GO HOOSIERS!

Coming as a surprise to no one at all (I hope) tonight I'll be tuned into the College Football National Championship Game as the Indiana University Hoosiers play the Miami Hurricanes. And yes, of course, I'll be cheering for IU.

And the adventure continues.

Lors de l'édition 2026 du CES de Las Vegas, The Verge a organisé un enregistrement en public de son podcast Decoder, invitant Min-Liang Tan, le PDG de Razer. Cet entretien a permis de détailler la nouvelle stratégie de l'entreprise, résolument tournée vers l'intelligence artificielle, malgré les controverses et les inquiétudes palpables au sein de la communauté des joueurs.

Si la devise de la marque a toujours été “For Gamers, By Gamers” (Pour les joueurs, par les joueurs), cette interview révèle un dirigeant qui semble non seulement déconnecté des attentes réelles de sa communauté, mais qui s'engage dans une fuite en avant technologique aux implications éthiques douteuses. Le point le plus alarmant est la légèreté avec laquelle il défend le “Projet Ava”, cet hologramme d'anime “waifu” destiné à trôner sur les bureaux. En choisissant de l’alimenter avec Grok (l'IA d'Elon Musk, actuellement embourbée dans des scandales de pornographie deepfake) Razer fait preuve d'un manque de discernement flagrant.

https://youtu.be/dJ-dIoTy6mo

Lorsque le journaliste Nilay Patel soulève les risques psychosociaux bien réels (attachement émotionnel, solitude, dérives), la réponse de Tan est désinvolte, voire méprisante. Il compare une intelligence artificielle générative capable de conversation complexe à un Tamagotchi. Il ignore donc délibérément une année entière de documentation sur les dangers de la dépendance aux chatbots. Prétendre se soucier de la sécurité tout en s'associant à l'IA la moins régulée du marché relève soit de l'incompétence, soit de l'hypocrisie.

Plus cynique encore est l'approche commerciale. Razer accepte des réservations payantes (20 $) pour ce projet Ava, alors même que le PDG admet ne pas connaître les spécifications finales, le modèle définitif, ni même la date de sortie. C'est la définition même du vaporware. Razer demande à ses fans de financer un concept ambigüe, transformant sa clientèle fidèle en bêta-testeurs payants pour une technologie dont il avoue lui-même ne pas savoir si elle sera “la pire idée possible”.

Le décalage est total. Alors que les sections commentaires des réseaux sociaux de Razer hurlent leur rejet de l'IA générative (le fameux “slop” ou contenu poubelle), Tan annonce un investissement massif de 600 millions de dollars dans ce domaine. Il tente de justifier cela par des outils d'aide aux développeurs, mais présente en parallèle des casques à caméras (Projet Motoko) dont l'utilité réelle (demander son chemin dans un aéroport à ChatGPT) semble dérisoire face à la complexité technique et au coût.

Enfin, il y a une ironie amère à l’entendre se plaindre de la hausse des prix de la RAM et des GPU qui rendent les ordinateurs portables Razer inabordables. Il déplore une situation (la bulle spéculative de l'IA) qu'il contribue activement à alimenter avec ses propres investissements et son battage médiatique au CES. Il semble avoir oublié que ses clients veulent du matériel performant et fiable, pas des abonnements mensuels pour discuter avec un hologramme dans un bocal. En poursuivant cette chimère de l'IA à marche forcée, la marque risque non seulement de diluer son identité, mais de s'aliéner définitivement la communauté qui a fait son succès.

Hey everyone,

I hope you’re all enjoying this holiday weekend! It’s Martin Luther King Jr. Day, and I’ve been thinking a lot about his incredible legacy. A while back, I had the chance to visit the King Center in Atlanta, and it really stayed with me.

Just a couple of blocks away from the main King Center, on a quiet, pretty residential street, sits Dr. King’s childhood home. It’s been beautifully preserved, so you really get a feel for what family life was like back then.

Dr. King used to talk about how, even as a kid, the view from the front stoop shaped him—the poor houses on one side, the wealthy ones on the other. It gave him an early sense that things needed to change.

Like most of us, I grew up watching those classic black-and-white clips of his “I Have a Dream” speech (and yes, I watched it again in my college U.S. History class). I still remember how big a deal it was in 1986 when MLK Day finally became a federal holiday—we even walked down the street as a family to mark the occasion.

The King Center itself is so moving. There’s this serene reflecting pool where Dr. King and Coretta Scott King are laid to rest, and along the sides are powerful quotes from his speeches, including his call to confront the three great evils: racism, poverty, and war.

Most people, when you ask what Dr. King stood for, will immediately say something about judging people “by the content of their character, not the color of their skin.” And that’s absolutely right. But in his later years, he was also passionately speaking out against poverty. He even talked about poor Black folks and poor white folks coming together to fight for better lives. In his final book, Where Do We Go From Here: Chaos or Community?, he wrote something that still hits hard today:

“A true revolution of values will soon look uneasily on the glaring contrast of poverty and wealth… Let us be those creative dissenters who will call our beloved nation to a higher destiny, to a new plateau of compassion, to a more noble expression of humanness.”

That was 58 years ago, and yet it feels like he could have written those words yesterday. So this year, on his birthday, I’ve been thinking: Are we any closer to that “beloved community” he dreamed of? And in our time—with AI changing jobs, economies, and lives so fast—how do we tackle poverty in a way that actually builds something better for everyone?

If you ever get the chance to visit the King Center in Atlanta, do it. It’s powerful, moving, and honestly kind of hopeful all at the same time.

Wishing you all a reflective and peaceful holiday. Love to you and yours.

I wasted away— and yet still I listened.

NARRATOR:

Look! Jehovah is emptying the land and making it desolate. He turns it upside down and scatters its inhabitants.

It will be the same for everyone: The people as well as the priest, The servant and his master, The servant and her mistress, The buyer and the seller, The lender and the borrower, The creditor and the debtor.

The land will be completely emptied; It will be completely plundered, For Jehovah has spoken this word.

The land mourns; it is wasting away. The productive land withers; it is fading away. The prominent people of the land wither.

The land has been polluted by its inhabitants, For they have bypassed the laws, Changed the regulation, And broken the lasting covenant.

That is why the curse devours the land, And those inhabiting it are held guilty. That is why the inhabitants of the land have dwindled, And very few men are left.

The new wine mourns, the vine withers, And all those cheerful at heart are sighing.

The joy of the tambourines has ceased; The noise of the revelers has ended; The happy sound of the harp has ceased.

They drink wine without song, And alcohol tastes bitter to those drinking it.

The deserted town is broken down; Every house is shut up so that no one can enter.

They cry out for wine in the streets. All rejoicing has disappeared; The joy of the land has gone.

The city is left in ruins; The gate has been crushed to a heap of rubble.

For this is how it will be in the land, among the peoples: As when an olive tree is beaten, Like the gleaning when the grape harvest comes to an end.

VOICES OF THE RIGHTEOUS:

They will raise their voice, They will shout joyfully. From the sea they will proclaim the majesty of Jehovah.

That is why they will glorify Jehovah in the region of light; In the islands of the sea they will glorify the name of Jehovah the God of Israel.

From the ends of the earth we hear songs: “Glory to the Righteous One!”

ISAIAH:

But I say: “I am wasting away, I am wasting away! Woe to me! The treacherous have acted treacherously; With treachery the treacherous have acted treacherously.”

Terror and pits and traps await you, inhabitant of the land.

Anyone fleeing from the sound of terror will fall into the pit, And anyone coming up from the pit will be caught in the trap. For the floodgates above will be opened, And the foundations of the land will quake.

The land has burst apart; The land has been shaken up; The land convulses violently.

The land staggers like a drunken man, And it sways back and forth like a hut in the wind. Its transgression weighs heavily on it, And it will fall, so that it will not rise up again.

In that day Jehovah will turn his attention to the army of the heights above And to the kings of the earth upon the earth.

And they will be gathered together Like prisoners gathered into a pit, And they will be shut up in the dungeon; After many days they will be given attention.

The full moon will be abashed, And the shining sun will be ashamed, For Jehovah of armies has become King in Mount Zion and in Jerusalem, Glorious before the elders of his people.

---

ISAIAH (PRAYER):

O Jehovah, you are my God. I exalt you, I praise your name, For you have done wonderful things, Things purposed from ancient times, In faithfulness, in trustworthiness.

For you have turned a city into a pile of stones, A fortified town into a crumbling ruin. The foreigner’s tower is a city no more; It will never be rebuilt.

That is why a strong people will glorify you; The city of tyrannical nations will fear you.

For you have become a stronghold to the lowly, A stronghold to the poor in his distress, A refuge from the rainstorm, And a shade from the heat. When the blast of the tyrants is like a rainstorm against a wall,

As the heat in a parched land, You subdue the uproar of strangers. Like heat that is subdued by the shadow of a cloud, So the song of the tyrants is silenced.

In this mountain Jehovah of armies will make for all the peoples A banquet of rich dishes, A banquet of fine wine, Of rich dishes filled with marrow, Of fine, filtered wine.

In this mountain he will do away with the shroud that is enveloping all the peoples And the covering that is woven over all the nations.

He will swallow up death forever, And the Sovereign Lord Jehovah will wipe away the tears from all faces. The reproach of his people he will take away from all the earth, For Jehovah himself has spoken it.

THE REDEEMED:

In that day they will say: “Look! This is our God! We have hoped in him, And he will save us. This is Jehovah! We have hoped in him. Let us be joyful and rejoice in the salvation by him.”

NARRATOR:

For the hand of Jehovah will rest on this mountain, And Moab will be trampled on in its place Like straw trampled into a pile of manure.

