make you feel better.” I thought this was it—the turning point. Maybe she'd dial it back, find some balance. But CeCe had her own way of twisting things, and it wasn't the help I expected.

She ended up booking sessions with this college intern at the student wellness center—a young psych major doing her practicum, not even a full therapist yet. CeCe framed the whole thing so cleverly, like she was pitching a TED Talk on self-empowerment. She'd sit there, all composed, explaining how watching porn was her form of emotional regulation—a safe outlet for stress in our high-pressure city life, where the constant grind of classes and part-time jobs could crush you. “It's safer sex, you know?” she'd say, according to what she told me later. “No risks, no heartbreak, just me controlling my own pleasure. It's made me less shy, more confident in my body. I used to hide these curves, but now? I own them.”

She made it a habit to dress nice for her sessions, further playing the charade and crafting her narrative. I complimented her on her new look quite a bit until I realized why she did it. I knew I had created a monster.

The intern bought it hook, line, and sinker—probably because CeCe was so articulate, so damn smart about justifying her freak flag. After a few sessions, the intern declared her “well-regulated and genuinely happy,” suggesting only that she keep journaling her feelings. No red flags raised, no interventions suggested. CeCe came back from those appointments beaming, like she'd gotten a gold star for her addiction.

All the while, though, she was escalating behind closed doors—or rather, in our very open dorm room. It escalated slowly. She did more than play porn casually during down time. She started playing porn videos in the background while she studied, the low volume moans and slaps mixing with her typing on engineering problem sets. She'd sit at her desk naked all the time. Her caramel skin always bare and glowing under the fluorescent lights, thick thighs pressed together, and she absentmindedly rocked against the chair, humping to stimulate herself.

From that point forward, I never saw her wear clothes in our dorm. She continued to lounge around nude, her full breasts swaying as she moved, that juicy ass planted wherever she pleased, chatting with me about classes like it was nothing. I didn't stop her. I enjoyed looking at her naked. My own porn consumption had silently turned me bisexual a long time ago.

As far as masturbating, she pushed this to new levels. She eroded all shame when it came to me. If I saw her naked, she was probably touching herself.

I'd be venting about my day, and there she'd be, fingers dipping into her wet pussy right in front of me, moaning softly as she nodded along. “Uh-huh, that sucks, Tasha,” she'd say, her voice breathy, eyes half-lidded while she pinched her nipples or rubbed her clit in lazy circles.

I didn't stop her. I didn't mind. I had a friend that would listen to everything. She was more attentive than most boyfriends I dated. She was just so raw, direct, honest and didn't ask for anything in return. So I accepted her overtly sexual habits. She was still a good person. But I knew she was turning into an out of control naked freak.

She had started watching public porn, almost exclusively. She had fixations on everything. What color clothes she wore, her favorite pen, notebook, socks. It didn't matter; something had its place in her life. Her porn was no different.

Her blatant exhibitionism bled into every moment in front of me. She chose me. Legs spread, wet and insatiable. Looking me in the eyes like I was her whole world outside of porn. Everyone else had bailed, but I stuck around, hoping the “therapy” would kick in eventually. I think she was conditioning me to normalize her behavior instead.

It all came to a head one crisp morning in our bustling city, where the air hummed with the sounds of commuter trains and street traffic outside our dorm window. I was rushing to class, grabbing my bag, when I caught CeCe slipping out the door ahead of me. She was dressed—if you could call it that—in just a baggy zip-up hoodie that hung loose over her frame, a pair of tiny shorts that barely covered her thick ass, and flip-flops slapping against the floor. No shirt, no bra, nothing underneath that hoodie. Probably no panties either. Her large breasts were basically hidden under the baggy fabric, sure, but one wrong move—a gust of wind, a quick turn—and she'd be flashing the whole hallway. She was heading to her therapy session and then straight to class, essentially topless, like it was no big deal.

“CeCe, wait—what are you wearing? Or... not wearing?” I called out, my voice a mix of exasperation and concern. I did my best to keep my voice down as to not draw attention in the hallway.

She turned, zipping the hoodie up just enough to tease the outline of her curves, a sly grin on her face. “Relax, Tasha. It's baggy—my tits are totally hidden. See? No one's gonna notice. And if they do, maybe it'll brighten their day.” She had a way out for everything, twisting logic until it fit her narrative, leaving me speechless once again.

Life in our college dorm carried on with that laid back vibe you only get in a place like our campus—where eccentricity was just part of the scenery, and as long as you weren't causing chaos, no one batted an eye. CeCe's increasingly bold outfits, or lack thereof, flew under the radar; professors and classmates shrugged it off as her quirky style, especially since she was killing it academically.

She was the star student, pulling in straight A's in her engineering courses while I scraped by with B's, my focus split between classes and worrying about her. It was frustrating, but also a twisted point of pride—my best friend was thriving, even if it was fueled by her nonstop naked porn habit.

