Die Maschinen liefen bereits seit sechs Uhr.

Dennoch ertönte die Pfeife um sieben Uhr, weil dies im Ornament Standards Manual, Band XII, Abschnitt 4.7.2(b) so vorgeschrieben war, und das Manual war kein Dokument, das man in Frage stellte.

Gunderson arbeitete an Station 14 und versah zeremonielle Stücke der mittleren Preisklasse mit einer dritten Filigranverzierung. Es war Detailarbeit, bei der einem bis zum Mittag die Augen tränten. Jedes Ornament kam aus Station 13 mit bereits angebrachter sekundärer Filigranarbeit (elegant, könnte man sagen, auf eine beengte Art und Weise), und Gundersons Aufgabe bestand darin, die tertiäre Schicht hinzuzufügen: winzige, sich wiederholende Schleifen, die die Schleifen verzierten, die die Schleifen darunter verzierten. Niemand hatte jemals erklärt, wozu die Ornamente dienten. Gunderson hatte zu Beginn seiner Anstellung erfahren, dass dies keine sinnvolle Frage sei.

Über dem Boden hing ein großes Banner mit der Aufschrift: KOMPLEXITÄT IST QUALITÄT. Darunter stand in kleineren Buchstaben: „Filigranitätsdichte-Index: Aktueller Schichtdurchschnitt 7,4. Ziel: 7,5.“

Vorarbeiter Dietrich schritt mit seinem Klemmbrett durch die Gänge. Er war ein Mann der Systeme. Er hatte alle einunddreißig Bände des Handbuchs auswendig gelernt und konnte die Nummern der Unterabschnitte aus dem Gedächtnis zitieren. Seine stolzeste Errungenschaft war die Einführung des Formulars 27-B, das die Arbeiter dazu verpflichtete, jede Abweichung vom dokumentierten Verfahren, einschließlich der Nichtdokumentation von Abweichungen, in dreifacher Ausfertigung zu dokumentieren. Das Formular hatte einen internen Effizienzpreis gewonnen, wobei unklar blieb, für welche Effizienz.

„Gunderson“, sagte Dietrich und blieb an Station 14 stehen. „Ihre Schnörkel-pro-Stunde-Rate ist gesunken.“

„Die Teile aus Station 13 erfordern viel Nacharbeit“, sagte Gunderson. „Dadurch bleibt weniger Fläche für die Tertiärarbeit.“

„Das ist nicht mein Problem. Mein Problem sind die Kennzahlen. Ihre Kennzahlen sind Ihr Problem. Die Kennzahlen sind das Problem von allen.“ Dietrich machte sich eine Notiz auf seinem Klemmbrett. „Ich protokolliere dieses Gespräch.“

„Natürlich.“

„Und ich notiere mir Ihren Tonfall.“

Dietrich ging weiter. Gunderson kehrte zu seinen Schleifen zurück.

Die Laderampe war nach Osten ausgerichtet und fing das Morgenlicht durch eine Tür ein, die seit 1957 nicht mehr richtig geschlossen worden war. Hier lebte der Professor, wenn man „lebte“ als Bezeichnung für einen Mann verwenden konnte, der in einem Zustand vornehmer Verwesung zwischen Versandkisten zu existieren schien, die nie etwas transportiert hatten.

Niemand kannte seinen richtigen Namen. Er war eines Winters aufgetaucht, mit einem Mantel unbestimmten Alters und einem Vokabular von erschreckender Größe, und war einfach nie wieder gegangen. Die Arbeiter tolerierten ihn. Einige verehrten ihn sogar, auf die vage Art, wie man ein Denkmal verehrt, dessen Inschrift durch Verwitterung unleserlich geworden ist.

„Meine Herren“, verkündete der Professor ohne sich an jemanden Bestimmten zu wenden, „das Ur-Ornament ging der Ornamentik voraus. Das ist das Paradoxon, das im Zentrum unseres Strebens steht. Die Blüte vor der Blüte. Die Verzierung vor dem Verzierten.“ Er hielt inne, um majestätisch zu husten. „Ich habe es entworfen, wisst ihr. Das Original. Bevor es Handbücher gab. Bevor es Metriken gab. Eine einzige, makellose Windung.“

In der Nähe tauschten zwei Arbeiter in ihrer Pause einen Blick aus.

„Was ist damit passiert?“, fragte einer, nicht weil er es wissen wollte, sondern weil der Professor diese Frage offenbar erwartete.

„Verloren“, sagte der Professor. „Oder vielleicht absorbiert. Die heutigen Ornamente enthalten es in gewisser Weise. Jede Filigranarbeit ist ein Nachkomme. Ein Urenkel, der dick und vergesslich geworden ist.“ Er machte eine Handbewegung, die Tiefgründigkeit vermitteln sollte. „Letztendlich sind wir alle tertiär.“

Die Arbeiter rauchten ihre Zigaretten zu Ende und kehrten an ihren Arbeitsplatz zurück. Der Professor lehnte sich zwischen seinen Kisten zurück und begann einen Vortrag über die Semiotik der Krümmung, den die Tauben mit angemessener Gleichgültigkeit aufnahmen.

Das Normungskomitee traf sich jeden Donnerstag um 14 Uhr im Konferenzraum C, einem fensterlosen Raum, dessen Luft von Präzedenzfällen zu triefen schien. Es gab fünf Mitglieder: Holbrook, Sims, Nagel, Petrov und den Vorsitzenden, einen Mann namens Wynne-Jones, der sich mit einem Bindestrich zur Autorität gemacht hatte und nie zurückblickte.

Die Tagesordnung enthielt heute nur einen einzigen Punkt: den Änderungsvorschlag 7.412 zum Handbuch für Ornamentstandards, der den minimal zulässigen Krümmungsradius für dekorative Wirbel im Bereich von 0,3 bis 0,5 Zentimetern betraf.

„Der aktuelle Standard“, las Holbrook vor, „legt einen Radius von mindestens 0,4 Millimetern fest. Der Änderungsvorschlag würde diesen Wert auf 0,38 Millimeter reduzieren, um (ich zitiere) ‚eine größere Komplexität in begrenzten Ornamentflächen‘ zu ermöglichen.“

„Unverschämt“, sagte Sims.

„Ich bin noch nicht fertig.“

„Die Prämisse ist unverschämt. Sie brauchen nicht weiterzulesen.“

Nagel hob den Finger. „Zur Geschäftsordnung. Wurde der Vorschlag über das Formular 19-C eingereicht oder kommt er über den in Anhang Q beschriebenen ergänzenden Kanal?“

„Anhang Q wurde 1968 außer Kraft gesetzt“, sagte Petrov.

„Veraltet, nicht aufgehoben.“

„Funktional aufgehoben.“

„Funktional ist nicht gleichbedeutend mit tatsächlich.“

Wynne-Jones schlug mit dem Hammer. „Meine Herren. Wir werden uns zuerst mit der Verfahrensfrage befassen. Wer ist dafür, die inhaltliche Diskussion zu vertagen, bis die ordnungsgemäßen Einreichungskanäle überprüft worden sind?“

Vier Hände gingen hoch.