He will slap out his hands into it Like a swimmer slapping out his hands to swim, And he will bring down its haughtiness With the skillful movements of his hands.

And the fortified city, with your high walls of security, He will bring down; He will knock it down to the ground, to the very dust.

---

SONG OF JUDAH:

In that day this song will be sung in the land of Judah:

“We have a strong city. He makes salvation its walls and its ramparts.

Open up the gates so that the righteous nation may enter, A nation that is keeping faithful conduct.

You will safeguard those who fully lean on you; You will give them continuous peace, Because it is in you that they trust.

Trust in Jehovah forever, For Jah Jehovah is the eternal Rock.

For he has brought low those inhabiting the height, the lofty city. He brings it down, He brings it down to the earth; He casts it down to the dust.

The foot will trample it, The feet of the afflicted, the steps of the lowly.”

ISAIAH:

The path of the righteous one is upright. Because you are upright, You will smooth out the course of the righteous.

As we follow the path of your judgments, O Jehovah, Our hope is in you. We long for your name and your memorial.

In the night I long for you with my whole being, Yes, my spirit keeps looking for you; For when there are judgments from you for the earth, The inhabitants of the land learn about righteousness.

Even if the wicked is shown favor, He will not learn righteousness. Even in the land of uprightness he will act wickedly, And he will not see the majesty of Jehovah.

O Jehovah, your hand is raised, but they do not see it. They will see your zeal for your people and be put to shame. Yes, the fire for your adversaries will consume them.

O Jehovah, you will grant us peace, Because everything we have done You have accomplished for us.

O Jehovah our God, other masters besides you have ruled over us, But we make mention of your name alone.

They are dead; they will not live. Powerless in death, they will not rise up. For you have turned your attention to them To annihilate them and destroy all mention of them.

You have enlarged the nation, O Jehovah, You have enlarged the nation; You have glorified yourself. You have greatly extended all the borders of the land.

O Jehovah, during distress they turned to you; They poured out their prayer in a whisper when you disciplined them.

Just as a pregnant woman about to give birth Has labor pains and cries out in pain, So we have been because of you, O Jehovah.

We became pregnant, we had labor pains, But it is as if we had given birth to wind. We have not brought salvation to the land, And no one is born to inhabit the land.

JEHOVAH (PROMISE):

“Your dead will live. My corpses will rise up. Awake and shout joyfully, You residents in the dust! For your dew is as the dew of the morning, And the earth will let those powerless in death come to life.

Go, my people, enter your inner rooms, And shut your doors behind you. Hide yourself for a brief moment Until the wrath has passed by.

For look! Jehovah is coming from his place To call the inhabitants of the land to account for their error, And the land will expose her bloodshed And will no longer cover over her slain.”

---

NARRATOR:

In that day Jehovah, with his harsh and great and strong sword, Will turn his attention to Leviathan, the gliding serpent, To Leviathan, the twisting serpent, And he will kill the monster that is in the sea.

SONG OF THE VINEYARD:

In that day sing to her: “A vineyard of foaming wine! I, Jehovah, am safeguarding her. Every moment I water her. I safeguard her night and day, So that no one may harm her.

There is no wrath in me. Who will confront me with thornbushes and weeds in the battle? I will trample them and set them on fire all together.

Otherwise, let him hold fast to my stronghold. Let him make peace with me; Peace let him make with me.”

NARRATOR:

In the coming days Jacob will take root, Israel will blossom and sprout, And they will fill the land with produce.

Must he be struck with the stroke of the one striking him? Or must he be killed as with the slaughter of his slain?

With a startling cry you will contend with her when sending her away. He will expel her with his fierce blast in the day of the east wind.

So in this way the error of Jacob will be atoned for, And this will be the full fruitage when his sin is taken away: He will make all the stones of the altar Like chalkstones that have been pulverized, And no sacred poles or incense stands will be left.

For the fortified city will be deserted; The pastures will be forsaken and abandoned like a wilderness. There the calf will graze and lie down And will consume her branches.

When her twigs have dried up, Women will come and break them off, Making fires with them. For this people is without understanding. That is why their Maker will show them no mercy, And the One who formed them will show them no favor.

In that day Jehovah will beat out the fruit from the flowing stream of the River to the Wadi of Egypt, And you will be gathered up one after the other, O people of Israel.

In that day a great horn will be blown, And those who are perishing in the land of Assyria And those dispersed in the land of Egypt Will come and bow down to Jehovah in the holy mountain in Jerusalem.

---

#reading #bible #isaiah

So about that, it’s 3:45. I do think however one nice thought from today was that I should set my goal in league to be hitting a certain number of games, rather than a certain rank.

It's been a season of grief and loss.

But kind souls are holding space for me to soothe this pain, in community. Feelings of gratefulness and gladness wash over me.

Some nice things that have helped me to navigate surges of sadness and other emotions:

• gelato
• sharing my sorrows with a friend whom I feel emotionally safe with
• impromptu sing-along sessions with strangers at public pianos (featuring pop songs with sad tear-jerking lyrics)
• long solo walks, on both quiet nights and sun-drenched days
• Anne Lamott's (hilarious) book: “Operating Instructions: A journal of my son's first year”
• organic vegetables at dinner-time

I could go on and on, but you get the idea.

May I direct you now to Anne Lamott's Substack (e-newsletter). She's like an auntie who stays far away, lucid-eyed and pithily humorous when she comes over suddenly and gives you uncomfortable kisses that you never asked for, but which you appreciate anyway.

https://annelamott.substack.com/

#lunaticus

Alors que les relations diplomatiques entre les États-Unis et les européens montrent des signes de fragilité, une autre bataille se joue dans nos laboratoires de recherche. Face à des rivaux qui dominent jusqu’à présent l’ensemble de la chaîne de production de l’intelligence artificielle, nous cherchons désespérément à combler notre retard. Des processeurs aux centres de données, en passant par le développement des modèles, les géants d’outre-Atlantique comme Nvidia, Google ou OpenAI semblent indétrônables, captant la majeure partie des investissements et dopant l’économie américaine.

Cette hégémonie est telle que certains experts estiment la partie déjà perdue. Le sentiment qui prévaut parfois est que la dépendance technologique de l’Europe vis-à-vis des États-Unis est devenue inéluctable, reproduisant le schéma de domination observé dans le secteur du cloud. Pourtant, malgré les avertissements de responsables cybernétiques suggérant que notre continent a déjà « perdu Internet », les gouvernements britanniques et européens refusent de capituler. Inspirés par le succès inattendu du laboratoire chinois DeepSeek, qui a prouvé que la puissance de calcul brute ne fait pas tout, nos chercheurs misent désormais sur l'inventivité architecturale plutôt que sur la force brute pour revenir dans la course.

L'ouverture comme arme stratégique

Pour contrer les firmes américaines, souvent perçues comme des boîtes noires gardant jalousement leurs secrets de fabrication, nos laboratoires parient sur une philosophie radicalement différente avec l'open source. L'idée est simple mais puissante. En publiant leurs modèles et en permettant à quiconque de les modifier, les chercheurs européens espèrent créer un effet multiplicateur. Cette approche collaborative permet d'affiner les technologies bien plus rapidement que ne pourrait le faire une entreprise isolée.

Cette quête d'autonomie a pris une urgence nouvelle face au climat géopolitique actuel. L'attitude parfois hostile de l'administration Trump et les tensions commerciales croissantes ont transformé la question technologique en enjeu de sécurité nationale. L'IA est une infrastructure critique que l'Europe ne peut plus se permettre d'ignorer. La dépendance envers une puissance étrangère, même alliée, devient un risque lorsque les alliances traditionnelles vacillent.

Un contexte transatlantique sous tension

Les récentes frictions entre Bruxelles et Washington illustrent parfaitement cette vulnérabilité. Les désaccords sur la régulation des plateformes technologiques, notamment le bras de fer concernant le réseau social X d'Elon Musk, ont provoqué de vives réactions diplomatiques. Lorsque l'Europe tente d'imposer ses règles, les États-Unis crient à l'attaque contre leurs intérêts nationaux. Dans ce contexte, la dépendance européenne à l'IA américaine ressemble de plus en plus à un levier de pression potentiel dans les futures négociations commerciales.

Pour se prémunir, les nations européennes tentent de relocaliser la production d'IA par le biais de financements et de déréglementations ciblées. Des projets comme GPT-NL ou Apertus visent à créer des modèles performants dans nos langues natives. Le défi reste malgré tout immense. Tant que les outils américains comme ChatGPT surclasseront les alternatives locales, l'effet « winner-takes-all » continuera de creuser l'écart.

Définir la souveraineté pour mieux avancer

Le chemin vers l'indépendance numérique reste toutefois semé d'embûches et de débats internes. La définition même de la souveraineté divise: s'agit-il d'une autosuffisance totale ou simplement de la capacité à proposer des alternatives domestiques ? Certains plaident pour un protectionnisme assumé, incitant nos entreprises à acheter local pour stimuler la demande, à l'instar de la stratégie chinoise. D'autres, craignant d'isoler l'Europe, défendent l'ouverture des marchés et la liberté de choix.

Malgré ces divergences, un consensus émerge sur la faisabilité du rattrapage technologique. L'exemple de DeepSeek a brisé le dogme selon lequel seuls les plus gros clusters de GPU permettent l'innovation. Avec des projets ambitieux comme SOOFI, qui vise à lancer un modèle de langage généraliste compétitif dans l'année, l'Europe veut prouver qu'elle n'est pas condamnée à être un simple spectateur. Nous devons donc devenir le DeepSeek européen et reprendre le contrôle de notre destin numérique.

