There are moments in Scripture that feel almost disruptive, not because they are unclear, but because they refuse to let us stay comfortable with the version of faith we have quietly settled into. Mark chapter 2 is one of those moments. It does not whisper. It does not politely knock. It tears open the roof of our assumptions and lowers something right into the center of our theology, our habits, and our sense of who belongs near God and who does not.

Mark 2 is not simply a chapter about healing or controversy. It is a chapter about collision. Faith collides with systems. Mercy collides with tradition. Authority collides with expectation. And in the middle of all of it stands Jesus, unbothered by outrage, unmoved by fear, calmly redefining what it means to encounter God at all.

What strikes me every time I return to this chapter is how ordinary the setting is. A house. A crowd. Religious leaders watching carefully. Sick bodies and desperate hearts pressing in. Nothing about the scene suggests that history is about to pivot. And yet it does. Quietly. Radically. Permanently.

Jesus has come back to Capernaum, and word spreads quickly that He is home. The house fills beyond capacity. People crowd every doorway, every window, every inch of standing room. This detail matters because it tells us something about human longing. People did not gather because Jesus promised comfort. They gathered because something about Him carried authority, hope, and truth that could not be found anywhere else. They gathered because when Jesus spoke, things changed.

Then Mark introduces four men carrying a paralyzed friend. They cannot get inside. The crowd is too dense. The door is blocked. The path is closed. And here is where the story quietly exposes us. Many people encounter a blocked door and interpret it as God saying no. These men interpret it as a problem to solve.

They climb onto the roof. They dig through it. They create an opening where none existed. And they lower their friend down, right in front of Jesus. This is not polite faith. This is not tidy faith. This is not faith that waits its turn. This is faith that refuses to let obstacles have the final word.

And Jesus sees it. Not the man first, but the faith of his friends. That detail alone unsettles many of our assumptions. Jesus responds not to the paralyzed man’s effort, but to communal faith. He responds to people who loved someone enough to carry him, to inconvenience others, to disrupt a gathering, to risk criticism. This is not a private, individualistic spirituality. This is faith that moves together.

Then Jesus says something unexpected. He does not begin with healing. He begins with forgiveness. “Son, thy sins be forgiven thee.” In that moment, the temperature of the room changes. The religious leaders are no longer passive observers. They accuse Jesus of blasphemy in their hearts. Who can forgive sins but God alone?

They are not wrong in their theology. They are wrong in their vision. They cannot see who is standing in front of them.

Jesus, knowing their thoughts, does not retreat. He does not soften His claim. He asks a question that exposes the heart of the issue. Which is easier, to say your sins are forgiven, or to say rise, take up your bed, and walk? The question is not about difficulty. It is about authority. Anyone can say words. Only God can make them true.

So Jesus heals the man, not as a spectacle, but as evidence. Evidence that forgiveness has authority. Evidence that mercy is not symbolic. Evidence that God’s kingdom is not theoretical. The man rises, carries the very mat that once carried him, and walks out in full view of everyone.

And the crowd is amazed. But amazement is not the same as transformation. Many will marvel at Jesus and still resist Him. Mark wants us to see that proximity to miracles does not guarantee surrender.

Immediately after this, Jesus does something else that unsettles religious categories. He calls Levi, a tax collector. Not after repentance. Not after reform. He calls him where he is. Tax collectors were collaborators, exploiters, symbols of betrayal. And Jesus sees Levi, looks at him, and says two words that change everything: Follow me.

Levi does. Instantly. And then Levi throws a feast. He invites other tax collectors and sinners. Jesus reclines at the table with them. This scene is one of the most revealing moments in the chapter because it shows us what grace looks like in practice. Jesus does not merely tolerate broken people. He enjoys them. He eats with them. He shares space with them.

The religious leaders are scandalized. Why does He eat with sinners? Jesus responds with a sentence that should permanently dismantle spiritual superiority. They that are whole have no need of the physician, but they that are sick. I came not to call the righteous, but sinners.

This is not an insult. It is an invitation. Jesus is not saying some people are actually righteous and others are not. He is saying some people know they are sick, and some people are pretending they are not. And only one of those groups is reachable.

Mark 2 forces us to confront whether our faith is about appearing whole or being healed. Whether we approach God as patients or as inspectors. Whether we want transformation or validation.

Then comes the question about fasting. Why do John’s disciples fast, and the Pharisees fast, but Jesus’ disciples do not? This is not a casual inquiry. It is a test. Are Jesus’ followers serious enough? Disciplined enough? Religious enough?

Jesus answers with imagery that reshapes spiritual imagination. Can the children of the bridechamber fast while the bridegroom is with them? This is not a dismissal of discipline. It is a declaration of presence. Fasting makes sense when God feels distant. But when God is standing in the room, joy is the proper response.

Then Jesus introduces two metaphors that are often quoted but rarely absorbed. New cloth on an old garment. New wine in old wineskins. These are not comments about change for its own sake. They are warnings about incompatibility. The life Jesus brings cannot be contained within old frameworks built to manage control, status, and fear.