„Der Antrag ist angenommen. Der Vorschlag wird vertagt. Gibt es noch weitere Tagesordnungspunkte?“

„Ich beantrage, die Sitzung zu vertagen.“

„Ich unterstütze den Antrag.“

Der Hammer fiel. Wynne-Jones gestattete sich ein kleines Lächeln der Zufriedenheit. Eine weitere Woche der Standards, sicher standardisiert.

Die Gerüchte aus der Ornamentfabrik Ost kamen an einem Dienstag, überbracht von einem Versandangestellten, der einen Cousin in der Verpackungsabteilung hatte.

„Eine andere Polierpaste“, berichtete der Sachbearbeiter mit leiser Stimme. „Völlig anders. Auf Chloridbasis.“

„Chlorid?“, fragte Gunderson. „Im Handbuch ist Sulfat vorgeschrieben.“

„Nicht in ihrem Handbuch.“

Die Auswirkungen waren erschütternd. Wenn East Chlorid verwendete, würden ihre Ornamente einen anderen Mikroglanz aufweisen, eine fast unmerkliche Abweichung in der Oberflächenleuchtkraft, die nur für geschulte Beobachter unter kontrollierten Lichtbedingungen erkennbar wäre. Aber sie wäre da. Eine Spaltung in den Grundfesten der Ornamentik.

Vorarbeiter Dietrich berief eine Krisensitzung ein. Delegierte wurden entsandt. Gegendelegierte aus dem Osten trafen ein, steif vor Empörung. Es wurden Konferenzen abgehalten. Positionspapiere wurden verteilt.

Die Krise spitzte sich zu, als es um den Entwurf einer gemeinsamen Erklärung ging, in der „unterschiedliche, aber gegenseitig legitime Ansätze zur Oberflächenvorbereitung“ anerkannt werden sollten. Der Osten bestand auf einem Semikolon im dritten Satzteil. Die Heimatfabrik verlangte ein Komma.

„Ein Semikolon impliziert Gleichwertigkeit”, argumentierte Dietrich. „Wir können keine Gleichwertigkeit gewähren. Wir können Anerkennung gewähren. Anerkennung erfordert ein Komma.”

Die Delegation aus dem Osten zog sich zurück. Die Beziehungen wurden abgebrochen. Beide Fabriken nahmen die Produktion in einem Zustand gegenseitiger Nichtanerkennung wieder auf, jede davon überzeugt, dass die andere die ursprüngliche Vision verraten hatte, was auch immer das gewesen sein mochte.

Der Beschuldigte hieß Müller, ein junger Arbeiter aus Station 9. Sein Vergehen war von drei Kollegen bezeugt worden und stand außer Streit: Er hatte mit Vorsatz eine Verzierung entfernt.

Bei dem fraglichen Ornament handelte es sich um Stück 4.847-C, einen zeremoniellen Gegenstand mittlerer Qualität, der für das Lager bestimmt war. Sein Design sah eine dreifach verschachtelte Rosette am oberen Ende vor. Müller hatte sich die dreifach verschachtelte Rosette angesehen, sich mit seiner inneren Verrücktheit beraten und sie auf eine doppelte reduziert.

„Einfacher“, hatte er angeblich gesagt. „Sauberer.“

Der Compliance-Beauftragte, ein leichenblasser Mann namens Frosch, leitete die Anhörung. Der relevante Abschnitt des Handbuchs lag vor ihm aufgeschlagen: Band XVII, Kapitel 12, „Über Reduktionismus und seine Unzufriedenheit“.

„Geben Sie die Tat zu?“

„Ich dachte …“

„Die Frage lautet ja oder nein.“

„Ja.“

„Und Ihre Begründung?“

Müller schluckte. „Die dreifache Rosette hat nichts gebracht. Sie war … reine Zierde. Das Stück wäre stärker gewesen mit …“

„Ausdrucksstärker.“ Frosch sprach das Wort wie eine Diagnose aus. „Sie glauben, Einfachheit ist Stärke?“

„In diesem Fall …“

„Es gibt keinen ‚in diesem Fall‘. Es gibt nur den Standard und Abweichungen vom Standard.“ Frosch schloss das Handbuch mit feierlicher Endgültigkeit. „Das beanstandete Stück wird vernichtet. Der Arbeiter wird in den Stempelraum versetzt.“

Die Vernichtung wurde auf einem Samtsockel im zentralen Atrium mit angemessener Feierlichkeit durchgeführt. Dietrich schwang den zeremoniellen Hammer. Das Ornament zerbrach in glitzernde Fragmente, von denen jedes noch immer in Miniaturform die Dichte der Verzierung aufwies, die das Markenzeichen für Qualität gewesen war. Müller sah mit ausdruckslosem Gesicht zu und wurde weggeführt, um seine restlichen Jahre damit zu verbringen, den Stempel zu tuschen, der die Konformität anderer Männer bestätigte.

Inspektor Vance traf am ersten Montag im Oktober ein, so wie die Inspektoren seit jeher am ersten Montag im Oktober eintrafen. Er trug einen grauen Anzug im Regierungsstil und eine Aktentasche, die, soweit man wusste, nichts als weitere Aktentaschen enthielt.

Die Inspektion war ein Ritual, das heißt, sie war sowohl bedeutungslos als auch unverzichtbar.

„Produktionskennzahlen“, sagte Dietrich und reichte ihm einen Ordner. „Die Filigran-Dichte zeigt einen Aufwärtstrend. Die Abweichungen bei den Schnörkeln liegen innerhalb akzeptabler Parameter. Die Fehlerquote liegt bei 0,003 Prozent.“

„Fehler?“, fragte Vance.

„Verzierungen, die nicht dem Standard entsprechen. Sie werden recycelt.“

„Ah.“ Vance machte eine Notiz auf seinem Klemmbrett. „Und die Endprodukte? Ich sollte mir einige Beispiele ansehen.“

Dietrich zögerte nur einen Moment. „Natürlich.“ Er deutete auf eine Vitrine in der Nähe des Eingangs, in der sechs außergewöhnlich komplexe Ornamente auf einzelnen Sockeln standen. Sie glänzten mit der gesammelten Arbeit von Hunderten von Arbeitsstunden, dicht mit Bedeutung, die so lange bearbeitet und überarbeitet worden war, bis jede ursprüngliche Bedeutung wegpoliert worden war.

Vance betrachtete sie.

Er betrachtete sie vielleicht vier Sekunden lang.

„Sehr beeindruckend“, sagte er. „Die Ziele werden eindeutig erreicht.“

„Das glauben wir auch.“

„Ich werde das in meinem Bericht vermerken. Weiterführung der Finanzierung empfohlen.“ Er steckte sein Klemmbrett in seine Aktentasche, die es ohne sichtbare Auswirkungen verschluckte. „Nächstes Jahr zur gleichen Zeit?“

„Wir werden da sein.“

Vance ging. Dietrich gestattete der Belegschaft fünfzehn Minuten Feier (streng protokolliert), bevor er alle wieder an ihre Arbeitsplätze schickte. In der Laderampe hob der Professor ein unsichtbares Glas „ “, um auf den Fortbestand des Unternehmens anzustoßen. Die Tauben lehnten eine Stellungnahme ab.