Freundinnen & Freunde der Weisheit, willkommen zur bereits dritten Ausgabe des wöchentlichen EpicMonday-Newsletters!

Produktivitätstools, Zeitmanagement-Methoden und Fokus-Techniken sollen helfen, den Arbeitstag effizient zu gestalten. Doch wer ausschliesslich auf Effizienz setzt, läuft Gefahr, kreative Potenziale zu blockieren. Denn gute Ideen entstehen selten im Modus maximaler Kontrolle. Psychologin Jennifer Haase verweist auf das sogenannte Cocktailparty-Phänomen: Unser Gehirn verarbeitet auch dann Informationen, wenn wir nicht bewusst darauf achten – entscheidend für das kreative Denken. Tools wie Trello oder Pomodoro sind nützlich für Routineaufgaben, können aber Innovation ersticken, wenn sie zu engmaschig eingesetzt werden.

Ein bewährtes Modell (entwickelt vom Sozialpsychologe Graham Wallas 1926 in seinem Buch The Art of Thought) für kreative Prozesse zeigt vier Phasen: Vorbereitung, Inkubation, Erleuchtung und Verifikation. Besonders die Inkubationsphase – also Zeiten der scheinbaren Untätigkeit – ist zentral für echte Durchbrüche. Spaziergänge, Gespräche, manuelle Tätigkeiten oder eine Stunde in der Kaffeeküche können genau jene geistige Beweglichkeit fördern, die effiziente Abläufe oft verhindern. Der Innovationsberater Tim Leberecht warnt deshalb vor einem „Kult der Effizienz“, der Unternehmen dazu verleitet, mit mittelmässigen Ergebnissen zufrieden zu sein – anstatt Raum für das Beste zu schaffen.

Auch Forschung zu Zeitmanagement liefert ein differenziertes Bild: Zwar steigert gutes Selbstmanagement das subjektive Wohlbefinden, nicht aber zwingend die Leistung. Wer zu viel plant, läuft Gefahr, sich in To-do-Listen zu verlieren und der „Planning Fallacy“ zu erliegen – der chronischen Unterschätzung von Aufwand. Die Empfehlung lautet daher: bewusst Pausen einbauen, Aufgaben hinterfragen und gelegentlich die Effizienzbrille absetzen. Denn Kreativität braucht nicht mehr Tools, sondern mehr Luft.

Denkanstoss zum Wochenbeginn

„Solange ein Mensch ein Buch schreibt, kann er nicht unglücklich sein.“ – Jean Paul (1763–1825)

ProductivityPorn-Tipp der Woche: Nein sagen

Du kannst nicht alles machen. Wenn Du ständig „Ja“ sagst, überlastest Du Dich selbst und riskierst, dass die Qualität Deiner Arbeit leidet. Lerne, freundlich, aber bestimmt abzulehnen, wenn etwas nicht in Deine Prioritäten passt.

Aus dem Archiv: Sinnvoll mit Prokrastination umgehen

Prokrastination ist ein komplexes Phänomen, das tief in unseren psychologischen Mustern verwurzelt ist. Indem man die zugrunde liegenden Ursachen versteht und gezielt Strategien anwendet, kann man lernen, mit Prokrastination umzugehen und ein produktiveres und erfüllteres Leben zu führen. Strukturiertes Prokrastinieren kann dabei eine hilfreiche Methode sein, um produktiv zu bleiben, auch wenn man Aufgaben aufschiebt.

weiterlesen …

Vielen Dank, dass Du Dir die Zeit genommen hast, diesen Newsletter zu lesen. Ich hoffe, die Inhalte konnten Dich inspirieren und Dir wertvolle Impulse für Dein (digitales) Leben geben. Bleib neugierig und hinterfrage, was Dir begegnet!

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EpicMind – Weisheiten für das digitale Leben „EpicMind“ (kurz für „Epicurean Mindset“) ist mein Blog und Newsletter, der sich den Themen Lernen, Produktivität, Selbstmanagement und Technologie widmet – alles gewürzt mit einer Prise Philosophie.

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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. Das Artikel-Bild wurde mit ChatGPT erstellt und anschliessend nachbearbeitet.

Topic #Newsletter

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I hear the way you talk The unkind words you use The cruel jokes and jabs Rationalizing while insulting

All aimed at yourself

“I'm meant to be alone” “It's fine, no one notices me” “Silly, why would anyone care about me?” “It's okay, I always mess everything up”

Why not stop?

I know kindness is hard Complementing yourself feels impossible Little by little, you can do it I believe in you

Start with small steps

End the cruelty Silence the harsh words Cease the insults Stop being so mean

Because you deserve better than that

#Poetry #SelfLove

Het is een treurig feit dat vooral Nederlandse, Griekse en Hongaarse joden relatief snel in de werkkampen van de Duitsers stierven.

De reden?

Ze konden minder goed de onmenselijke omstandigheden aan dan joden uit andere gebieden van Europa.

Andere joden waren al een zwaar leven, inclusief mentale en fysieke vernederingen, gewend.

Ook Belgische joden deden het relatief beter. Omdat de meesten al eerder in de twintigste eeuw gevlucht waren uit Polen, Rusland en Litouwen.

Dat is toch absurd om te lezen?

De bekendste strafpleiter van Nederland, Max Moszkowicz (1926–2022), kwam als tiener in een concentratiekamp terecht. Hij overleefde als een van de weinigen de Tweede Wereldoorlog.

Dat heeft veel met geluk te maken. Want ook Max heeft de dood af en toe in de ogen aangekeken daar.

Maar er speelden meer dingen mee die zijn overlevingskansen vergrootten.

Om te beginnen kwam Moszkowicz uit Limburg, maar hij was geboren in Duitsland. Zijn ouders waren al eerder gevlucht uit Polen.

Hij sprak daarom Duits en Pools, wat hem hielp in het kamp Birkenau (Auschwitz II).

Een ander punt dat hielp, was dat zijn vader ook in het kamp zat. Ze hadden enorm veel aan elkaar als mentale steun.

Helaas heeft zijn vader het einde van de Tweede Wereldoorlog niet gehaald. Hij overleed uiteindelijk in een ander kamp. Waarschijnlijk door uitputting.

Max kwam terecht bij het metselaarsschooltje in het kamp. Dat was een van de ‘betere’ plekken om te zijn. Het vernietigingskamp bleef maar groeien, dus er moest altijd wat gemetseld worden.

Maar denk niet dat er ook maar iets luxe aan was. Als je te laat kwam of niet oplette omdat je flauwviel van de honger, werd je afgeranseld. Ook vond er seksueel misbruik plaats.

Man. Man. Man.

De tranen sprongen zo vaak in mijn ogen bij het lezen van de barbaarsheid van de nazi’s. De willekeur van vernedering, vernietiging, afranseling en uithongering.

Lichamelijk was het al nauwelijks vol te houden: elke dag veertien uur lang zwaar werk doen en aan het einde van de dag een half broodje en wat gekookt water met aardappelschillen krijgen.

Maar mentaal was het ook nauwelijks vol te houden.

Toch deed Max iets briljants.

Als een van de weinigen in zijn blok van 800 man.

Zelfzorg.

Hij zorgde ervoor dat hij er elke dag goed verzorgd uitzag. Hij waste zijn gezicht in de sneeuw. Zorgde dat zijn kleding netjes bleef.

Dat gaf niet alleen een gevoel van zelfrespect en eigenwaarde. Het was ook een tegenbeeld aan de nazi’s, die hem als een minderwaardig, smerig en nutteloos beest zagen.

Het hielp hem niet alleen om respect af te dwingen, maar ook om een baantje in de bakkerij te krijgen. Een plek waar normaal gesproken geen jood welkom was. Maar hij werkte altijd hard en zag er schoon en netjes uit, en dat gaf blijkbaar genoeg vertrouwen bij de Duitsers.

In de bakkerij werken zorgde ervoor dat hij stukjes brood kon stelen om uit te delen of te ruilen met andere gevangenen.

Dit gaat heel gek klinken.

Maar als je gedachten niet zo lief voor je zijn, zorg dat je voor jezelf blijft zorgen.

Het is ook het eerste waar een psycholoog naar kijkt als je daar binnenkomt.

Heb je je haren gefatsoeneerd? Je nagels geknipt? Schone kleren aangedaan?

Omdat het een indicatie is van hoe je je mentaal voelt.

Hoe klote je ook voelt. Hoe zwaar je gedachten ook zijn.

• Vind de energie om op te staan.
• Om je tanden te poetsen.
• Te ontbijten.
• Een schone trui aan te trekken.
• Een korte wandeling te maken.
• Te reageren op appjes.
• Op tijd te slapen.

Zelfzorg geeft niet alleen structuur aan je dag terwijl er chaos in je hoofd is. Het zorgt ook voor het behoud van zelfrespect. Het is de laatste verdedigingslinie van je mentale welzijn.

Zorg goed voor jezelf.
Juist als het leven tegenzit.

Het is wat Max in het concentratiekamp staande hield. Ook na het overleven van de Holocaust bleef hij goed voor zichzelf zorgen. Max stond in de rechtszaal bekend om zijn goed verzorgde, vlekkeloze toga met sneeuwwitte bef.

Het was een handelsmerk van hem om de tegenstanders en de rechter te imponeren.

Dat is een andere waarheid.

Hoe je je kleedt, is hoe je je voelt.

Liefs,

tomson

Helaas kun je weinig meer veranderen aan wat er gebeurd is.

Ik denk ook niet dat je veel aan de gevoelens kunt doen die zo heftig opkomen. Het overvalt je. Of je voelt het al een tijdje borrelen. En dan is het er en dan stopt het ook weer even.