Trying to force the gospel into systems designed to preserve power will destroy both the system and the witness. Jesus is not interested in minor adjustments. He is introducing something entirely new.

And then the chapter moves into Sabbath controversy. Jesus’ disciples are walking through grain fields, plucking heads of grain. The Pharisees object. This is unlawful, they say. Jesus responds by referencing David eating the consecrated bread when he was in need. Then He delivers one of the most misunderstood statements in Scripture: The Sabbath was made for man, not man for the Sabbath.

This sentence dismantles religious legalism at its core. God did not create rest as a test. He created it as a gift. The Sabbath is not about proving devotion. It is about restoring life.

And then Jesus says something even more disruptive. The Son of man is Lord also of the Sabbath. This is not merely a theological claim. It is a declaration of authority over time, tradition, and sacred rhythm. Jesus is not breaking the Sabbath. He is revealing its purpose.

What Mark 2 shows us, again and again, is that Jesus is not interested in preserving systems that exclude mercy. He is not impressed by religious performance disconnected from compassion. He is not intimidated by outrage when love is on the line.

This chapter invites us to ask difficult questions. Are we blocking doors that desperate people are trying to break through? Are we more offended by disruption than moved by faith? Are we clinging to old structures that cannot hold the life Jesus brings?

Faith that tears open roofs will always offend those who prefer order over healing. Mercy that eats with sinners will always scandalize those who benefit from distance. And authority rooted in love will always unsettle authority rooted in control.

Mark 2 does not let us remain neutral. It places us in the crowd and asks us where we stand. Are we watching critically, calculating violations? Are we carrying someone toward Jesus? Are we lying on the mat, waiting for a word that restores both body and soul?

This chapter reminds us that Jesus does not ask permission to forgive, to heal, or to redefine belonging. He simply does it. And the invitation is not to admire Him from a distance, but to follow Him into a faith that looks less like maintenance and more like resurrection.

Mark chapter 2 continues to unfold not as a collection of isolated moments, but as a single, deliberate revelation of who Jesus is and what His presence does to every structure it touches. By the time we reach the end of the chapter, it becomes clear that Jesus is not merely correcting misunderstandings. He is re-centering reality itself. Everything that once revolved around rules, status, and control is now being pulled into orbit around mercy, restoration, and truth.

One of the most revealing aspects of this chapter is how consistently Jesus refuses to argue on the terms given to Him. The religious leaders keep presenting questions framed by legality, tradition, and precedent. Jesus responds by reframing the entire conversation around purpose. Not “what is allowed,” but “what brings life.” Not “what has always been done,” but “what God intended from the beginning.”

This distinction matters because it exposes a temptation that still exists in faith communities today. It is easier to defend systems than to discern purpose. Systems are measurable. They can be enforced. They create a sense of order. Purpose, however, requires attentiveness. It demands humility. It forces us to ask whether our structures are serving people or using people to serve the structure.

Jesus consistently chooses people.

When the paralyzed man is lowered through the roof, Jesus does not pause to address the property damage. He does not rebuke the interruption. He does not insist on decorum. He addresses the deepest need first. Forgiveness. This tells us something profound about how Jesus views human suffering. Physical limitations matter. Social exclusion matters. Emotional pain matters. But separation from God is never treated as secondary. Healing without reconciliation would be incomplete.

Yet what is equally striking is that Jesus does not separate forgiveness from restoration. He does not leave the man forgiven but immobilized. The grace of God is never meant to keep us stuck. It lifts, restores, and reorients us toward movement. The mat that once symbolized helplessness becomes evidence of transformation. The man carries the reminder of his former state as testimony, not shame.

This is something many believers struggle to internalize. We want forgiveness without change, or change without vulnerability. Jesus offers neither. He offers wholeness.

The calling of Levi continues this theme in a different way. Levi is not healed from a visible illness. He is healed from a distorted identity. Tax collectors were defined by their profession, their reputation, and their alignment with oppressive power. Jesus does not begin by dismantling Levi’s career with a lecture. He simply calls him into relationship.

Follow me.

Those two words carry an implicit redefinition. Levi is no longer first and foremost a tax collector. He is a follower. Everything else will be re-ordered in time. This is how Jesus still works. He does not demand that people fix themselves before approaching Him. He calls them close enough to be changed.

The meal that follows is not an accident. In the ancient world, table fellowship was a declaration of belonging. Sharing food meant shared life. Jesus eating with sinners was not a casual act of kindness; it was a public statement about who God is willing to sit with. And that statement threatens every hierarchy built on exclusion.

The Pharisees’ objection reveals a mindset that still persists: holiness as separation rather than restoration. But Jesus reframes holiness as proximity. The physician does not avoid the sick. He moves toward them. Not to affirm the sickness, but to heal it.

This is where Mark 2 becomes deeply personal. Many people avoid God not because they do not believe, but because they believe they are too broken to approach Him. Jesus dismantles that lie by placing Himself at the table with those who were told they did not belong there.