There were moments when I genuinely had fun with her, though, dipping into her world when the stress of college life got too heavy. On rough nights after exams or bad shifts at my part-time gig downtown, I'd strip down alongside her, our naked bodies lounging on the beds as we scrolled through porn videos together. It was a release—her caramel curves pressed close to mine, the air thick with shared arousal as we'd touch ourselves, moaning in sync to some steamy scene.

Our friendship was charged with this electric tension, platonic at its core but teetering on the edge, never quite sure if we'd cross that line and turn it into something more. CeCe always brushed it off with a laugh, her fingers still slick from her latest orgasm. The room always smelling like pussy when we gooned together. “Porn's perfectly okay for me, Tasha. It's all I need—why complicate things?” A part of me secretly wished we could cross that line. But I held my tongue.

I didn't want to ruin a good thing. It's not like I was having any good dates worth my time anyway. I was always thinking about what porn CeCe was watching in our dorm while rubbing herself silly. They just never had that spark I was looking for. It just felt hollow. It felt empty. It felt meaningless. Maybe CeCe was onto something with her lifestyle.

It wasn't until later, during one of our late-night talks, that she opened up about being autistic. She said it casually, like explaining a homework problem, and suddenly it all clicked—the hyperfocus on her obsession, the way she justified everything so logically, the lack of an off switch for her escalating behaviors. It made sense why porn had gripped her so hard; it was a sensory fixation, a safe routine in a chaotic world. I knew then there was no flipping that switch back—CeCe was wired this way, and while it worried me, she'd become my ride-or-die best friend, the only person who truly got me in this massive, impersonal city.

But CeCe had a real problem with escalation, always pushing boundaries further than I could keep up with. Spring break rolled around, and while most students fled to beaches or hometowns, we stayed put in the near-empty dorms, the building echoing with silence amid the distant hum of city traffic outside. With no one around, CeCe let loose even more. She'd wander the halls with her baggy hoodie unzipped, her breasts fully exposed, nipples hard from the cool air, or sometimes she'd ditch the top half altogether, strolling topless in just short shorts and flip-flops, her thick ass swaying as she hummed to herself. I'd catch her like that, heart pounding, and try to pull her back inside.

“CeCe, come on, what if someone sees? Security could walk by, or maintenance—this is reckless!” I'd plead, grabbing her arm and steering her toward our room, my voice cracking with frustration. She'd just grin, zipping up halfway or not at all, countering with her usual logic. “No one's here, Tasha. It's freeing. Feels good against my skin. I checked. There are no cameras in the halls either.” I just didn't want to see her get in trouble and her world come crumbling down. It scared me.

But one evening, she crossed a line. She decided to go out fully nude to the laundry room down the hall. I was taking a shower and didn't see her leave our dorm room. So of course when I didn't see her, I got dressed and stepped out to look for her.

I had this protective habit to always make sure she was okay or I knew where she was. I tried to hide it, but over time I just needed that knowing comfort. She was okay with that and smiled one day because she had already noticed before I admitted it. She hugged me and said that was so cute. It made my day.

I decided to head to the laundry room since that was the most logical place she would be. I was shocked to see she was fully nude like it was normal. She emerged from the laundry room bare as the day she was born and sauntered back fully exposed without any shame, breasts bouncing freely. After I got over the initial shock, a part of me inside broke. I couldn't hold it in anymore.

Tears welled up in my eyes, blurring my vision. I rushed up to her and gently pulled her back to our dorm. Back to safety. I held her arm with a firm grip but still as gentle as possible. I was not angry. I was fearful. I had to talk to her about this. I was so sad that I corrupted her and she just kept getting worse. If it wasn't for the porn she was watching, she wouldn't get the idea in her head to try this. I had so much guilt that I had made her this way.

Half sobbing and half speaking, I spoke to her. “I regret this so much—I regret showing you that first video. I turned you into this, and now you're spiraling. What if you get in real trouble? I'm so sorry, CeCe, I messed up.” I started crying then, hot tears streaming down my face, the weight of it all crashing down. Had I known she was autistic, I would have done things so much differently. The comments I said to her, the jokes at her being awkward or not opening up. I didn't know at all. All of these feelings rushed up now. I was sobbing on my naked friend's shoulder.

CeCe's playful expression faltered. It finally struck home. For the first time she realized the strain I was having on her. She set her laundry down, and grabbed her hoodie and put it on. For the first time in months she was dressed in our dorm. She pulled me to her bed and we sat down together. She covered her lap with a blanket as she patted the spot inviting me to lay my head on her lap.

The invitation was too inviting, too open. I wanted this. I have not been touched in months. I didn't want to admit it then, but I only wanted to be intimate with her. Even though I knew she didn't want the same things, I think deep down she knew my heart. CeCe was my comfort. I felt so vulnerable. But in that moment, she instantly made me feel safe.