Het enige waar je invloed op hebt, is hoe je ermee omgaat. Maar ja.

Hoe?

Ik kan je vertellen wat mijn depressie me opleverde. Een heel slank lichaam. Dat ging vanzelf. Elke dag wandelen om zuurstof naar die sombere gedachten te brengen. Kilometers lang. Deze wandelgewoonte heeft mijn leven nooit meer verlaten.

Weet je wat een gebroken vriendschap en alle gevoelens die daarbij horen me opleverden? Een nieuw boek. Weliswaar mijn meest donkere. Maar het vulde mijn dagen om die zware gevoelens de baas te kunnen.

Wat mijn paniekstoornis me opleverde, was een gespierder lijf. Ik ging daar overigens niet voor. Ik zocht een manier om de controle over mijn lijf terug te krijgen. Want paniekaanvallen is allesbehalve controle hebben en dat maakt niet alleen machteloos. Het is alsof je gevangen zit in een zak met vlees en bloed en botten. In de sportschool aan gewichten hangen gaf me het idee dat ik mijn lichaam weer begon te voelen en te begrijpen en er invloed op had.

Juist als verdriet of zware gevoelens of extreme gedachten je dagen overnemen, start dan een project voor jezelf.

Een project waar je volledig controle over hebt, wat moeite kost. Wat je uiteindelijk iets zichtbaars oplevert en je een trots gevoel geeft. En oh ja. Dat wat je zo vaak als nodig kunt herhalen. En waar je niet afhankelijk bent van anderen.

Want dit wordt je obsessie het komende jaar.

Verdrietige periodes na een scheiding, herstel van een burn-out, de dood of een gebroken hart duren altijd langer dan je wil.

Dit is het moment om het sporten naar een serieus niveau te brengen. Ga drie keer in de week spieren kweken in de sportschool en wees de resterende dagen van de week bezig met voeding en herstelwandelingen. Of ga trainen voor een 15 kilometer run en schrijf je alvast in voor een event in de lente.

Of stof die naaimachine af en ga elke avond weer kleren maken die je altijd al hebt willen maken en werk aan iets groots.

Of sta elke ochtend vroeg op en schrijf minimaal een gedichtje voor je bundel. Of stort je op het lezen van ‘Oorlog en vrede’ van Tolstoj (1828–1910) of wat ik deed in de maanden nadat mijn huis in de brand was gevlogen: het lezen van ‘De gebroeders Karamazov’ van Dostojevski (1821–1881).

En juist de dagen waarop alles nog kutter voelt, beloon jezelf met een extra sessie van je project.

Zodat je over een jaar kunt zeggen: mijn hart was inderdaad flink gebroken. Ik was ook echt flink naar de tyfus. Maar het heeft me wel deze sixpack opgeleverd en trek dan je shirt omhoog en laat het glinsteren in het zonlicht en trek de meest arrogante kop die je in je hebt. Wat wilden ze doen dan?

Je uitlachen? Maak indruk op anderen. Maar vooral op jezelf.

Laat je begeleiden door misschien wel de beste tekstschrijver uit het euro-dance genre. Scooter met ‘Move Your Ass’.

Met de onvergetelijke wijsheid: ‘It's nice to be important. But it’s more important to be nice’.

Ik weet dat Tinder en dansen in de club en de joint aan je lippen en je hand in de chipszak ook manieren zijn om met gevoelens om te gaan.

Maar dat levert je nog meer problemen op over een jaar.

Ga voor ijver en discipline. Iets waar je trots op kunt zijn.

Is dat ook een vorm van verdoven? O, zeker. Maar ook:

Verdoven is niet het antwoord. Maar het helpt waarschijnlijk wel.

Om met schrijver Adriaan van Dis (1946) af te sluiten uit een Volkskrant-interview.

‘Ik geloof in een citadel van ijver en discipline. Dat harde werken is heel noodzakelijk voor mij. Dat heb ik mijn leven lang gedaan, ook als ik somber was. Ja, dat is een absolute vlucht. Maar ik geloof ook in de vlucht.’

Geloof in de vlucht.

Maar dan wel de vlucht die je echt een gevoel van eigenwaarde oplevert.

liefs,

tomson 19 januari 2026 Versie 1

Why do humans feel the need to indulge their senses in something like art? What is the purpose of art?

Our minds are double edged swords. It's all good when everything is hunky dory in our minds.

But when mental health diseases start cropping up, we need to either learn to control our minds through practices like meditation or keep our mind and bodies busy by find solace in art.

Art is supposed to help you express how you feel.

It doesn't matter if your painting or sketch will look good. It doesn't matter if your singing is melodious or dance is pleasing to watch.

What matters is how you feel when you express yourself through art. Immerse yourself in the process. Submit to the way it feels in that moment of expression. Lose yourself, relax and breathe.

My Rating: ⭐⭐⭐½ (3.5/5 stars)

Final Verdict

A solid, if unremarkable, entry in the Tron series. Jared Leto stands out, and the plot introduces a novel twist: the digital world invades ours, spotlighting AI. It's a fine way to kill almost 2 hours. However, it's not worth a theater visit. Watching it on Disney+ is your best bet.

Read more... Discuss...

The promotional materials are breathtaking. Artificial intelligence systems that can analyse medical scans with superhuman precision, autonomous vehicles that navigate complex urban environments, and vision-language models that understand images with the fluency of a seasoned art critic. The benchmark scores are equally impressive: 94% accuracy here, state-of-the-art performance there, human-level capabilities across dozens of standardised tests.

Then reality intrudes. A robotaxi in San Francisco fails to recognise a pedestrian trapped beneath its chassis and drags her twenty feet before stopping. An image recognition system confidently labels photographs of Black individuals as gorillas. A frontier AI model, asked to count the triangles in a simple geometric image, produces answers that would embarrass a primary school student. These are not edge cases or adversarial attacks designed to break the system. They represent the routine failure modes of technologies marketed as transformative advances in machine intelligence.

The disconnect between marketed performance and actual user experience has become one of the defining tensions of the artificial intelligence era. It raises uncomfortable questions about how we measure machine intelligence, what incentives shape the development and promotion of AI systems, and whether the public has been sold a vision of technological capability that fundamentally misrepresents what these systems can and cannot do. Understanding this gap requires examining the architecture of how AI competence is assessed, the economics that drive development priorities, and the cognitive science of what these systems actually understand about the world they purport to perceive.

The Benchmark Mirage

To understand why AI systems that excel on standardised tests can fail so spectacularly in practice, one must first examine how performance is measured. The Stanford AI Index Report 2025 documented a striking phenomenon: many benchmarks that researchers use to evaluate AI capabilities have become “saturated,” meaning systems score so high that the tests are no longer useful for distinguishing between models. This saturation has occurred across domains including general knowledge, reasoning about images, mathematics, and coding. The Visual Question Answering Challenge, for instance, now sees top-performing models achieving 84.3% accuracy, while the human baseline sits at approximately 80%.

The problem runs deeper than simple test exhaustion. Research conducted by MIT's Computer Science and Artificial Intelligence Laboratory revealed that “traditionally, object recognition datasets have been skewed towards less-complex images, a practice that has led to an inflation in model performance metrics, not truly reflective of a model's robustness or its ability to tackle complex visual tasks.” The researchers developed a new metric called “minimum viewing time” which quantifies the difficulty of recognising an image based on how long a person needs to view it before making a correct identification. When researchers at MIT developed ObjectNet, a dataset comprising images collected from real-life settings rather than curated repositories, they discovered substantial performance gaps between laboratory conditions and authentic deployment scenarios.

This discrepancy reflects a phenomenon that economists have studied for decades: Goodhart's Law, which states that when a measure becomes a target, it ceases to be a good measure. A detailed 68-page analysis from researchers at Cohere, Stanford, MIT, and the Allen Institute for AI documented systematic distortions in how companies approach AI evaluation. The researchers found that major technology firms including Meta, OpenAI, Google, and Amazon were able to “privately pit many model versions in the Arena and then only publish the best results.” This practice creates a misleading picture of consistent high performance rather than the variable and context-dependent capabilities that characterise real AI systems.

The problem of data contamination compounds these issues. When testing GPT-4 on benchmark problems from Codeforces in 2023, researchers found the model could regularly solve problems classified as easy, provided they had been added before September 2021. For problems added later, GPT-4 could not solve a single question correctly. The implication is stark: the model had memorised questions and answers from its training data rather than developing genuine problem-solving capabilities. As one research team observed, the “AI industry has turned benchmarks into targets, and now those benchmarks are failing us.”

The consequence of this gaming dynamic extends beyond misleading metrics. It shapes the entire trajectory of AI development, directing research effort toward whatever narrow capabilities will boost leaderboard positions rather than toward the robust, generalisable intelligence that practical applications require.

Counting Failures and Compositional Collapse

Perhaps nothing illustrates the gap between benchmark performance and real-world competence more clearly than the simple task of counting objects in an image. Research published in late 2024 introduced VLMCountBench, a benchmark testing vision-language models on counting tasks using only basic geometric shapes such as triangles and circles. The findings were revealing: while these sophisticated AI systems could count reliably when only one shape type was present, they exhibited substantial failures when multiple shape types were combined. This phenomenon, termed “compositional counting failure,” suggests that these systems lack the discrete object representations that make counting trivial for humans.

This limitation has significant implications for practical applications. A study using Bongard problems, visual puzzles that test pattern recognition and abstraction, found that humans achieved an 84% success rate on average, while the best-performing vision-language model, GPT-4o, managed only 17%. The researchers noted that “even elementary concepts that may seem trivial to humans, such as simple spirals, pose significant challenges” for these systems. They observed that “most models misinterpreted or failed to count correctly, suggesting challenges in AI's visual counting capabilities.”