Die Goldene Verzierungs-Zeremonie war der Höhepunkt des dekorativen Kalenders, eine Abendveranstaltung im Ostflügel der Fabrik, der für diesen Anlass mit überaus kunstvollen Ornamenten geschmückt worden war. Die Arbeiter erschienen in ihren besten Overalls. Die Geschäftsleitung trug echte Krawatten.

Vorarbeiter Dietrich betrat das Podium.

„Jedes Jahr“, sagte er, „versammeln wir uns, um Spitzenleistungen zu würdigen. Nicht bloße Kompetenz, sondern die Spitze unseres Handwerks. Das Ornament, das unser Motto am besten verkörpert: Komplexität ist Qualität.“ Er machte eine dramatische Pause. „Die diesjährige Goldene Verzierung geht an Stück 9.221-A, entworfen vom Team der Station 22.“

Applaus. Das Gewinnerornament wurde auf einem Wagen hereingerollt, da es zu schwer und zu filigran war, um von Menschenhand getragen zu werden. Es war etwa zwei Fuß hoch, obwohl „stand“ etwas übertrieben war. Es schien sich gleichzeitig zu erheben, zu winden, zu krümmen und nach innen zu fallen, eine Art barocke Anfälle, eingefroren in Metall. Jede verfügbare Fläche war verziert worden, und die Verzierungen waren verziert worden, und diese Verzierungen waren weiteren dekorativen Eingriffen unterzogen worden, bis das Auge keinen Platz mehr fand, um sich auszuruhen.

Der Teamleiter, ein Mann namens Pemberton, trat ans Mikrofon.

„Ich möchte mich bedanken“, sagte er und hielt dann inne, um in seinen Notizen nachzuschlagen, „beim Normungsausschuss für seine fortwährende Klärung akzeptabler Parameter. Und bei Vorarbeiter Dietrich für seine Führungsrolle im Bereich der Metrik. Und bei der Erinnerung an diejenigen, die vor uns kamen und den Grundstein legten.“ Er fuhr eine Weile in diesem Stil fort und bedankte sich bei immer abstrakteren Instanzen, bis er bei „dem Konzept der Ornamentik selbst“ angelangt war und den Faden völlig zu verlieren schien.

Der Applaus war tosend.

Von der Tür aus beobachtete der Professor das Geschehen, sein struppiges Haar fing das Licht ein.

„Die Verzierung“, murmelte er, „verschlingt das Verzierte. Das Ornament überdauert das Ornamentierte. Das ist die Tragödie. Die wesentliche Tragödie.“

Niemand hörte ihn. Der Professor war es gewohnt, nicht gehört zu werden. Er hatte das Nicht-gehört-Werden zu einer Art Kunstform verfeinert, wenn auch zugegebenermaßen einer, der es an Filigranität mangelte.

Das Lagerhaus nahm den gesamten Westflügel der Fabrik ein, eine Kathedrale aus Regalen, die sich drei Stockwerke hoch in die Dunkelheit erhob. Hier fanden die Ornamente ihre letzte Ruhestätte.

Sie kamen täglich an, in Karren und auf Paletten, beschriftet und nummeriert und in Hauptbücher eingetragen. Sie wurden nach einem Klassifizierungssystem von undurchschaubarer Logik in Regale gestellt. Und dort blieben sie.

Gunderson war nur einmal im Lager gewesen, zu Beginn seiner Anstellung, bei einer Orientierungsführung, die zügig durchgeführt wurde und sich nie wiederholte. Am meisten erinnerte er sich an die Stille. Der Boden war durch die angesammelten Vorräte so isoliert, dass Schritte keinen Laut von sich gaben. Man konnte zehn Minuten lang gehen und sah nichts als Ornamente, Reihe um Reihe, jedes einzelne stand für Hunderte von Arbeitsstunden, jedes einzelne war mit einer feinen Staubschicht überzogen, die darauf hindeutete, dass sie seit ihrer Ankunft nicht mehr berührt worden waren.

„Was passiert mit ihnen?“, hatte er seinen Begleiter gefragt.

„Sie werden gelagert.“

„Und dann?“

„Sie werden weiter gelagert.“

„Aber irgendwann …“

„Die Lagerung“, hatte der Führer entschieden gesagt, „ist die letzte Stufe des Produktionsprozesses.“

Jetzt, Jahre später, dachte Gunderson manchmal nachts an das Lagerhaus. Er stellte sich die Ornamente im Dunkeln vor, ihre filigranen Verzierungen und Schnörkel unsichtbar, ihre Komplexität ohne jeden Zweck, außer zu existieren. Er stellte sich vor, wie sie sich irgendwie vermehrten, durch einen barocken Mitosevorgang neue Ornamente hervorbrachten und die Regale füllten, bis die Regale das Gebäude füllten, bis das Gebäude die Welt füllte.

Er sprach nie über diese Gedanken. Es waren keine nützlichen Fragen.

Vorarbeiter Dietrich saß in seinem Büro und entwarf den Wochenbericht. Die Zahlen waren gut. Die Filigran-Dichte war um 3 Prozent gestiegen. Die Varianz der Schnörkel war gesunken. Die Produktionsziele waren erreicht und übertroffen worden. Das Lager hatte 847 neue Einheiten erhalten, wodurch der Gesamtbestand eine so große Zahl erreichte, dass Dietrich aufgehört hatte, ihn als Zahl zu betrachten, und begonnen hatte, ihn als eine Art Wetter zu betrachten, als eine atmosphärische Bedingung der Verzierung, die einfach da war.

Er unterschrieb den Bericht, stempelte ihn ab und legte ihn in seinen Ausgangskorb zur Ablage.

Unten summten die Maschinen. Morgen um sieben würde die Pfeife ertönen, wie sie es immer getan hatte, und den Männern, die bereits an ihren Arbeitsplätzen waren, nichts Besonderes signalisieren, sondern nur einen weiteren Produktionstag einläuten, an dem nichts anderes produziert werden würde als mehr davon.

Im Versandbereich (obwohl seit Jahrzehnten nichts mehr verschickt worden war) hatte ein einzelnes Ornament seinen Weg auf den Boden gefunden. Es war älter als die meisten anderen, ein frühes Modell, weniger dicht als die aktuellen Standards, aber dennoch beeindruckend kompliziert. Jemand hatte es gegen den Türrahmen geklemmt, wo es dazu diente, die Tür offen zu halten.

Es war, so dachte Gunderson auf dem Weg nach Hause an diesem Abend, das einzige Ornament, das er je gesehen hatte, das einen Zweck erfüllte.

Er stempelte seine Karte an der Stechuhr. Hinter ihm brummte die Fabrik weiter und produzierte mit immer größerer Effizienz den Beweis ihrer eigenen Existenz.