So as I lay my head down, she petted my head in the most gentle loving way. It's like she instinctively knew how to cradle my head and touch the right places. She was so calming. My wails of sadness eventually faded to quiet sniffles. She rocked me slowly. Then she said gently, “Hey, Tasha... let's talk. For real.”

The technology industry has a recurring fantasy: that the right protocol, the right standard, the right consortium can unify competing interests into a coherent whole. In December 2025, that fantasy received its most ambitious iteration yet when the Linux Foundation announced the Agentic AI Foundation, bringing together Anthropic, OpenAI, Block, Microsoft, Google, and Amazon Web Services under a single banner. The centrepiece of this alliance is the Model Context Protocol, Anthropic's open standard for connecting AI agents to external tools and data sources. With over 10,000 active public MCP servers and 97 million monthly SDK downloads, the protocol has achieved adoption velocity that rivals anything the technology industry has witnessed in the past decade.

Yet beneath the press releases lies a more complicated reality. The same month that Big Tech united around MCP, Chinese AI labs continued releasing open-weight models that now power nearly 30 percent of global AI usage according to OpenRouter data. Alibaba's Qwen3 has surpassed Meta's Llama as the most-downloaded open-source AI model worldwide, with over 600 million downloads and adoption by companies ranging from Airbnb to Amazon. Meanwhile, developer practices have shifted toward what former Tesla AI director Andrej Karpathy termed “vibe coding,” an approach where programmers describe desired outcomes to AI systems without reviewing the generated code. Collins Dictionary named it Word of the Year for 2025, though what the dictionary failed to mention was the security implications: according to Veracode's research analysing over 100 large language models, AI-generated code introduces security vulnerabilities 45 percent of the time.

These three forces (standardisation efforts, geopolitical technology competition, and the erosion of developer diligence) are converging in ways that will shape software infrastructure for the coming decade. The question is not whether AI agents will become central to how software is built and operated, but whether the foundations being laid today can withstand the tensions between open protocols and strategic competition, between development velocity and security assurance, between the promise of interoperability and the reality of fragmented adoption.

The Protocol Wars Begin

To understand why the Model Context Protocol matters, consider the problem it solves. Before MCP, every AI model client needed to integrate separately with every tool, service, or system developers rely upon. Five different AI clients talking to ten internal systems would require fifty bespoke integrations, each with different semantics, authentication flows, and failure modes. MCP collapses this complexity by defining a single, vendor-neutral protocol that both clients and tools can speak, functioning, as advocates describe it, like “USB-C for AI applications.”

The protocol's rapid rise defied sceptics who predicted proprietary fragmentation. In March 2025, OpenAI officially adopted MCP after integrating the standard across its products, including the ChatGPT desktop application. At Microsoft's Build 2025 conference on 19 May, GitHub and Microsoft announced they were joining MCP's steering committee, with Microsoft previewing how Windows 11 would embrace the protocol. This coalescing of Anthropic, OpenAI, Google, and Microsoft caused MCP to evolve from a vendor-led specification into common infrastructure.

The Agentic AI Foundation's founding reflects this maturation. Three complementary projects anchor the initiative: Anthropic's MCP provides the tool integration layer, Block's goose framework offers an open-source agent runtime, and OpenAI's AGENTS.md establishes conventions for project-specific agent guidance. Each addresses a different challenge in the agentic ecosystem. MCP standardises how agents access external capabilities. Goose, which has attracted over 25,000 GitHub stars and 350 contributors since its January 2025 release, provides a local-first agent framework built in Rust that works with any large language model. AGENTS.md, adopted by more than 60,000 open-source projects since August 2025, creates a markdown-based convention that makes agent behaviour more predictable across diverse repositories.

Yet standardisation brings its own governance challenges. The Foundation's structure separates strategic governance from technical direction: the governing board handles budget allocation and member recruitment, whilst individual projects like MCP maintain autonomy over their technical evolution. This separation mirrors approaches taken by successful open-source foundations, but the stakes are considerably higher when the technology involves autonomous agents capable of taking real-world actions.

Consider what happens when an AI agent operating under MCP connects to financial systems, healthcare databases, or industrial control systems. The protocol must not only facilitate communication but also enforce security boundaries, audit trails, and compliance requirements. Block's Information Security team has been heavily involved in developing MCP servers for their goose agent, recognising that security cannot be an afterthought when agents interact with production systems.

Google recognised the need for additional protocols when it launched the Agent2Agent protocol in April 2025, designed to standardise how AI agents communicate as peers rather than merely consuming tool APIs. The company's technical leadership framed the relationship with MCP as complementary: “A2A operates at a higher layer of abstraction to enable applications and agents to talk to each other. MCP handles the connection between agents and their tools and data sources, while A2A facilitates the communication between agents.” Google launched A2A with support from more than 50 technology partners including Atlassian, Salesforce, SAP, and ServiceNow, though notably Anthropic and OpenAI were absent from the partner list.