Text-to-image generation systems demonstrate similar limitations. Research on the T2ICountBench benchmark revealed that “all state-of-the-art diffusion models fail to generate the correct number of objects, with accuracy dropping significantly as the number of objects increases.” When asked to generate an image of ten oranges, these systems frequently produce either substantially more or fewer items than requested. The failure is not occasional or marginal but systematic and predictable. As one research paper noted, “depicting a specific number of objects in the image with text conditioning often fails to capture the exact quantity of details.”

These counting failures point to a more fundamental issue in how current AI architectures process visual information. Unlike human cognition, which appears to involve discrete object representations and symbolic reasoning about quantities, large vision-language models operate on statistical patterns learned from training data. They can recognise that images containing many objects of a certain type tend to have particular visual characteristics, but they lack what researchers call robust “world models” that would allow them to track individual objects and their properties reliably.

The practical implications extend far beyond academic curiosity. Consider an AI system deployed to monitor inventory in a warehouse, assess damage after a natural disaster, or count cells in a medical sample. Systematic failures in numerical accuracy would render such applications unreliable at best and dangerous at worst.

The Architectural Divide

The question of whether these failures represent fundamental limitations of current AI architectures or merely training deficiencies remains actively debated. Gary Marcus, professor emeritus of psychology and neural science at New York University and author of the 2024 book “Taming Silicon Valley: How We Can Ensure That AI Works for Us,” has argued consistently that neural networks face inherent constraints in tasks requiring abstraction and symbolic reasoning.

Marcus has pointed to a problem he first demonstrated in 1998: neural networks trained on even numbers could generalise to some new even numbers, but when tested on odd numbers, they would systematically fail. He concluded that “these tools are good at interpolating functions, but not very good at extrapolating functions.” This distinction between interpolation within known patterns and extrapolation to genuinely novel situations lies at the heart of the benchmark-reality gap.

Marcus characterises current large language models as systems that “work at the extensional level, but they don't work at the intentional level. They are not getting the abstract meaning of anything.” The chess-playing failures of models like ChatGPT, which Marcus has documented attempting illegal moves such as having a Queen jump over a knight, illustrate how systems can “approximate the game of chess, but can't play it reliably because it never induces a proper world model of the board and the rules.” He has emphasised that these systems “still fail at abstraction, at reasoning, at keeping track of properties of individuals. I first wrote about hallucinations in 2001.”

Research on transformer architectures, the technical foundation underlying most modern AI systems, has identified specific limitations in spatial reasoning. A 2024 paper titled “On Limitations of the Transformer Architecture” identified “fundamental incompatibility with the Transformer architecture for certain problems, suggesting that some issues should not be expected to be solvable in practice indefinitely.” The researchers documented that “when prompts involve spatial information, transformer-based systems appear to have problems with composition.” Simple cases where temporal composition fails cause all state-of-the-art models to return incorrect answers.

The limitations extend to visual processing as well. Research has found that “ViT learns long-range dependencies via self-attention between image patches to understand global context, but the patch-based positional encoding mechanism may miss relevant local spatial information and usually cannot attain the performance of CNNs on small-scale datasets.” This architectural limitation has been highlighted particularly in radiology applications where critical findings are often minute and contained within small spatial locations.

Melanie Mitchell, professor at the Santa Fe Institute whose research focuses on conceptual abstraction and analogy-making in artificial intelligence, has offered a complementary perspective. Her recent work includes a 2025 paper titled “Do AI models perform human-like abstract reasoning across modalities?” which examines whether these systems engage in genuine reasoning or sophisticated pattern matching. Mitchell has argued that “there's a lot of evidence that LLMs aren't reasoning abstractly or robustly, and often over-rely on memorised patterns in their training data, leading to errors on 'out of distribution' problems.”

Mitchell identifies a crucial gap in current AI systems: the absence of “rich internal models of the world.” As she notes, “a tenet of modern cognitive science is that humans are not simply conditioned-reflex machines; instead, we have inside our heads abstracted models of the physical and social worlds that reflect the causes of events rather than merely correlations among them.” Current AI systems, despite their impressive performance on narrow benchmarks, appear to lack this causal understanding.

An alternative view holds that these limitations may be primarily a consequence of training data rather than architectural constraints. Some researchers hypothesise that “the limited spatial reasoning abilities of current VLMs is not due to a fundamental limitation of their architecture, but rather is a limitation in common datasets available at scale on which such models are trained.” This perspective suggests that co-training multimodal models on synthetic spatial data could potentially address current weaknesses. Additionally, researchers note that “VLMs' limited spatial reasoning capability may be due to the lack of 3D spatial knowledge in training data.”

When Failures Cause Harm

The gap between benchmark performance and real-world capability becomes consequential when AI systems are deployed in high-stakes domains. The case of autonomous vehicles provides particularly sobering examples. According to data compiled by researchers at Craft Law Firm, between 2021 and 2024, there were 3,979 incidents involving autonomous vehicles in the United States, resulting in 496 reported injuries and 83 fatalities. The Stanford AI Index Report 2025 noted that the AI Incidents Database recorded 233 incidents in 2024, a 56.4% increase compared to 2023, marking a record high.

In May 2025, Waymo recalled over 1,200 robotaxis following disclosure of a software flaw that made vehicles prone to colliding with certain stationary objects, specifically “thin or suspended barriers like chains, gates, and even utility poles.” These objects, which human drivers would navigate around without difficulty, apparently fell outside the patterns the perception system had learned to recognise. Investigation revealed failures in the system's ability to properly classify and respond to stationary objects under certain lighting and weather conditions. As of April 2024, Tesla's Autopilot system had been involved in at least 13 fatal crashes according to NHTSA data, with Tesla's Full Self-Driving system facing fresh regulatory scrutiny in January 2025.

The 2018 Uber fatal accident in Tempe, Arizona, illustrated similar limitations. The vehicle's sensors detected a pedestrian, but the AI system failed to classify her accurately as a human, leading to a fatal collision. The safety driver was distracted by a mobile device and did not intervene in time. As researchers have noted, “these incidents reveal a fundamental problem with current AI systems: they excel at pattern recognition in controlled environments but struggle with edge cases that human drivers handle instinctively.” The failure to accurately classify the pedestrian as a human being highlighted a critical weakness in object recognition capabilities, particularly in low-light conditions and complex environments.

A particularly disturbing incident involved General Motors' Cruise robotaxi in San Francisco, where the vehicle struck a pedestrian who had been thrown into its path by another vehicle, then dragged her twenty feet before stopping. The car's AI systems failed to recognise that a human being was trapped underneath the vehicle. When the system detected an “obstacle,” it continued to move, causing additional severe injuries.

These cases highlight how AI systems that perform admirably on standardised perception benchmarks can fail catastrophically when encountering situations not well-represented in their training data. The gap between laboratory performance and deployment reality is not merely academic; it translates directly into physical harm.

The Gorilla Problem That Never Went Away

One of the most persistent examples of AI visual recognition failure involves the 2015 incident in which Google Photos labelled photographs of Black individuals as “gorillas.” In that incident, a Black software developer tweeted that Google Photos had labelled images of him with a friend as “gorillas.” The incident exposed how image recognition algorithms trained on biased data can produce racist outputs. Google's response was revealing: rather than solving the underlying technical problem, the company blocked the words “gorilla,” “chimpanzee,” “monkey,” and related terms from the system entirely.

Nearly a decade later, that temporary fix remains in place. By censoring these searches, the service can no longer find primates such as “gorilla,” “chimp,” “chimpanzee,” or “monkey.” Despite enormous advances in AI technology since 2015, Google Photos still refuses to label images of gorillas. This represents a tacit acknowledgement that the fundamental problem has not been solved, only circumvented. The workaround creates a peculiar situation where one of the world's most advanced image recognition systems cannot identify one of the most recognisable animals on Earth. As one analysis noted, “Apple learned from Google's mistake and simply copied their fix.”

The underlying issue extends beyond a single company's product. Research has consistently documented that commercially available facial recognition technologies perform far worse on darker-skinned individuals, particularly women. Three commercially available systems made by Microsoft, IBM, and Megvii misidentified darker female faces nearly 35% of the time while achieving near-perfect accuracy (99%) on white men.

These biases have real consequences. Cases such as Ousmane Bah, a teenager wrongly accused of theft at an Apple Store because of faulty face recognition, and Amara K. Majeed, wrongly accused of participating in the 2019 Sri Lanka bombings after her face was misidentified, demonstrate how AI failures disproportionately harm marginalised communities. The technology industry's approach of deploying these systems despite known limitations and then addressing failures reactively raises serious questions about accountability and the distribution of risk.

The Marketing Reality Gap

The discrepancy between how AI capabilities are marketed and how they perform in practice reflects a broader tension in the technology industry. A global study led by Professor Nicole Gillespie at Melbourne Business School surveying over 48,000 people across 47 countries between November 2024 and January 2025 found that although 66% of respondents already use AI with some regularity, less than half (46%) are willing to trust it. Notably, this represents a decline in trust compared to surveys conducted prior to ChatGPT's release in 2022. People have become less trusting and more worried about AI as adoption has increased.

The study found that consumer distrust is growing significantly: 63% of consumers globally do not trust AI with their data, up from 44% in 2024. In the United Kingdom, the situation is even starker, with 76% of shoppers feeling uneasy about AI handling their information. Research from the Nuremberg Institute for Market Decisions showed that only 21% of respondents trust AI companies and their promises, and only 20% trust AI itself. These findings reveal “a notable gap between general awareness of AI in marketing and a deeper understanding or trust in its application.”