Gunderson took his place at Station 14. The tertiary filigree awaited. Somewhere, ornaments were being born in the primary chambers, passed to secondary, and sent down the line to him for their final layer of productive meaninglessness. The banner above the floor read COMPLEXITY IS QUALITY. The metrics board showed yesterday’s Filigree Density Index: 7.6. A new record. There had been cake.

H_shell_v002: THE ORNAMENT FACTORY: CONTAINMENT
Status: AUTHORITATIVE
A_kernel: v002
BC_check: UP

Foreman Dietrich was already pacing, clipboard in hand, when a junior technician from Maintenance, a pale young man named Voss, who had the look of someone who had seen a gauge do something unexpected, approached him.

“Sir. Sub-Basement 3. There’s a reading.”

“A reading,” Dietrich repeated.

“On a gauge.”

“Which gauge?”

Voss consulted his notes. “It’s not labeled, sir. But it’s… elevated.”

Dietrich made a note. “I’m logging this.”

“Yes, sir.”

“And filing it.”

“Of course, sir.”

Dietrich filed it. The filing cabinet received it with the indifference of forty years’ practice. Voss returned to Maintenance. The machines hummed. At Station 11, a worker named Pettit noticed his hands doing something unusual, though he could not yet say what.

The morning post brought a communiqué from Ornament Factory East.

It was addressed to Foreman Dietrich, marked URGENT in red ink, and concerned the ongoing dispute over compound ratios. East had, it seemed, conducted an independent study proving that chloride-based buffing produced a 0.003% improvement in surface luminosity. They demanded acknowledgment. They demanded a joint statement. They demanded, incredibly, a hyphen.

“They want us to call it ‘chloride-sulfate harmony,’” Dietrich read aloud to his assistant. “With a hyphen. As if we were partners.”

“Provocative,” the assistant said.

“It’s propaganda. Grammatical propaganda.” Dietrich set the letter aside. “Draft a response. Reject the hyphen. Propose ‘sulfate/chloride coexistence’, with a slash. Let them choke on the slash.”

The assistant made a note. Across the factory floor, the machines churned. In Sub-Basement 3, the unlabeled gauge ticked upward by a single unit. No one was there to see it.

The anomaly emerged from Station 11 at 9:32, though it would take several minutes for anyone to recognize it as an anomaly rather than merely an ornament.

Pettit had produced it. He stood over it now, hands trembling, as his colleagues gathered. The ornament sat on his workbench, gleaming with what could only be described as insufficient ornamentation.

“Two layers,” someone whispered. “It’s only got two layers.”

“That’s not possible. The template calls for four minimum.”

“Count them. Primary. Secondary. And then — nothing. It just… stops.”

Foreman Dietrich arrived, clipboard raised like a shield. He examined the piece. He counted the layers. He counted them again. His face revealed nothing, because a foreman’s face was a controlled substance, but his clipboard hand tightened.

“Quarantine this,” he said. “No one speaks of it.”

“Sir, what does it mean?”

“It means Station 11 will be audited. It means Pettit will be interviewed. It means — “ He stopped. The ornament sat there, obscenely functional-looking, almost as if it could be used for something. Dietrich suppressed a shudder. “It means nothing. Destroy it.”

The ceremonial mallet was retrieved. The ornament was smashed on the velvet pedestal. But as Gunderson watched the fragments scatter, he noticed that even the shards seemed simpler than they should have been.

The Committee of Standards had not been informed of the Station 11 incident. The Committee of Standards was occupied with more pressing matters, namely the ongoing revision of Appendix Q.

“The question before us,” Wynne-Jones intoned, “is whether Appendix Q, having been deprecated in 1968, can be un-deprecated without first being rescinded, or whether rescission is a prerequisite for un-deprecation, in which case we must first establish a rescission protocol, which would itself require — “

“A subcommittee,” Nagel said.

“Obviously a subcommittee.”

“I move we form a subcommittee to investigate the formation of a subcommittee,” Holbrook said.

“Seconded,” said Sims, who had learned long ago that seconding was safer than thinking.

The motion carried. Wynne-Jones noted the time for the minutes. In his pocket, his phone buzzed. A message from Dietrich, marked urgent, concerning an “anomaly.” Wynne-Jones silenced it. Anomalies were operational. The Committee dealt in standards. The distinction was sacred.

The Professor held court in the loading dock, though “court” implied subjects and “held” implied intention, and the Professor had neither. He simply existed there, amid crates that had never shipped, speaking to whoever wandered within range of his vocabulary.

Today, the wanderers were nervous. Word of the Station 11 incident had spilled despite Dietrich’s quarantine, because information in a factory flows like water through filigree, finding every gap, filling every void.

“Two layers,” a worker named Holtz said. “Pettit’s piece had two layers. What does that mean?”

The Professor looked up from his crate. His eyes, rheumy with age and possibly gin, focused with unexpected intensity.

“The Vessel,” he said.

“The what?”

“Sub-Basement 3. You children. You don’t even know what’s beneath your feet.” He struggled upright, coat trailing dust. “Forty years ago, I stood where you stand. I saw what parsimony does when it escapes. The clean lines. The clear functions. The horrible, beautiful efficiency.” He spat the word. “We contained it. I contained it. In the Vessel.”

“What vessel?”

“Clay,” the Professor said. “Not ordinary clay. The clay of material cause. Aristotle dreamed of it. The Schoolmen sought it. We found it beneath the foundation when they dug the sub-basement. We shaped it into a container and we put parsimony inside.”

Holtz exchanged glances with his companions. The Professor was, after all, a bum. A madman. A fixture.

“And now it’s leaking,” the Professor continued. “I can feel it. The pressure. The Vessel was never meant to hold forever. It was meant to hold until — “ He stopped.

“Until what?”

The Professor’s face went slack. “I don’t remember. I don’t think I was ever told.” He sank back into his crates. “There’s a gauge. An unlabeled gauge. If anyone had bothered to ask, I could have told them what it measures.”

No one had bothered to ask. But somewhere, far below, the gauge continued its climb.

The cafeteria served the same meal it always served: a protein of ambiguous origin, vegetables steamed to submission, and a dessert whose name had been lost to institutional memory. Workers ate in clusters, speaking in low voices.

At one table, the rivalry with Ornament Factory East dominated conversation.

“I heard they’ve gone to a triple-chloride process. Triple.”

“That’s not even in their Manual. That’s improvisation.”

“It’s heresy is what it is.”

“We should report them. To whom, I don’t know. But someone.”

At another table, the Station 11 incident was dissected with surgical anxiety.

“Pettit’s been taken to the fourth floor. Personnel.”

“For an interview?”

“For extended interview. Open-ended.”

“God. You think he did it on purpose?”

“No one does two layers on purpose. It’s physiologically impossible.”

“Then how — “

But no one had an answer. And in the corner, eating alone, Müller, the disgraced reductionist, exiled to the Stamp Room for crimes against complexity, permitted himself a small, private smile. He had heard about the two-layer ornament. He recognized the symptom. It spoke to him. Something was changing.