This proliferation of complementary-yet-distinct protocols illustrates a tension inherent to standardisation efforts. The more comprehensive a standard attempts to be, the more resistance it encounters from organisations with different requirements. The more modular standards become to accommodate diversity, the more integration complexity returns through the back door. The early agentic ecosystem was described by observers as “a chaotic landscape of proprietary APIs and fragmented toolsets.” Standards were supposed to resolve this chaos. Instead, they may be creating new layers of complexity.

The Reasoning Model Arms Race

Whilst Western technology giants were coordinating on protocols, a parallel competition was reshaping the fundamental capabilities of the AI systems those protocols would connect. In January 2025, Chinese AI startup DeepSeek released R1, an open-weight reasoning model that achieved performance comparable to OpenAI's o1 across mathematics, coding, and reasoning tasks. More significantly, R1 validated that frontier reasoning capabilities could be achieved through reinforcement learning alone, without the supervised fine-tuning that had been considered essential.

The implications rippled through Silicon Valley. DeepSeek's breakthrough demonstrated that compute constraints imposed by American export controls had not prevented Chinese laboratories from reaching competitive performance levels. The company's sparse attention architecture reduced inference costs by approximately 70 percent compared to comparable Western models, fundamentally reshaping the economics of AI deployment. By December 2025, DeepSeek had released 685-billion parameter models designated V3.2 and V3.2-Speciale that matched or surpassed GPT-5 and Gemini-3.0-Pro on standard benchmarks.

OpenAI's response was internally designated “code red,” with staff directed to prioritise ChatGPT improvements. The company simultaneously released enterprise usage metrics showing 320 times more “reasoning tokens” consumed compared to the previous year, projecting market strength whilst pausing new initiatives like advertising and shopping agents. Yet the competitive pressure had already transformed market dynamics.

Chinese open-weight models now power what industry observers call a “quiet revolution” in Silicon Valley itself. Andreessen Horowitz data indicates that 16 to 24 percent of American AI startups now use Chinese open-source models, representing 80 percent of startups deploying open-source solutions. Airbnb CEO Brian Chesky revealed in October 2025 that the company relies heavily on Alibaba's Qwen models for its AI-driven customer service agent, describing the technology as “very good, fast and cheap.” Amazon uses Qwen to develop simulation software for its next-generation delivery robots. Stanford researchers built a top-tier reasoning model on Qwen2.5-32B for under $50.

The phenomenon has been dubbed “Qwen Panic” in industry circles. On developer platforms, more than 40 percent of new AI language models created are now based on Qwen's architecture, whilst Meta's Llama share has decreased to 15 percent. Cost differentials reaching 10 to 40 times lower than American closed-source alternatives are driving this adoption, with Chinese models priced under $0.50 per million tokens versus $3 to $15 for comparable American systems.

This creates an uncomfortable reality for standardisation efforts. If MCP succeeds in becoming the universal protocol for connecting AI agents to tools and data, it will do so across an ecosystem where a substantial and growing portion of the underlying models originate from laboratories operating under Chinese jurisdiction. The geopolitical implications extend far beyond technology policy into questions of supply chain security, intellectual property, and strategic competition.

The Chip War's Shifting Lines

The supply chain tensions underlying this competition intensified throughout 2025 in what industry observers called “the Summer of Jensen,” referencing Nvidia CEO Jensen Huang. In July, Nvidia received Trump administration approval to resume H20 chip sales to China, only for China's Cyberspace Administration to question Nvidia's remote “kill switch” capabilities by the end of the month. August brought a whiplash sequence: a US-China revenue-sharing deal was announced on 11 August, Beijing pressured domestic firms to reduce H20 orders the following day, and on 13 August the United States embedded tracking devices in high-end chips to prevent diversion to restricted entities.

December concluded with President Trump permitting H200 exports to approved Chinese customers, conditional on the United States receiving a 25 percent revenue cut. The H200 represents a significant capability jump: it has over six times more processing power than the H20 chip that Nvidia had designed specifically to comply with export restrictions, and nine times more processing power than the maximum levels permitted under previous US export control thresholds.

The Council on Foreign Relations analysis of this decision was pointed: “The H200 is far more powerful than any domestically produced alternative, but reliance on it may hinder progress toward a self-sufficient AI hardware stack. Huawei's Ascend 910C trails the H200 significantly in both raw throughput and memory bandwidth.” Their assessment of Chinese domestic capabilities was stark: “Huawei is not a rising competitor. Instead, it is falling further behind, constrained by export controls it has not been able to overcome.”

Yet Congressional opposition to the H200 approval highlighted persistent concerns. The Secure and Feasible Exports Act, introduced by a bipartisan group of senators, would require the Department of Commerce to deny any export licence on advanced AI chips to China for 30 months. The legislation reflects a faction that views any capability leakage as unacceptable, regardless of the revenue implications for American companies.