Emily Bender, professor of linguistics at the University of Washington and one of the authors of the influential 2021 “stochastic parrots” paper, has been a prominent voice challenging AI hype. Bender was recognised in TIME Magazine's first 100 Most Influential People in Artificial Intelligence and is the author of the upcoming book “The AI Con: How to Fight Big Tech's Hype and Create the Future We Want.” She has argued that “so much of what we read about language technology and other things that get called AI makes the technology sound magical. It makes it sound like it can do these impossible things, and that makes it that much easier for someone to sell a system that is supposedly objective but really just reproduces systems of oppression.”

The practical implications of this marketing-reality gap are significant. A McKinsey global survey in early 2024 found that 65% of respondents said their organisations use generative AI in some capacity, nearly double the share from ten months prior. However, despite widespread experimentation, “comprehensive integration of generative AI into core business operations remains limited.” A 2024 Deloitte study noted that “organisational change only happens so fast” despite rapid AI advances, meaning many companies are deliberately testing in limited areas before scaling up.

The gap is particularly striking in mental health applications. Despite claims that AI is replacing therapists, only 21% of the 41% of adults who sought mental health support in the past six months turned to AI, representing only 9% of the total population. The disconnect between hype and actual behaviour underscores how marketing narratives can diverge sharply from lived reality.

Hallucinations and Multimodal Failures

The problem of AI systems generating plausible but incorrect outputs, commonly termed “hallucinations,” extends beyond text into visual domains. Research published in 2024 documented that multimodal large language models “often generate outputs that are inconsistent with the visual content, a challenge known as hallucination, which poses substantial obstacles to their practical deployment and raises concerns regarding their reliability in real-world applications.”

Object hallucination represents a particularly problematic failure mode, occurring when models identify objects that do not exist in an image. Researchers have developed increasingly sophisticated benchmarks to evaluate these failures. ChartHal, a benchmark featuring a taxonomy of hallucination scenarios in chart understanding, demonstrated that “state-of-the-art LVLMs suffer from severe hallucinations” when interpreting visual data.

The VHTest benchmark introduced in 2024 comprises 1,200 diverse visual hallucination instances across eight modes. Medical imaging presents particular risks: the MediHall Score benchmark was developed specifically to assess hallucinations in medical contexts through a hierarchical scoring system. When AI systems hallucinate in clinical settings, the consequences can be life-threatening.

Mitigation efforts have shown some promise. One recent framework operating entirely with frozen, pretrained vision-language models and requiring no gradient updates “reduces hallucination rates by 9.8 percentage points compared to the baseline, while improving object existence accuracy by 4.7 points on adversarial splits.” Research by Yu et al. (2023) explored human error detection to mitigate hallucinations, successfully reducing them by 44.6% while maintaining competitive performance.

However, Gary Marcus has argued that there is “no principled solution to hallucinations in systems that traffic only in the statistics of language without explicit representation of facts and explicit tools to reason over those facts.” This perspective suggests that hallucinations are not bugs to be fixed but fundamental characteristics of current architectural approaches. He advocates for neurosymbolic AI, which would combine neural networks with symbolic AI, making an analogy to Daniel Kahneman's System One and System Two thinking.

The ARC Challenge and the Limits of Pattern Matching

Francois Chollet, the creator of Keras, an open-source deep learning library adopted by over 2.5 million developers, introduced the Abstraction and Reasoning Corpus (ARC) in 2019 as a benchmark designed to measure fluid intelligence rather than narrow task performance. ARC consists of 800 puzzle-like tasks designed as grid-based visual reasoning problems. These tasks, trivial for humans but challenging for machines, typically provide only a small number of example input-output pairs, usually around three.

What makes ARC distinctive is its focus on measuring the ability to “generalise from limited examples, interpret symbolic meaning, and flexibly apply rules in varying contexts.” Unlike benchmarks that can be saturated through extensive training on similar problems, ARC tests precisely the kind of novel reasoning that current AI systems struggle to perform. The benchmark “requires the test taker to deduce underlying rules through abstraction, inference, and prior knowledge rather than brute-force or extensive training.”

From its introduction in 2019 until late 2024, ARC remained essentially unsolved by AI systems, maintaining its reputation as one of the toughest benchmarks available for general intelligence. The ARC Prize competition, co-founded by Mike Knoop and Francois Chollet, saw 1,430 teams submit 17,789 entries in 2024. The state-of-the-art score on the ARC private evaluation set increased from 33% to 55.5% during the competition period, propelled by techniques including deep learning-guided program synthesis and test-time training. More than $125,000 in prizes were awarded across top papers and top scores.

While this represents meaningful progress, it remains far below human performance and the 85% threshold set for the $500,000 grand prize. The persistent difficulty of ARC highlights a crucial distinction: current AI systems excel at tasks that can be solved through pattern recognition and interpolation within training distributions but struggle with the kind of abstract reasoning that humans perform effortlessly.

Trust Erosion and the Normalisation of Failure

Research on human-AI interaction has documented asymmetric trust dynamics: building trust in AI takes more time compared to building trust in humans, but when AI encounters problems, trust loss occurs more rapidly. Studies have found that simpler tasks show greater degradation of trust following errors, suggesting that failures on tasks perceived as easy may be particularly damaging to user confidence.

This pattern reflects what researchers term “perfect automation schema,” the tendency for users to expect flawless performance from AI systems and interpret any deviation as evidence of fundamental inadequacy rather than normal performance variation. The marketing of AI as approaching or exceeding human capabilities may inadvertently amplify this effect by setting unrealistic expectations.

Research comparing early and late errors found that initial errors affect trust development more negatively than late ones in some studies, while others found that trust dropped most for late mistakes. The explanation may be that early mistakes allow people to adjust expectations over time, whereas trust damaged at a later stage proves more difficult to repair. Research has found that “explanations that combine causal attribution (explaining why the error occurred) with boundary specification (identifying system limitations) prove most effective for competence-based trust repair.”

The normalisation of AI failures presents a concerning trajectory. If users come to expect that AI systems will periodically produce nonsensical or harmful outputs, they may either develop excessive caution that undermines legitimate use cases or, alternatively, become desensitised to failures in ways that increase risk. Neither outcome serves the goal of beneficial AI deployment.

Measuring Intelligence or Measuring Training

The fundamental question underlying these failures concerns what benchmarks actually measure. The dramatic improvement in AI performance on new benchmarks shortly after their introduction, documented by the Stanford AI Index, suggests that current systems are exceptionally effective at optimising for whatever metrics researchers define. In 2023, AI systems could solve just 4.4% of coding problems on SWE-bench. By 2024, this figure had jumped to 71.7%. Performance on MMMU and GPQA saw gains of 18.8 and 48.9 percentage points respectively.

This pattern of rapid benchmark saturation has led some researchers to question whether improvements reflect genuine capability gains or increasingly sophisticated ways of matching test distributions. The Stanford report noted that despite strong benchmark performance, “AI models excel at tasks like International Mathematical Olympiad problems but still struggle with complex reasoning benchmarks like PlanBench. They often fail to reliably solve logic tasks even when provably correct solutions exist.”

The narrowing performance gaps between models further complicate the picture. According to the AI Index, the Elo score difference between the top and tenth-ranked model on the Chatbot Arena Leaderboard was 11.9% in 2023. By early 2025, this gap had narrowed to just 5.4%. Similarly, the difference between the top two models shrank from 4.9% in 2023 to just 0.7% in 2024.

The implications for AI development are significant. If benchmarks are increasingly unreliable guides to real-world performance, the incentive structure for AI research may be misaligned with the goal of building genuinely capable systems. Companies optimising for benchmark rankings may invest disproportionately in test-taking capabilities at the expense of robustness and reliability in deployment.

Francois Chollet has framed this concern explicitly, arguing that ARC-style tasks test “the ability to generalise from limited examples, interpret symbolic meaning, and flexibly apply rules in varying contexts” rather than the ability to recognise patterns encountered during training. The distinction matters profoundly for understanding what current AI systems can and cannot do.

Reshaping Expectations and Rebuilding Trust

Addressing the gap between marketed performance and actual capability will require changes at multiple levels. Researchers have begun developing dynamic benchmarks that are regularly updated to prevent data contamination. LiveBench, for example, is updated with new questions monthly, many from recently published sources, ensuring that performance cannot simply reflect memorisation of training data. This approach represents “a close cousin of the private benchmark” that keeps benchmarks fresh without worrying about contamination.

Greater transparency about the conditions under which AI systems perform well or poorly would help users develop appropriate expectations. OpenAI's documentation acknowledges that their models struggle with “tasks requiring precise spatial localisation, such as identifying chess positions” and “may generate incorrect descriptions or captions in certain scenarios.” Such candour, while not universal in the industry, represents a step toward more honest communication about system limitations.

The AI Incidents Database, maintained by the Partnership on AI, and the AIAAIC Repository provide systematic tracking of AI failures. The AIAAIC documented that in 2024, while incidents declined to 187 compared to the previous year, issues surged to 188, the highest number recorded, totalling 375 occurrences, ten times more than in 2016. Accuracy and reliability and safety topped the list of incident categories. OpenAI, Tesla, Google, and Meta account for the highest number of AI-related incidents in the repository.

Academic researchers have proposed that evaluation frameworks should move beyond narrow task performance to assess broader capabilities including robustness to distribution shift, calibration of confidence, and graceful degradation when facing unfamiliar inputs. Melanie Mitchell has argued that “AI systems ace benchmarks yet stumble in the real world, and it's time to rethink how we probe intelligence in machines.”