Then comes the conversation about fasting. Fasting, in Scripture, is associated with mourning, repentance, longing, and humility. The question posed to Jesus implies that His disciples lack seriousness. But Jesus responds by revealing something astonishing: the season has changed.

The bridegroom is present.

This is not merely poetic language. It is covenantal language. In the Old Testament, God is often described as a bridegroom to His people. By using this imagery, Jesus is making a claim that goes beyond religious practice. He is identifying Himself as the fulfillment of God’s relational promise. Fasting will have its place, He says, but joy is the appropriate response when God is near.

This challenges the idea that spirituality must always look somber to be sincere. There is a form of religiosity that mistakes heaviness for holiness. Jesus rejects that equation. Joy, when rooted in truth, is not shallow. It is evidence of reconciliation.

The metaphors of new cloth and new wine deepen this idea. They warn against trying to contain the life of the kingdom within frameworks designed for something else. Old wineskins were rigid, brittle, already stretched to capacity. New wine, still fermenting, would burst them. Jesus is not criticizing the old for being old. He is pointing out that it cannot carry what He is bringing.

This is where resistance often intensifies. People are willing to accept new ideas as long as they do not require structural change. Jesus insists that transformation cannot be cosmetic. You cannot patch the gospel onto a system built on fear and control. You cannot pour grace into containers shaped by condemnation.

The Sabbath controversy brings all of this to a head. The Sabbath was one of the most sacred institutions in Jewish life. It represented trust in God, rest from labor, and remembrance of creation and deliverance. The Pharisees had built layers of regulation around it to ensure it was never violated. In doing so, they had turned a gift into a burden.

When Jesus’ disciples pluck grain, the accusation is not about hunger. It is about compliance. Jesus responds by pointing to David, Israel’s beloved king, who broke ceremonial law in a moment of need. The implication is clear: human need has always mattered to God more than ritual precision.

Then Jesus delivers the statement that reframes everything: the Sabbath was made for man, not man for the Sabbath. This is not a rejection of sacred rhythm. It is a reclamation of its purpose. Rest exists to restore humanity, not to police it.

And then Jesus declares Himself Lord of the Sabbath.

This statement does more than assert authority. It reveals identity. Only the one who instituted the Sabbath could claim lordship over it. Jesus is not a reformer working within the system. He is the origin of the system stepping into it.

What Mark 2 ultimately confronts us with is a choice. Do we want a faith that feels manageable, or a faith that is alive? Manageable faith can be scheduled, regulated, and contained. Living faith disrupts, challenges, and transforms.

Jesus disrupts spaces when faith breaks through roofs. He challenges reputations when He calls the unwanted. He transforms traditions by restoring their original intent. And He does all of this without apology.

This chapter asks us whether we are more concerned with guarding boundaries or opening doors. Whether we evaluate faith by compliance or by compassion. Whether we see people as problems to manage or lives to restore.

Mark does not record these events to entertain us. He records them to reorient us. To show us that Jesus does not fit neatly into religious boxes, because He was never meant to. He is not a supplement to existing systems. He is the center around which everything else must turn.

If we are honest, Mark 2 exposes areas where we have grown comfortable with distance. Distance from need. Distance from discomfort. Distance from people whose presence complicates our categories. Jesus refuses that distance. He moves toward paralysis, toward betrayal, toward hunger, toward accusation.

And He invites us to do the same.

Faith, in this chapter, is not passive belief. It is active trust. Trust that carries people. Trust that digs through obstacles. Trust that follows when called. Trust that rejoices in God’s nearness. Trust that rests without fear.

The chapter closes not with resolution, but with tension. The questions are not settled. The opposition has not disappeared. In many ways, it has only begun. But that, too, is part of the message. Living faith will always provoke resistance from systems that benefit from the way things are.

Yet Mark 2 assures us that resistance does not diminish authority. Compassion does not weaken truth. And mercy does not compromise holiness.

Jesus walks away from every confrontation in this chapter unchanged, but everything else is altered. And that is the invitation placed before us as well. Not to domesticate Him, but to follow Him. Not to protect our structures, but to participate in His restoration. Not to manage faith, but to live it.

That is what it means to let mercy break the roof.

Watch Douglas Vandergraph’s inspiring faith-based videos on YouTube https://www.youtube.com/@douglasvandergraph

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Your friend, Douglas Vandergraph

#Faith #BibleStudy #GospelOfMark #ChristianReflection #ScriptureStudy #FaithAndLife #JesusChrist

I’m in the last 20% of my #AdventOfProgress project for a public release, but I started a new project over the weekend. Now I’m here in the last 20% and got distracted with an old project. 😅

Today something out of my control distracted me. And while I get distracted, I get more ideas to distract myself even more from other stuff.

Getting distracted from distractions is distracting. 🫠

---

88 of #100DaysToOffload
#log
Thoughts?

walk the burn-side to the bench

even in winter the knot grass the bitter cress like alpine finds

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.

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#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

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

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

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.

⚠️ SPOILER WARNING: FULL SPOILERS

My Rating: ⭐⭐⭐½ (3.5/5 stars)

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.

#review #movies

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
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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