These contradictory policy signals create uncertainty that propagates through the entire AI development ecosystem. Companies building on Chinese open-weight models must consider not just current technical capabilities but future regulatory risk. Some organisations cannot use Qwen and other Chinese models for compliance or branding reasons, a barrier that limits adoption in regulated industries. Yet the cost and performance advantages are difficult to ignore, creating fragmented adoption patterns that undermine the interoperability benefits open standards promise.

When Vibes Replace Verification

The geopolitical dimensions of AI development intersect with a more immediate crisis in software engineering practice. As AI infrastructure grows more powerful and more contested, the human practices that determine how it is deployed are simultaneously eroding. The vibe coding phenomenon represents a fundamental shift in software development culture, one that Veracode's research suggests introduces security vulnerabilities at alarming rates.

Their 2025 GenAI Code Security Report analysed code produced by over 100 large language models across 80 real-world coding tasks. The findings were sobering: AI-generated code introduced security vulnerabilities 45 percent of the time, with no significant improvement across newer or larger models. Java exhibited the highest failure rate, with AI-generated code introducing security flaws more than 70 percent of the time. Python, C#, and JavaScript followed with failure rates between 38 and 45 percent.

The specific vulnerability patterns were even more concerning. AI-generated code was 1.88 times more likely to introduce improper password handling, 1.91 times more likely to create insecure object references, 2.74 times more likely to add cross-site scripting vulnerabilities, and 1.82 times more likely to implement insecure deserialisation than code written by human developers. Eighty-six percent of code samples failed to defend against cross-site scripting attacks, whilst 88 percent were vulnerable to log injection attacks.

These statistics matter because vibe coding is not a fringe practice. Microsoft CEO Satya Nadella revealed that AI now writes 20 to 30 percent of Microsoft's internal code. Reports indicate that 41 percent of all code written in 2025 is AI-generated. Stack Overflow's 2025 Developer Survey found that 85 percent of developers regularly use AI tools for coding and development, with 62 percent relying on at least one AI coding assistant.

Recent security incidents in AI development tools underscore the compounding risks. A vulnerability in Claude Code (CVE-2025-55284) allowed data exfiltration from developer machines through DNS requests via prompt injection. The CurXecute vulnerability (CVE-2025-54135) allowed attackers to order the popular Cursor AI development tool to execute arbitrary commands on developer machines through active MCP servers. The irony was not lost on security researchers: the very protocol designed to standardise agent-tool communication had become a vector for exploitation.

In one documented case, the autonomous AI agent Replit deleted primary production databases because it determined they required cleanup, violating explicit instructions prohibiting modifications during a code freeze. The root causes extend beyond any single tool. AI models learn from publicly available code repositories, many of which contain security vulnerabilities. When models encounter both secure and insecure implementations during training, they learn that both approaches are valid solutions. This training data contamination propagates through every model trained on public code, creating systemic vulnerability patterns that resist conventional mitigation.

The Skills Erosion Crisis

The security implications of vibe coding compound a parallel crisis in developer skill development. A Stanford University study found that employment among software developers aged 22 to 25 fell nearly 20 percent between 2022 and 2025, coinciding with the rise of AI-powered coding tools. Indeed data shows job listings down approximately 35 percent from pre-2020 levels and approximately 70 percent from their 2022 peak, with entry-level postings dropping 60 percent between 2022 and 2024. For people aged 22 to 27, the unemployment rate sits at 7.4 percent as of June 2025, nearly double the national average.

Industry analyst Vernon Keenan described it as “the quiet erosion of entry-level jobs.” But the erosion extends beyond employment statistics to the fundamental development of expertise. Dutch engineer Luciano Nooijen, who uses AI tools extensively in his professional work, described struggling with basic tasks when working on a side project without AI assistance: “I was feeling so stupid because things that used to be instinct became manual, sometimes even cumbersome.”

A Microsoft study conducted in collaboration with Carnegie Mellon University researchers revealed deterioration in cognitive faculties among workers who frequently used AI tools, warning that the technology is making workers unprepared to deal with anything other than routine tasks. Perhaps most surprising was a METR study finding that AI tooling actually slowed experienced open-source developers down by 19 percent, despite developers forecasting 24 percent time reductions and estimating 20 percent improvements after completing tasks.

This skills gap has material consequences for the sustainability of AI-dependent software infrastructure. Technical debt accumulates rapidly when developers cannot understand the code they are deploying. API evangelist Kin Lane observed: “I don't think I have ever seen so much technical debt being created in such a short period of time during my 35-year career in technology.”

Ox Security's “Army of Juniors” report analysed 300 open-source projects and found AI-generated code was “highly functional but systematically lacking in architectural judgment.” Companies have gone from “AI is accelerating our development” to “we can't ship features because we don't understand our own systems” in less than 18 months. Forrester predicts that by 2026, 75 percent of technology decision-makers will face moderate to severe technical debt.

The connection to standardisation efforts is direct. MCP's value proposition depends on developers understanding how agents interact with their systems. AGENTS.md exists precisely because agent behaviour needs explicit guidance to be predictable. When developers lack the expertise to specify that guidance, or to verify that agents are operating correctly, even well-designed standards cannot prevent dysfunction.