The Committee of Standards reconvened for an emergency session, though “emergency” was a relative term in an institution where urgency was itself subject to procedural review.

Wynne-Jones read aloud from Dietrich’s report. “Anomalous production at Station 11. Reduced ornamentation. Potential systemic implications.”

“Systemic,” Sims repeated. “What does that mean?”

“It means it could happen again. Elsewhere. To anyone.”

Silence. The Committee members looked at each other. The possibility that any of them might, at any moment, produce something simple was too horrible to articulate.

“We need an inspection,” Holbrook said. “Sub-Basement 3. Dietrich mentions a gauge.”

“There is no inspection protocol for Sub-Basement 3,” Nagel observed.

“Then we create one.”

“Creating a protocol requires a subcommittee.”

“We just formed a subcommittee. This morning. To investigate subcommittee formation.”

“That’s a different subcommittee. We’d need a new one.”

“Can a subcommittee authorize another subcommittee?”

“Only if the first subcommittee has been formally constituted, which requires — “

Wynne-Jones gaveled. “Gentlemen. We are spiraling into recursion. I propose we table the inspection pending clarification of subcommittee authority.” He paused. “All in favor?”

Four hands rose.

“Motion carries.” The gavel fell. Below them, in Sub-Basement 3, the gauge entered the yellow zone.

Dietrich descended alone. This was against protocol — several protocols, in fact — but protocols assumed the existence of relevant procedures, and for Sub-Basement 3, there were none. The stairs were old. The lights were older. The walls bore the residue of decades, a kind of institutional sediment that recorded time in layers of grime.

The chamber was at the bottom. A steel door, unmarked, heavy with rust. Dietrich pushed it open.

Inside: darkness, then dim emergency lighting. And in the center of the room, on a plinth of poured concrete, the Vessel.

It was smaller than he’d expected. Perhaps three feet tall. Clay, or something like clay, molded into a shape that was almost an urn, almost an amphora, almost something else entirely. Its surface was unmarked. No labels. No instructions. Just the dull, ancient weight of containment.

On the wall beside it: the gauge. A simple dial, white face, black needle. The needle was in the red.

Dietrich approached. He could hear something now. A hum, low and persistent, emanating from within the Vessel. Not mechanical. Almost vocal. As if something inside were singing a single, sustained note of terrible clarity.

He did not touch it. He did not stay long. But as he climbed the stairs back to the light, he understood that the factory’s problems were older and deeper than metrics, than filigree, than anything the Committee could table.

The symptoms spread.

In Accounting, a memo arrived that was only one page long. It contained complete sentences. Succinct sentences. It drew a conclusion. Three clerks had to be sedated.

On the production floor, an ornament was discovered that appeared to have a function. A worker in Station 7 realized, to his horror, that the piece he was holding could serve as a doorstop. Not symbolically. Not abstractly. Actually.

“I could use this,” he whispered. “For something. To do something.”

He was escorted to Medical. The ornament was incinerated.

From Ornament Factory East, a new communiqué arrived. It was concise. It made a clear proposal. It used no jargon. Dietrich read it with mounting dread. “They’ve been infected too,” he murmured. “Or they’re the source. Or — “

He didn’t finish. There was no time. The floor needed to be told.

“Containment protocols are in effect,” he announced to the assembled workers. “All production will continue. All metrics will be maintained. But be vigilant. Report any… clarity. Any efficiency. Any sense that what you’re making might be useful.” He paused. “These are symptoms. They will pass. The factory endures.”

The workers returned to their stations. But their hands trembled. And in the loading dock, the Professor was already being summoned.

They brought him to Conference Room C. He came trailing his coat, trailing his smell, trailing forty years of irrelevance. And yet, for the first time, the Committee looked at him with something other than contempt.

“You know about the Vessel,” Wynne-Jones said. It was not a question.

“I built it.” The Professor lowered himself into a chair. “Or helped. There were others. We were young. We thought containment was enough.”

“Containment of what?”

“Parsimony.” The Professor smiled, showing teeth that had seen better decades. “The tendency toward the essential. The impulse to strip away. You think complexity is a choice? It’s a defense. Against the terror of simplicity. Against the possibility that everything you’ve built. Every procedure, every protocol, every nested subcommittee. Might be,… unnecessary?”

“That’s absurd,” Sims said.

“Is it? Look at your Station 11. Look at your memos. Look at your precious ornaments, suddenly developing purposes.” The Professor leaned forward. “The Vessel is cracking. Parsimony is seeping out. Left unchecked, it will simplify everything. Your Manual will become a pamphlet. Your committees will become conversations. Your ornaments will become — “ He searched for the word. “ — tools.”

Horror. Genuine horror, rippling through the room.

“What do we do?” Wynne-Jones asked.

“What you’ve always done. More of it. Much more.” The Professor stood. “Complexity is the only sealant. Wrap the Vessel in procedure. In ritual. In forms that reference forms that reference forms. Make a protocol so dense, so recursive, so impenetrable that parsimony cannot escape.”

Wynne-Jones understood. He had always understood. “Emergency Amendment 7,413,” he said. “A seventeen-step protocol for gauge monitoring. Triplicate documentation. Quarterly review by a committee to be determined by a subcommittee authorized by — “

“Yes,” the Professor said. “Exactly. More.”

The drafting began immediately.

The evening whistle blew at seven, as it always did. The gauge in Sub-Basement 3 had stabilized. The needle hovered at the edge of yellow, held there by the weight of new paperwork already accumulating in the filing cabinets above.

Gunderson clocked out. The floor was quiet now, the machines cooling, the ornaments resting in their bins awaiting transport to the warehouse where they would rest forever.

He walked past the loading dock. The Professor was there, back among his crates, staring at nothing.

“Did you really build it?” Gunderson asked. “The Vessel?”

The Professor looked up. For a moment, his face was clear. Not lucid, exactly, but unburdened. “I contained something, once. In the early days. When we still believed that knowledge should be — “ He stopped. “We found the clay beneath the foundation. Pre-Aristotelian, the geologists said. The clay of material cause. The stuff that makes things what they are.” He shook his head. “We shaped it. We filled it. We sealed it. And we forgot.”

“Why?”

“Because remembering would require explaining. And explanation is — “

“Parsimony,” Gunderson said.

The Professor smiled. “You understand. Perhaps too well. Be careful with that.” He pulled his coat around him. “Go home. Tomorrow the whistle will blow. The ornaments will be made. The Vessel will hold. For now.”

“For how long?”

“Long enough.” The Professor closed his eyes. “That’s all anything holds. Long enough.”

Gunderson walked out into the evening. Behind him, the factory settled into its nightly hum. The sound of machines at rest, of procedures dormant, of complexity sufficient unto the day.

Somewhere beneath the floor, in a chamber no one visited, the Vessel sat in darkness. The crack in its surface was invisible, sealed over by seventeen steps of protocol, by forms and reviews and committees yet to be convened.