Mitchell maintains that “just scaling up these same kinds of models will not solve these problems. Some new approach has to be created, as there are basic capabilities that current architectures and training methods aren't going to overcome.” She notes that current models “are not learning from their mistakes in any long-term sense. They can't carry learning from one session to another. They also have no 'episodic memory,' unlike humans who learn from experiences, mistakes, and successes.”

The gap between benchmark performance and real-world capability is not simply a technical problem awaiting a technical solution. It reflects deeper questions about how we define and measure intelligence, what incentives shape technology development, and how honest we are prepared to be about the limitations of systems we deploy in consequential domains. The answers to these questions will shape not only the trajectory of AI development but also the degree to which public trust in these technologies can be maintained or rebuilt.

For now, the most prudent stance may be one of calibrated scepticism: appreciating what AI systems can genuinely accomplish while remaining clear-eyed about what they cannot. The benchmark scores may be impressive, but the measure of a technology's value lies not in how it performs in controlled conditions but in how it serves us in the messy, unpredictable complexity of actual use.

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References and Sources

• Stanford Human-Centered AI. (2025). “The 2025 AI Index Report: Technical Performance.” https://hai.stanford.edu/ai-index/2025-ai-index-report/technical-performance
• Stanford HAI. (2025). “AI Benchmarks Hit Saturation.” https://hai.stanford.edu/news/ai-benchmarks-hit-saturation
• MIT News. (2023). “Image recognition accuracy: An unseen challenge confounding today's AI.” https://news.mit.edu/2023/image-recognition-accuracy-minimum-viewing-time-metric-1215
• Collinear AI. (2024). “Gaming the System: Goodhart's Law Exemplified in AI Leaderboard Controversy.” https://blog.collinear.ai/p/gaming-the-system-goodharts-law-exemplified-in-ai-leaderboard-controversy
• Marcus, G. (2025). “Generative AI's crippling and widespread failure to induce robust models of the world.” https://garymarcus.substack.com/p/generative-ais-crippling-and-widespread
• Marcus, G. (2024). “Taming Silicon Valley: How We Can Ensure That AI Works for Us.” MIT Press.
• Mitchell, M. (2025). “AI's challenge of understanding the world.” Science. https://www.science.org/doi/10.1126/science.adm8175
• Mitchell, M. (2025). “The LLM Reasoning Debate Heats Up.” https://aiguide.substack.com/p/the-llm-reasoning-debate-heats-up
• AIAAIC Repository. (2025). “AI, algorithmic, and automation incidents.” https://www.aiaaic.org/aiaaic-repository
• AI Incident Database. (2025). Partnership on AI. https://incidentdatabase.ai/
• Craft Law Firm. (2024). “Data Analysis: Self-Driving Car Accidents [2019-2024].” https://www.craftlawfirm.com/autonomous-vehicle-accidents-2019-2024-crash-data/
• Responsible AI Labs. (2025). “AI Safety Incidents of 2024: Lessons from Real-World Failures.” https://responsibleailabs.ai/knowledge-hub/articles/ai-safety-incidents-2024
• AlgorithmWatch. (2020). “Google apologizes after its Vision AI produced racist results.” https://algorithmwatch.org/en/google-vision-racism/
• KPMG/University of Melbourne. (2025). “Trust, attitudes and use of artificial intelligence: A global study 2025.” https://kpmg.com/xx/en/our-insights/ai-and-technology/trust-attitudes-and-use-of-ai.html
• Nuremberg Institute for Market Decisions. (2024). “Consumer attitudes toward AI-generated marketing content.” https://www.nim.org/en/publications/detail/transparency-without-trust
• Bender, E. et al. (2021). “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” ACM Conference on Fairness, Accountability, and Transparency.
• Chollet, F. (2019). “On the Measure of Intelligence.” arXiv:1911.01547. https://arxiv.org/abs/1911.01547
• ARC Prize Foundation. (2024). “ARC Prize 2024: Technical Report.” https://arcprize.org/media/arc-prize-2024-technical-report.pdf
• The Debrief. (2024). “AI's Puzzle-Solving Limitations: Vision-Language Models Struggle with Human-Like Pattern Recognition.” https://thedebrief.org/29289-2-vision-language-models/
• arXiv. (2024). “On Limitations of the Transformer Architecture.” https://arxiv.org/html/2402.08164v1
• arXiv. (2024). “Hallucination of Multimodal Large Language Models: A Survey.” https://arxiv.org/abs/2404.18930
• arXiv. (2024). “Your Vision-Language Model Can't Even Count to 20.” https://arxiv.org/abs/2510.04401v1
• Frontiers in Psychology. (2024). “Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust.” https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1382693/full
• OpenAI. (2024). “GPT-4o System Card.” https://cdn.openai.com/gpt-4o-system-card.pdf
• Deloitte. (2024). “Earning trust as gen AI takes hold: 2024 Connected Consumer Survey.” https://www.deloitte.com/us/en/insights/industry/telecommunications/connectivity-mobile-trends-survey/2024.html
• IEEE Spectrum. (2025). “The State of AI 2025: 12 Eye-Opening Graphs.” https://spectrum.ieee.org/ai-index-2025

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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

Mark does not ease us into the story. He does not warm us up. He does not clear his throat and offer context the way other writers might. He opens the door and shoves us straight into movement. “The beginning of the gospel of Jesus Christ, the Son of God.” No genealogy. No childhood. No soft music. Just a declaration and then action. Mark writes like someone who knows time is short and truth matters more than polish. From the very first line of Mark 1, the reader is confronted with urgency. Something has begun, and it will not wait for anyone to feel ready.

That urgency is not accidental. It mirrors the way God so often moves in real life. God rarely announces Himself with long explanations. He breaks into ordinary routines, interrupts settled assumptions, and forces people to respond. Mark 1 is not just the beginning of a book; it is the beginning of disruption. It is the moment when heaven steps into history and refuses to be ignored. If you read it carefully, you realize that nothing in this chapter allows for passive faith. Everything demands movement, repentance, obedience, or resistance. There is no neutral ground.

Mark opens by grounding the moment in prophecy, reminding us that what is unfolding did not come out of nowhere. God had been speaking long before He started moving. Isaiah had declared that a messenger would prepare the way, that a voice would cry out in the wilderness. This matters because it shows us something essential about God’s character. God does not act randomly. He is intentional. Even when His timing feels sudden to us, it is rooted in long-established purpose. The problem is not that God moves without warning; it is that people stop listening.

John the Baptist appears in the wilderness, not in the centers of power, not in religious institutions, not in palaces or synagogues. The wilderness is uncomfortable. It is exposed. It is inconvenient. Yet that is where God chooses to begin. John’s message is simple and confrontational: repent. Not feel sorry. Not explain yourself. Not blame circumstances. Repent. Turn around. Change direction. Prepare yourself, because something holy is approaching.

People respond. Not because John is gentle, but because he is honest. He does not flatter them or promise comfort. He calls them out. He tells them they are not ready, and somehow that truth draws crowds. This is important to notice, because it challenges a modern assumption that people only want encouragement. Deep down, people want truth. They want clarity. They want someone to tell them what is wrong and how to get right again. John offers that, and people come from everywhere to hear it.

John’s humility is just as striking as his boldness. He knows exactly who he is, and more importantly, who he is not. He refuses to let the attention confuse him. He does not build a following for himself. He points away from himself entirely. He says that the one coming after him is greater, so much greater that John is unworthy even to loosen his sandals. In a world obsessed with recognition, John stands as a rebuke. His entire purpose is to prepare the way and then step aside.

Then Jesus appears.

There is no dramatic entrance. No announcement from the crowd. No reaction shot. Jesus simply comes from Nazareth of Galilee and is baptized by John in the Jordan. If you read too quickly, you might miss how shocking this is. The sinless one submits to a baptism of repentance. The one who needs no cleansing steps into the water with those who do. This is not weakness. It is identification. From the very beginning, Jesus aligns Himself with humanity in its brokenness.

As Jesus comes up out of the water, the heavens are torn open. Not gently parted. Torn. The language Mark uses is violent, deliberate, and irreversible. God does not politely peek into the world; He rips the barrier open. The Spirit descends like a dove, and a voice speaks from heaven, declaring pleasure and identity. “Thou art my beloved Son, in whom I am well pleased.” Before Jesus preaches a sermon, performs a miracle, or calls a disciple, His identity is affirmed. This is crucial. Jesus does not earn the Father’s approval through performance. He receives it before doing anything publicly at all.

That order matters more than many people realize. So many believers spend their lives trying to earn what God offers freely. Mark 1 quietly dismantles that lie. Identity comes before assignment. Belonging comes before obedience. Approval comes before action. When we reverse that order, faith becomes exhausting and joyless. Jesus begins His ministry from a place of affirmation, not insecurity.

Immediately, Mark says, the Spirit drives Jesus into the wilderness. Not gently leads. Drives. The same Spirit who descended in affirmation now pushes Jesus into isolation and testing. This too is unsettling, because it challenges the idea that God’s pleasure guarantees ease. It does not. Sometimes God’s affirmation is followed by testing, not because He doubts us, but because He is preparing us.

Jesus is in the wilderness forty days, tempted by Satan, among wild beasts, attended by angels. Mark gives no details of the temptations themselves. He does not linger. He simply states the reality. Temptation is not an anomaly. It is part of the story. Even Jesus faces it. The difference is not the absence of temptation, but the presence of obedience. Jesus does not negotiate with evil. He endures, resists, and remains faithful.