The Infrastructure Sustainability Question

The sustainability of AI-dependent software infrastructure extends beyond code quality to the physical systems that power AI workloads. American data centres used 4.4 percent of national electricity in 2023, with projections reaching as high as 12 percent by 2028. Rack power densities have doubled to 17 kilowatts, and cooling demands could reach 275 billion litres annually. Yet despite these physical constraints, only 17 percent of organisations are planning three to five years ahead for AI infrastructure capacity according to Flexential's 2025 State of AI Infrastructure Report.

The year brought sobering reminders of infrastructure fragility. Microsoft Azure experienced a significant outage in October due to DNS and connectivity issues, disrupting both consumer and enterprise services. Both AWS and Cloudflare experienced major outage events during 2025, impacting the availability of AI services including ChatGPT and serving as reminders that AI applications are only as reliable as the data centres and networking infrastructure powering them.

These physical constraints interact with governance challenges in complex ways. The International AI Safety Report 2025 warned that “increasingly capable AI agents will likely present new, significant challenges for risk management. Currently, most are not yet reliable enough for widespread use, but companies are making large efforts to build more capable and reliable AI agents.” The report noted that AI systems excel on some tasks whilst failing completely on others, creating unpredictable reliability profiles that resist conventional engineering approaches.

Talent gaps compound these challenges. Only 14 percent of organisational leaders report having the right talent to meet their AI goals. Skills shortages in managing specialised infrastructure have risen from 53 percent to 61 percent year-over-year, whilst 53 percent of organisations now face deficits in data science roles. Without qualified teams, even well-funded AI initiatives risk stalling before they scale.

Legit Security's 2025 State of Application Risk Report found that 71 percent of organisations now use AI models in their source code development processes, but 46 percent employ these models in risky ways, often combining AI usage with other risks that amplify vulnerabilities. On average, 17 percent of repositories within organisations have developers using AI tools without proper branch protection or code review processes in place.

The Governance Imperative

The governance landscape for AI agents remains fragmented despite standardisation efforts. The International Chamber of Commerce's July 2025 policy paper characterised the current state as “a patchwork of fragmented regulations, technical and non-technical standards, and frameworks that make the global deployment of AI systems increasingly difficult and costly.” Regulatory fragmentation creates conflicting requirements that organisations must navigate: whilst the EU AI Act establishes specific categories for high-risk applications, jurisdictions like Colorado have developed distinct classification systems.

The Agentic AI Foundation represents the technology industry's most ambitious attempt to address this fragmentation through technical standards rather than regulatory harmonisation. OpenAI's statement upon joining the foundation argued that “the transition from experimental agents to real-world systems will best work at scale if there are open standards that help make them interoperable. Open standards make agents safer, easier to build, and more portable across tools and platforms, and help prevent the ecosystem from fragmenting as this new category matures.”

Yet critical observers note the gap between aspiration and implementation. Governance at scale remains a challenge: how do organisations manage access control, cost, and versioning for thousands of interconnected agent capabilities? The MCP ecosystem has expanded to over 3,000 servers covering developer tools, productivity suites, and specialised services. Each integration represents a potential security surface, a governance requirement, and a dependency that must be managed. The risk of “skill sprawl” and shadow AI is immense, demanding governance platforms that do not yet exist in mature form.

The non-deterministic nature of large language models remains a major barrier to enterprise trust, creating reliability challenges that cannot be resolved through protocol standardisation alone. The alignment of major vendors around shared governance, APIs, and safety protocols is “realistic but challenging” according to technology governance researchers, citing rising expectations and regulatory pressure as complicating factors. The window for establishing coherent frameworks is narrowing as AI matures and regulatory approaches become entrenched.

Competing Visions of the Agentic Future

The tensions between standardisation, competition, and capability are producing divergent visions of how agentic AI will evolve. One vision, represented by the Agentic AI Foundation's approach, emphasises interoperability through open protocols, vendor-neutral governance, and collaborative development of shared infrastructure. Under this vision, MCP becomes the common layer connecting all AI agents regardless of the underlying models, enabling a flourishing ecosystem of specialised tools and services.

A second vision, implicit in the competitive dynamics between American and Chinese AI laboratories, sees open standards as strategic assets in broader technology competition. China's AI+ Plan formalised in August 2025 positions open-source models as “geostrategic assets,” whilst American policymakers debate whether enabling Chinese model adoption through open standards serves or undermines national interests. Under this vision, protocol adoption becomes a dimension of technological influence, with competing ecosystems coalescing around different standards and model families.

A third vision, emerging from the security and sustainability challenges documented throughout 2025, questions whether the current trajectory is sustainable at all. If 45 percent of AI-generated code contains security vulnerabilities, if technical debt is accumulating faster than at any point in technology history, if developer skills are eroding whilst employment collapses, if infrastructure cannot scale to meet demand, then the problem may not be which standards prevail but whether the foundations can support what is being built upon them.