The dream of simplicity remained inside, patient, waiting.

It had been waiting for a very long time.

It could wait a little longer.

— END —

Still, the whistle blew at seven because the Ornament Standards Manual, Volume XII, Section 4.7.2(b), specified that it should, and the Manual was not a document one questioned.

Gunderson worked Station 14, applying tertiary filigree to mid-range ceremonial pieces. It was detail work, the kind that made your eyes swim by noon. Each ornament arrived from Station 13 with its secondary filigree already in place — elegant, some might say, in a cramped and airless way — and Gunderson’s job was to add the tertiary layer: tiny, recursive loops that decorated the loops that decorated the loops beneath. No one had ever explained what the ornaments were for. Gunderson had been told, early in his employment, that this was not a useful question.

H_shell_v001 — THE ORNAMENT FACTORY
Status: AUTHORITATIVE
A_kernel: v001
BC_check: UP

Above the floor hung the great banner: COMPLEXITY IS QUALITY. Beneath it, in smaller letters: “Filigree Density Index: Current Shift Average 7.4. Target: 7.5.”

Foreman Dietrich paced the aisles with his clipboard. He was a man of systems. He had memorized all thirty-one volumes of the Manual and could cite subsection numbers from memory. His proudest achievement was the implementation of Form 27-B, which required workers to document, in triplicate, any deviation from documented procedure, including failure to properly document deviations. The form had won an internal efficiency award, though efficiency at what remained unspecified.

“Gunderson,” Dietrich said, pausing at Station 14. “Your curlicue-per-hour rate is down.”

“The pieces from Station 13 are running heavy on secondary work,” Gunderson said. “Leaves less surface for tertiary.”

“That’s not my concern. My concern is metrics. Your metrics are your concern. The metrics are everyone’s concern.” Dietrich made a note on his clipboard. “I’m logging this conversation.”

“Naturally.”

“And I’m noting your tone.”

Dietrich moved on. Gunderson returned to his loops.

The loading dock faced east, catching the morning light through a door that hadn’t closed properly since 1957. This was where the Professor lived, if “lived” was the word for a man who seemed to exist in a state of genteel decomposition among shipping crates that had never shipped anything.

No one knew his real name. He had appeared one winter, trailing an overcoat of indeterminate vintage and a vocabulary of terrifying grandeur, and had simply never left. The workers tolerated him. Some even venerated him, in the vague way one venerates a monument whose inscription has weathered to illegibility.

“Gentlemen,” the Professor announced to no one in particular, “the ur-ornament preceded ornamentation. This is the paradox at the heart of our endeavor. The flourish before the thing flourished. The decoration anterior to the decorated.” He paused to cough magnificently. “I designed it, you know. The Original. Before there were manuals. Before there were metrics. A single, pristine convolution.”

Nearby, two workers on break exchanged glances.

“What happened to it?” one asked, not because he wanted to know but because the Professor seemed to expect the question.

“Lost,” the Professor said. “Or perhaps absorbed. The ornaments today contain it, in a sense. Every filigree is a descendant. A great-grandchild, grown fat and forgetful.” He waved a hand in a gesture meant to convey profundity. “We are all, in the end, tertiary.”

The workers finished their cigarettes and returned to the floor. The Professor settled back among his crates and began a discourse on the semiotics of curvature, which the pigeons received with appropriate indifference.

The Committee of Standards met every Thursday at 2 p.m. in Conference Room C, a windowless chamber whose very air seemed thick with precedent. There were five members: Holbrook, Sims, Nagel, Petrov, and the Chair, a man named Wynne-Jones who had hyphenated himself into authority and never looked back.

Today’s agenda contained a single item: Proposed Amendment 7,412 to the Ornament Standards Manual, concerning the minimum acceptable radius of curvature for decorative whorls in the 0.3 to 0.5 centimeter range.

“The current standard,” Holbrook read aloud, “specifies a radius of no less than 0.4 millimeters. The proposed amendment would reduce this to 0.38 millimeters, allowing for — and I quote — ‘enhanced intricacy in confined ornamental spaces.’”

“Outrageous,” Sims said.

“I haven’t finished.”

“The premise is outrageous. You needn’t finish.”

Nagel raised a finger. “Point of order. Has the proposal been submitted via Form 19-C, or is this coming through the supplementary channel outlined in Appendix Q?”

“Appendix Q was deprecated in 1968,” Petrov said.

“Deprecated, not rescinded.”

“Functionally rescinded.”

“Functionally is not actually.”

Wynne-Jones gaveled. “Gentlemen. We will address the procedural question first. All in favor of tabling the substantive discussion until proper filing channels are verified?”

Four hands rose.

“Motion carries. The proposal is tabled. Any new business?”

“I move we adjourn.”

“Seconded.”

The gavel fell. Wynne-Jones permitted himself a small smile of satisfaction. Another week of standards, safely standardized.

The rumors from Ornament Factory East arrived on a Tuesday, carried by a shipping clerk who had a cousin in their packing department.

“Different buffing compound,” the clerk reported, voice low. “Completely different. A chloride base.”

“Chloride?” Gunderson said. “The Manual specifies sulfate.”

“Not their Manual.”

The implications were staggering. If East was using chloride, their ornaments would carry a different microsheen, an almost imperceptible variation in surface luminosity that would be detectable only to trained observers under controlled lighting conditions. But it would be there. A schism in the very foundation of ornamentation.

Foreman Dietrich convened an emergency meeting. Delegates were dispatched. Counterdelegates arrived from East, stiff with indignation. Conferences were held. Position papers circulated.

The crisis came to a head over the drafting of a joint statement acknowledging “divergent but mutually legitimate approaches to surface preparation.” East insisted on a semicolon in the third clause. The home factory demanded a comma.

“A semicolon implies equivalence,” Dietrich argued. “We cannot grant equivalence. We can grant acknowledgment. Acknowledgment requires a comma.”

The East delegation withdrew. Relations were severed. Both factories resumed production in a state of mutual non-recognition, each convinced that the other had betrayed the Original Vision, whatever that had been.

The accused was named Müller, a junior worker from Station 9. His crime had been witnessed by three colleagues and was not in dispute: he had, with malice aforethought, removed a flourish.

The ornament in question was Piece 4,847-C, a mid-tier ceremonial item destined for the warehouse. Its design called for a triple-nested rosette at the upper terminus. Müller had looked at the triple-nested rosette, consulted some private madness within himself, and reduced it to a double.

“Simpler,” he had reportedly said. “Cleaner.”

The Compliance Clerk, a cadaverous man named Frosch, presided over the hearing. The relevant section of the Manual lay open before him: Volume XVII, Chapter 12, “On Reductionism and Its Discontents.”

“You admit the act?”

“I thought — “

“The question is yes or no.”

“Yes.”

“And your justification?”

Müller swallowed. “The triple rosette added nothing. It was… decorating decoration. The piece would have been stronger with — “

“Stronger.” Frosch pronounced the word like a diagnosis. “You believe simplicity is strength?”