After John is arrested, Jesus begins His public ministry. The timing is significant. When one voice is silenced, another rises. God’s work does not stop because a servant is removed. It continues through obedience. Jesus comes into Galilee preaching the gospel of God, proclaiming that the time is fulfilled and the kingdom of God is at hand. Repent and believe the gospel. This is not a suggestion. It is a declaration. Something has changed in the fabric of reality, and the appropriate response is repentance and belief.

As Jesus walks by the Sea of Galilee, He sees Simon and Andrew casting a net. They are fishermen. Ordinary men doing ordinary work. Jesus does not approach scholars first. He does not recruit religious elites. He calls working people in the middle of their routines. “Follow me, and I will make you fishers of men.” Mark tells us they immediately leave their nets and follow Him. No debate. No delay. No exit strategy.

This immediacy should make us uncomfortable. It confronts the illusion that obedience requires perfect understanding. They do not know where Jesus will lead. They do not know how long they will be gone. They do not know what the future holds. They only know who is calling. That is enough.

James and John are next. They leave their father in the boat with the hired servants and follow Jesus. This is not just a career shift. It is a relational rupture. Following Jesus often means redefining loyalties. Not abandoning love, but reordering it. Jesus does not apologize for the cost. He simply calls.

From there, Mark moves quickly into action. Jesus enters Capernaum, goes into the synagogue, and teaches. The people are astonished because He teaches with authority, not like the scribes. Authority here is not volume or aggression. It is alignment. Jesus speaks as one who knows God, not one who merely discusses Him. Truth sounds different when it comes from someone who lives it.

A man with an unclean spirit interrupts the service, crying out in recognition and fear. The demon knows exactly who Jesus is. This is one of the most sobering moments in Mark 1. The spiritual realm recognizes Jesus before many people do. The demon calls Him the Holy One of God. Jesus silences the spirit and commands it to come out. There is no struggle. No ritual. Just authority. The spirit obeys.

The people are amazed, not only by the deliverance, but by the manner in which it happens. Jesus does not rely on formulas or traditions. His authority is intrinsic. It flows from who He is. News spreads quickly. Mark emphasizes this again and again. Jesus cannot remain hidden, not because He seeks fame, but because power cannot be concealed.

After leaving the synagogue, Jesus goes to Simon’s house, where Simon’s mother-in-law is sick with a fever. Jesus takes her by the hand and lifts her up. The fever leaves, and she begins to serve them. This is not exploitation; it is restoration. Healing returns people to purpose. The same hand that casts out demons lifts up the sick. Authority and tenderness coexist in Jesus without contradiction.

That evening, the whole city gathers at the door. The sick, the possessed, the desperate all come. Jesus heals many and casts out many demons, but He does not allow the demons to speak because they know who He is. Jesus controls the narrative. Revelation is not forced; it unfolds according to God’s timing.

Very early the next morning, while it is still dark, Jesus goes out to a solitary place to pray. This detail matters. After a night of intense ministry, Jesus does not sleep in. He withdraws to pray. Power flows from communion, not exhaustion. If Jesus needs solitude with the Father, we cannot pretend we do not.

The disciples search for Him, telling Him that everyone is looking for Him. This could have been a moment of expansion, of consolidation, of building momentum. Instead, Jesus says they must go on to other towns, because that is why He came. He refuses to be trapped by popularity. Purpose determines His movement, not demand.

Jesus continues preaching and casting out demons throughout Galilee. Then a leper comes to Him, kneeling and begging, saying that Jesus can make him clean if He is willing. This is one of the most emotionally charged moments in the chapter. Lepers are untouchable. They are isolated, feared, and forgotten. The man’s question is not about ability, but willingness.

Jesus is moved with compassion. He stretches out His hand and touches him. This touch is scandalous. Jesus does not need to touch him to heal him. He chooses to. In that moment, Jesus takes on ritual uncleanness to restore the outcast. “I will; be thou clean.” The leprosy leaves immediately.

Jesus tells the man to say nothing to anyone and to show himself to the priest, but the man goes out and spreads the news freely. The result is ironic. Jesus can no longer openly enter towns and stays in deserted places, while people come to Him from everywhere.

This is where Mark 1 leaves us, with roles reversed. The cleansed man moves freely. Jesus bears the cost. From the very beginning, the pattern of the cross is already present. Jesus heals by taking upon Himself the consequences of restoration. He does not merely fix problems. He absorbs them.

Mark 1 is not a gentle invitation to religious reflection. It is a declaration of invasion. God has entered the world, authority has arrived, and everything must respond. There is no room for delay, no space for neutrality, no comfort in half-hearted belief. The beginning of the gospel is not the beginning of information. It is the beginning of transformation.

And it is only just beginning.

Mark 1 ends in a place that feels unresolved, almost uncomfortable. Jesus is pushed to the outskirts. The healed man walks freely while the Healer withdraws into deserted places. That tension is intentional. Mark wants us to sit with it. He wants us to feel that following Jesus is not about personal comfort or religious polish. It is about collision. When God enters human history, something always gets displaced.

One of the great mistakes we make when reading Mark 1 is treating it like an introduction rather than a warning. We read it as the opening chapter of a book instead of the opening chapter of a life-altering reality. But Mark does not write introductions. He writes thresholds. He writes moments where you either step forward or stay behind. Mark 1 is not asking if you find Jesus interesting. It is asking if you are willing to be changed.

What stands out when you slow down and sit with the chapter is how little time Jesus spends explaining Himself. He declares, He acts, He moves on. The gospel is not built on persuasion techniques or clever arguments. It is built on authority that reveals itself through action. Jesus does not argue demons into submission. He commands them. He does not negotiate with sickness. He touches it. He does not wait for disciples to feel qualified. He calls them while they are still holding nets.

This confronts a deeply ingrained belief many people carry: that we must become ready before we respond. Mark 1 dismantles that idea piece by piece. Readiness does not precede calling; calling creates readiness. Simon and Andrew do not attend a seminar on discipleship. James and John do not receive a five-year plan. They hear a voice, and they move. Obedience begins with trust, not clarity.

Another striking theme in Mark 1 is the relentless pace. The word “immediately” appears again and again. Immediately the Spirit drives Jesus into the wilderness. Immediately the disciples leave their nets. Immediately the demons obey. Immediately the fever leaves. Mark is showing us something about the nature of the kingdom of God. It does not drift in slowly. It arrives decisively. Delay is almost always human, not divine.

This does not mean God is impatient. It means He is purposeful. When God moves, He does so with intent. Our hesitation often comes from fear of loss. The fishermen leave nets. James and John leave their father. Jesus leaves popularity. Every movement forward involves leaving something behind. Mark does not soften this reality. He simply presents it as fact.

The wilderness scenes in Mark 1 deserve special attention, because they frame the entire chapter. John preaches in the wilderness. Jesus is driven into the wilderness. Jesus retreats to a solitary place to pray. The wilderness is not a detour; it is a classroom. It strips away distractions. It exposes motives. It reveals dependence. The wilderness is where identity is tested and clarified.

For Jesus, the wilderness confirms what was already declared at baptism. He is the Son. He does not need to prove it. For us, the wilderness often feels like punishment or abandonment, but Mark 1 suggests otherwise. The wilderness is where God prepares His servants for public faithfulness. What is shaped in solitude sustains obedience in crowds.

Notice also how Mark balances Jesus’ authority with His compassion. He casts out demons with command, yet He touches the leper with tenderness. He heals with power, yet He prays in quiet places. Too often, people try to separate strength and gentleness, as if one cancels the other. Jesus embodies both fully. Mark 1 refuses to let us domesticate Him into a one-dimensional figure.

The leper’s encounter near the end of the chapter is especially revealing. The man does not question Jesus’ power. He questions His willingness. That question echoes through history. People often believe God can help, but doubt that He cares enough to do so personally. Jesus answers that question not with theology, but with touch. He crosses a boundary no one else will cross.

In doing so, Jesus models the cost of compassion. He becomes ceremonially unclean so that the leper can be restored. This exchange foreshadows the cross. From the very beginning, Jesus absorbs the consequences of healing others. Salvation is not a transaction where everyone walks away untouched. Someone always bears the weight. In Mark 1, that someone is already Jesus.

The ending of the chapter leaves us with movement outward. Jesus is still healing. People are still coming. The gospel is spreading, not because of a marketing strategy, but because lives are being changed. Even disobedience plays a role, as the healed man spreads the news despite Jesus’ instructions. Mark is not endorsing disobedience, but he is showing that the power of what Jesus does cannot be contained.

So what does Mark 1 demand of us now?

It demands honesty. Repentance is not optional. Turning toward God requires turning away from something else. There is no version of Christianity that avoids this reality.

It demands movement. Faith is not a mental agreement with ideas. It is a response to a call. Nets are left. Paths are changed. Direction shifts.

It demands humility. John knows his place. Demons know their limits. Disciples learn they are not in control. Pride has no place in the presence of real authority.

It demands trust. Jesus does not give full explanations. He gives commands. Following Him means trusting who He is more than understanding where He is going.

And it demands surrender. From the wilderness to the leper’s touch, Mark 1 shows us that God’s work involves cost. Jesus bears it willingly. Those who follow Him must be prepared to bear it too.

Mark 1 is the beginning of the gospel, but it is also the end of comfortable religion. It introduces a Savior who cannot be managed, predicted, or contained. He interrupts routines, exposes hearts, heals deeply, and then moves on, calling others to follow.

If this is the beginning, then everything that follows makes sense. The cross. The empty tomb. The cost of discipleship. The power of resurrection. None of it is surprising if you have been paying attention since the wilderness, since the nets, since the touch of a leper.

The gospel begins here, but it does not stop here.

It keeps moving.

And it is still calling.

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