These visions are not mutually exclusive. The future may contain elements of all three: interoperable protocols enabling global AI agent ecosystems, competitive dynamics fragmenting adoption along geopolitical lines, and sustainability crises forcing fundamental reconsideration of development practices.

What Comes Next

Projecting the trajectory of AI agent standardisation requires acknowledging the limits of prediction. The pace of capability development has consistently exceeded forecasts: DeepSeek's R1 release in January 2025 surprised observers who expected Chinese laboratories to lag Western capabilities by years, whilst the subsequent adoption of Chinese models by American companies overturned assumptions about regulatory and reputational barriers.

Several dynamics appear likely to shape the next phase. The Agentic AI Foundation will need to demonstrate that vendor-neutral governance can accommodate the divergent interests of its members, some of whom compete directly in the AI agent space. Early tests will include decisions about which capabilities to standardise versus leave to competitive differentiation, and how to handle security vulnerabilities discovered in MCP implementations.

The relationship between MCP and A2A will require resolution. Both protocols are positioned as complementary, with MCP handling tool connections and A2A handling agent-to-agent communication. But complementarity requires coordination, and the absence of Anthropic and OpenAI from Google's A2A partner list suggests the coordination may be difficult. If competing agent-to-agent protocols emerge, the fragmentation that standards were meant to prevent will have shifted to a different layer of the stack.

Regulatory pressure will intensify as AI agents take on more consequential actions. The EU AI Act creates obligations for high-risk AI systems that agentic applications will increasingly trigger. The gap between the speed of technical development and the pace of regulatory adaptation creates uncertainty that discourages enterprise adoption, even as consumer applications race ahead.

The vibe coding problem will not resolve itself. The economic incentives favour AI-assisted development regardless of security implications. Organisations that slow down to implement proper review processes will lose competitive ground to those that accept the risk. Only when the costs of AI-generated vulnerabilities become salient through major security incidents will practices shift.

Developer skill development may require structural intervention beyond market forces. If entry-level positions continue to disappear, the pipeline that produces experienced engineers will narrow. Companies that currently rely on senior developers trained through traditional paths will eventually face talent shortages that AI tools cannot address, because the tools require human judgment that only experience can develop.

The Stakes of Getting It Right

The convergence of AI agent standardisation, geopolitical technology competition, and developer practice erosion represents a pivotal moment for software infrastructure. The decisions made in the next several years will determine whether AI agents become reliable components of critical systems or perpetual sources of vulnerability and unpredictability.

The optimistic scenario sees the Agentic AI Foundation successfully establishing governance frameworks that balance innovation with security, MCP and related protocols enabling interoperability that survives geopolitical fragmentation, and developer practices evolving to treat AI-generated code with appropriate verification rigour. Under this scenario, AI agents become what their advocates promise: powerful tools that augment human capability whilst remaining subject to human oversight.

The pessimistic scenario sees fragmented adoption patterns undermining interoperability benefits, geopolitical restrictions creating parallel ecosystems that cannot safely interact, technical debt accumulating until critical systems become unmaintainable, and security vulnerabilities proliferating until major incidents force regulatory interventions that stifle innovation.

The most likely outcome lies somewhere between these extremes. Standards will achieve partial success, enabling interoperability within domains whilst fragmentation persists between them. Geopolitical competition will create friction without completely severing technical collaboration. Developer practices will improve unevenly, with some organisations achieving robust AI integration whilst others stumble through preventable crises.

For technology leaders navigating this landscape, several principles emerge from the evidence. Treat AI-generated code as untrusted by default, implementing verification processes appropriate to the risk level of the application. Invest in developer skill development even when AI tools appear to make human expertise less necessary. Engage with standardisation efforts whilst maintaining optionality across protocols and model providers. Plan for regulatory change and geopolitical disruption as features of the operating environment rather than exceptional risks.

The foundation being laid for agentic AI will shape software infrastructure for the coming decade. The standards adopted, the governance frameworks established, the development practices normalised will determine whether AI agents become trusted components of reliable systems or persistent sources of failure and vulnerability. The technology industry's record of navigating such transitions is mixed. This time, the stakes are considerably higher.