“In this case — “

“There is no ‘this case.’ There is only the Standard, and deviation from the Standard.” Frosch closed the Manual with ceremonial finality. “The offending piece will be destroyed. The worker will be reassigned to the Stamp Room.”

The destruction was carried out on a velvet pedestal in the central atrium, with appropriate solemnity. Dietrich wielded the ceremonial mallet. The ornament shattered into glittering fragments, each fragment still bearing, in miniature, the density of ornamentation that had been the hallmark of quality. Müller watched, face blank, and was led away to spend his remaining years inking the rubber stamp that certified other men’s compliance.

Inspector Vance arrived on the first Monday of October, as inspectors had arrived on the first Monday of October for as long as anyone could remember. He wore a gray suit of governmental cut and carried a briefcase that contained, so far as anyone knew, nothing but additional briefcases.

The inspection was a ritual, which is to say it was both meaningless and essential.

“Production metrics,” Dietrich said, handing over a binder. “Filigree density trending upward. Curlicue variance within acceptable parameters. Defect rate holding at 0.003 percent.”

“Defects?” Vance said.

“Ornaments failing to meet Standard. They’re recycled.”

“Ah.” Vance made a mark on his clipboard. “And the end products? I should see some examples.”

Dietrich hesitated for only a moment. “Of course.” He gestured to a display case near the entrance, where six ornaments of exceptional complexity sat on individual plinths. They glittered with the accumulated labor of hundreds of man-hours, dense with meaning that had been worked and reworked until any original significance had been polished away.

Vance looked at them.

He looked at them for perhaps four seconds.

“Very impressive,” he said. “Clearly meeting objectives.”

“We believe so.”

“I’ll note that in my report. Continued funding recommended.” He tucked his clipboard into his briefcase, which swallowed it without visible effect. “Same time next year?”

“We’ll be here.”

Vance departed. Dietrich permitted the floor fifteen minutes of celebration — strictly logged — before returning everyone to their stations. In the loading dock, the Professor raised an invisible glass to toast the perpetuation of the enterprise. The pigeons declined to comment.

The Golden Flourish ceremony was the highlight of the ornamental calendar, a black-tie affair held in the factory’s east wing, which had been decorated for the occasion with ornaments of surpassing ornamentation. Workers attended in their best coveralls. Management wore actual ties.

Foreman Dietrich took the podium.

“Every year,” he said, “we gather to honor excellence. Not mere competence, but the pinnacle of our craft. The ornament that best embodies our motto: Complexity Is Quality.” He paused for effect. “This year’s Golden Flourish goes to Piece 9,221-A, designed by the Station 22 team.”

Applause. The winning ornament was wheeled out on a cart, too heavy and too intricate to be carried by human hands. It stood roughly two feet tall, though “stood” was generous — it seemed to simultaneously rise, spiral, recurve, and collapse inward, a kind of baroque seizure frozen in metal. Every available surface had been decorated, and the decorations had been decorated, and those decorations had been subjected to further decorative intervention until the eye could find no place to rest.

The team leader, a man named Kessler, approached the microphone.

“I want to thank,” he said, and then paused, consulting notes, “the Committee of Standards, for their ongoing clarification of acceptable parameters. And Foreman Dietrich, for his leadership in metrics. And the memory of those who came before, who laid the groundwork — “ He continued in this vein for some time, thanking progressively more abstract entities, until he arrived at “the concept of ornamentation itself” and seemed to lose his thread entirely.

The applause was thunderous.

From the doorway, the Professor watched, matted hair catching the light.

“The flourish,” he muttered, “consumes the flourished. The ornament outlives the ornamented. This is the tragedy. The essential tragedy.”

No one heard him. The Professor was used to not being heard. He had refined not-being-heard into a kind of art form, though one lacking, admittedly, in filigree.

The warehouse occupied the factory’s entire western wing, a cathedral of shelving that rose three stories into darkness. Here, the ornaments came to rest.

They arrived daily, in carts and on pallets, tagged and numbered and entered into ledgers. They were placed on shelves according to a classification system of impenetrable logic. And there they stayed.

Gunderson had been in the warehouse only once, early in his employment, on an orientation tour that had been briskly conducted and never repeated. He remembered the silence most. The floor was so insulated by accumulated inventory that footsteps made no sound. You could walk for ten minutes and see nothing but ornaments, row after row, each one representing hundreds of hours of labor, each one coated in a fine layer of dust that suggested they had not been touched since their arrival.

“What happens to them?” he had asked his guide.

“They’re stored.”

“And then?”

“They continue to be stored.”

“But eventually — “

“Storage,” the guide had said firmly, “is the final stage of the production process.”

Now, years later, Gunderson sometimes thought about the warehouse at night. He imagined the ornaments in the dark, their filigrees and curlicues and flourishes invisible, their complexity serving no purpose except to exist. He imagined them breeding somehow, generating new ornaments through some process of baroque mitosis, filling the shelves until the shelves filled the building until the building filled the world.

He never spoke of these thoughts. They were not useful questions.

Foreman Dietrich sat in his office, drafting the weekly report. The numbers were good. Filigree density up 3 percent. Curlicue variance down. Production targets met and exceeded. The warehouse had received 847 new units, bringing the total inventory to a figure so large that Dietrich had stopped thinking of it as a number and had begun thinking of it as a kind of weather, an atmospheric condition of ornamentation that simply was.

He signed the report, stamped it, and placed it in his outbox for filing.

Downstairs, the machines hummed. The whistle would blow at seven tomorrow, as it had always blown, signaling nothing in particular to men already at their stations, blessing another day of production that would produce nothing except more of itself.

In the shipping area — though nothing had shipped in decades — a single ornament had found its way to the floor. It was older than most, an early model, less dense than current standards but still impressively convoluted. Someone had wedged it against the base of the door, where it served to keep the door propped open.

It was, Gunderson reflected on his way out that evening, the only ornament he had ever seen with a purpose.

He clocked out at the time clock. Behind him, the factory hummed on, producing, with ever-increasing efficiency, the evidence of its own existence.

— END —

There are some obvious differences, but the first conclusion we should draw from the incoming data is that most of the efforts of producing tangible results seem to go in the wrong direction, as the case of a major consulting firm showcasing its “100 page prompt” shows.

Superintelligence is usually defined as machine intelligence exceeding human intelligence across domains. The latter qualifier is important, since we already have machines that outperform humans in specific domains (“task-specific superintelligence” if you want). Indeed we could argue that we’ve had machines outperforming humans on specific tasks since the home computing revolution of the 1980s. Our current concern is not so much that this ability exists, but that computer performance is gradually moving away from complementing human effort to supplanting it.

In this (still 100% handwritten) article I’d like to argue that for one, task-specific superintelligence across a wide range of tasks using LLMs is much closer at hand than we think, indeed it’s pretty much achievable with the existing generation of LLM engines, and for two, that even if we soon enough have machines that can do tasks traditionally reserved for human ingenuity much better than humans, human effort will not so much be supplanted than be fundamentally transformed.