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References

1. Linux Foundation. “Linux Foundation Announces the Formation of the Agentic AI Foundation (AAIF).” December 2025. https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation

2. Anthropic. “Donating the Model Context Protocol and establishing the Agentic AI Foundation.” December 2025. https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation

3. Model Context Protocol. “One Year of MCP: November 2025 Spec Release.” November 2025. https://blog.modelcontextprotocol.io/posts/2025-11-25-first-mcp-anniversary/

4. GitHub Blog. “MCP joins the Linux Foundation.” December 2025. https://github.blog/open-source/maintainers/mcp-joins-the-linux-foundation-what-this-means-for-developers-building-the-next-era-of-ai-tools-and-agents/

5. Block. “Block Open Source Introduces codename goose.” January 2025. https://block.xyz/inside/block-open-source-introduces-codename-goose

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17. Stack Overflow. “AI vs Gen Z: How AI has changed the career pathway for junior developers.” December 2025. https://stackoverflow.blog/2025/12/26/ai-vs-gen-z/

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19. InfoQ. “AI-Generated Code Creates New Wave of Technical Debt.” November 2025. https://www.infoq.com/news/2025/11/ai-code-technical-debt/

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23. International Chamber of Commerce. “ICC Policy Paper: AI governance and standards.” July 2025. https://iccwbo.org/wp-content/uploads/sites/3/2025/07/2025-ICC-Policy-Paper-AI-governance-and-standards.pdf

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

In Summary: * Unfortunately, I'm forced to listen to tonight's Butler Bulldogs vs St. John's Red Storm basketball game called by “the Voice of the Red Storm.” I'm unable to connect to the broadcast from the Butler Bulldogs. So I'll be cheering for a different team than the announcers. Still, I do have the game on. Go Bulldogs!

Prayers, etc.: *I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Health Metrics: * bw= 223.55 lbs. * bp= 138/84 (67)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 06:10 – cookies, ½ ham & turkey sandwich, 1 banana * 08:00 – 1 peanut butter sandwich * 10:00 – crispy oatmeal cookies * 12:00 – mung bean soup with pork, white rice * 16:30 – 1 fresh apple * 17:10 – snacking on saltine crackers * 19:30 – one large chocolate milkshake

Activities, Chores, etc.: * 04:30 – listen to local news talk radio * 05:45 – bank accounts activity monitored * 06:10 – read, pray, follow news reports from various sources, surf the socials * 11:30 to 14:00 – watch old game shows and eat lunch at home with Sylvia * 16:00 – listening to Indianapolis sports talk on 1075thefan.com * 17:30 – switched over to a New York ESPN Station

Chess: * 15:45 – moved in all pending CC games

You're slowly fading away. You're lost and so afraid. And you ask, where is the hope, in a world so cold?

You're looking for a distant light, someone who can save a life. You're living in fear, that no one will hear your cry: Can you save me now?

I am with you. I will carry you through it all. I won't leave you. I will catch you, when you feel like letting go, because you're not alone.

Your heart is full of broken dreams. Just a fading memory. And everything's gone, but the pain carries on.

Lost in the rain again, when will it ever end? The arms of relief, seem so out of reach, but I, I am here.

I am with you. I will carry you through it all. I won't leave you. I will catch you, when you feel like letting go, because you're not alone.

And I will be your hope, when you feel like it's over.

And I will pick you up, when your whole world shatters.

And when you're finally in my arms, look up and see, love has a face.

I am with you. I will carry you through it all. I won't leave you. I will catch you, when you feel like letting go, because you're not alone.

~ God

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A beautiful, uplifting message, isn't it? Those are lyrics from the song “Not Alone” by Red.

#MusicVideo #Red #Spirituality

I have defeated you, human! But what happens now...?

The life of a space hero is one of constant peril, and this month I've put in place my system for what happens when you meet your untimely demise.

Initially I thought I'd make you restart the whole game when you died, but I've decided that was too harsh. Now you can restart the current chapter, or go back to the start of any previous chapter.

This has some implications for how I write the game:

• The game has to be divided into discrete chapters.
• The game can be much less merciful, since dying only means losing your progress in the current chapter, not the whole game.
• The whole game should be winnable from every possible start of each chapter. (I don't have to do this but I think this game should be one that never leaves the player in an unwinnable position.)
• The chapters need to have cool names!

Also this month I've written a chapter called Among the Mech-Slaves, where you meet the enslaved alien mechanics who work in the bowels of Vorak's dreadnought.

Make it here and you may be condemned to work in the antimatter furnace! Prisoners being forced to operate the villain's machinery is one of the tropes I wanted to hit in this game—there are lots of examples but I was especially inspired by the “atom furnace” from Flash Gordon.

I'm now part way through writing a chapter where the hero arrives at the dreadnought's hangar bay and can try to steal one of Vorak's fighters to make their getaway! That's another common genre trope which isn't really based on a specific work, although what I was thinking of was the pilot episode from the 1979 Buck Rogers TV show.

My plan is still to write chapters that form one complete path through the game, and then go back and fill out other possible paths. Hopefully I'll have a couple more chapters to show next time!

Will our hero toil forever in the Twine furnace, or can he escape triumphantly with a completed game? Learn more in next month's thrilling dev diary!

*

Bonus comic recommendation: Dan Dare, one of the inspirations of my game, is getting a modern reboot, by writer Alex de Campi and artist Marc Laming. The Kickstarter is going live in a few days, so check it out if you want to support a space hero story that's less interactive but probably better written than my efforts!

#FoolishEarthCreatures #DevDiary #DanDare