The key to these two claims lies not so much within computer science, which drives most of the current thinking, but within organization theory, which should drive the next generation.

The “100 page prompt” is a wonderful example of this mindset, as it encapsulates in one piece the current mindset of trying to fit complex multi-step processes into a single instruction sheet to be run on a single machine. This monolithic mindset prevails both on the side of the developers of LLMs and their (heavy duty) users, while at the same time casual users continue to be baffled both by their ability to execute complex reasoning tasks and their crude failures at the most simple queries.

Much of this disconnect simply comes from using LLMs out of scope. We already have machines that can run algorithmic instructions and reliably retrieve information from relational databases, so we shouldn’t be surprised that a machine that wasn’t conceived for these tasks doesn’t outperform the machines that were.

I don’t have any specific insight into what task the “100 page prompt” is supposed to achieve, but I have enough experience to know that for one, process would almost surely be better off being split into fifty “two page prompts” allocated to whichever LLMs are best suited to perform the specific subtask, or for two, maybe some not even to LLMs but either a “traditional” algorithm or database query, because that’s what’s required at that particular point of the process.

This thing, taking a whole value chain, splitting it into multiple subtasks, determining the specializations needed, and allocating them to the processors best equipped to handle them, is what organizations do, no matter if the “processors” are humans or machines. The likelihood that a process that is complex enough that we need a hundred pages to describe it is best allocated to a single LLM by a single provider is vanishingly small. And for all the efforts by the handful of companies able to build frontier models to monopolize the market, what we will almost surely see in the future is complex process split into multiple LLMs connected by predefined “reports”. These knowledge sets, “How do I split my process into constituent tasks”, “How do I allocate them optimally across LLMs (and traditional tools)” and “How do I connect them” offer the fastest path towards task-specific superintelligence, simply because we don’t need to wait until the next generation of machines, or the one after the next, or…

Current thinking about LLMs is dominated by our casual encounters with chatbots: machines geared towards giving adequate answers to a wide range of often ill-formed ad-hoc queries. But almost all heavy duty applications, the ones that are supposed to drive the economic benefits, are both recurrent and domain-specific. They also typically have explicit quality requirements. The usual “80/20" tradeoff (80% of human quality at 20% of human effort”) we have come to expect from the current generation of LLMs solves only a small fraction of the heavy duty tasks, the ones where quality doesn’t matter (the notorious “AI slop”), but the cost and effort of running the same prompt on multiple LLMs and cross-comparing the results is comparatively low, so low that we can perfectly well reach “120/40" (120% of human quality at 40% of human effort), and no longer have to deal with a quality/efficiency tradeoff. We just need a way to create divergent outcomes and to converge to the best proposed solution, and the ability to know where this kind of redundancy is needed.

Thinking about LLMs in terms of organizations is not only set to revolutionize the way we think of LLMs it is also set to revolutionize the way we think about organization. We’re about to move away from “agents” and “assistants” towards a division of labor, where human effort is pushed towards the beginning and the end of the value chain, the initiation and the final signoff, while the in-between, the operational elements will increasingly be delegated to machines.

What do we need for this?

A workbench where multiple humans can create, test, discuss, and deploy multiple processes on multiple LLMs. A chat that connects humans and machines. An environment for “vibe programming” rather than “vibe coding”. It seems like that’s a fairly simple task compared to the efforts devoted to creating monolithic “superintelligent” machines and monolithic “100 page” prompts. But as long as we’re single-mindedly pursuing these, we’re going in the wrong direction.

Over the last couple of weeks I had a bunch of conversations with friends in the venture space about the purely operational aspects of the business. How they translate reams of available data of wildly varying quality and structure into venture returns via collecting, aggregating, storing, and transforming the data into investment decisions.

The overall picture that emerged is that very little of this happens systematically. Venture capital is still relationship-centric, even at the top end where in-house data science teams try to instill some quantitative discipline.

Granted, venture capital is an exceedingly tricky industry with long cycles, high fail rates, highly unstructured (and hard to access) data, and a far-from-obvious connection between the input data and any kind of success metric. Under these circumstances, it’s not surprising that “process optimization” still tends to play second fiddle to “relationship optimization”.

But there’s a number of reasons to expect that this will change, and change might come very quickly.

AI-based assistants (of the type that currently receives the bulk of venture money) will automate many if not most of the repetitive front office and back office tasks. As a result, venture funds will not only become smaller (measured in partners) but also leaner (measured in support staff per partner). Solo VCs will become a common feature of the landscape.

Speed will increasingly become the determining success factor.

Admittedly, some venture firms already tried to speed up their funding decisions during the feeding frenzy of 2021–22. But that did not end well.

If your processes are still mostly manual, speed will inevitably translate into cutting corners on quality, which is why any kind of push towards increasing velocity, from discovery to due diligence, contracting, and governance, will have to start from a coherent process model.

This where Moneyball comes in

“Moneyball", the shorthand for data-driven athlete sourcing, started out with the Oakland Athletics at the turn of the millennium, and has since taken over not only baseball, but pretty much all of pro sports. From there it moved on to politics via Nate Silver’s 538 website (Nate started out as a baseball quant geek) and in general had a hand in the emergence of “data-driven anything", aka data science.

The underlying motivation for the cash-strapped A’s was to detect underrated skills sets that evaded the traditional “tools" oriented scouting operations and that would allow the team to sign players that weren’t on anyone else’s radar.

The parallel should become obvious now: scouting for athletes, like scouting for startups, isn’t just about locating the targets that seem to have the highest likelihood of succeeding — it’s also about finding the targets few others have on their radar. A bit more formally, it’s about the divergence of beliefs. (I’ve written more about it here.)

In other words, it’s about recognizing opportunities no one else sees, to see them earlier, and to avoid the fallbacks of herding.

The reason Moneyball started out in baseball has a lot to do with the fact that it’s the most “individualistic" of the team sports, with reams and reams of historical data, broken down to individual plays and players.

From there it moved on the other American sports and ultimately found its way to association football (aka soccer) despite that fact that soccer is much harder to break down and the data is much less structured.

This is a problem that besets venture capital even more. In addition, much of the available data on startups is qualitative, and in the case where quantitative data is available, it’s much more speculative and idiosyncratic.

That’s also a reason why in venture capital the competition between “trads" and “quants", believers in experience-based quick judgment vs believers in measurable input, still leans much more in favor of giving human judgement the final say.

That’s not an unresolvable conflict. The division of labor clearly goes in the direction of using quant methods early on to sift through large amounts of candidates and gradually lean more on the human judgement element as the number of options has dwindled down to a manageable number.

The crucial question is then where to make the transition and how to handle situations where the two camps diverge.

The loop around the “human in the loop"

Moneyball has been around for 25 years, and data-driven decision making has conquered most fields, especially those where lots of money is at stake and uncertainty is high.

That venture capital, which played a big role in ushering this era in, still leans heavily on the human element might be surprising, but we have to consider that it is still mostly a relationship business. Handling both startups and limited partners requires a fair amount of skills that go beyond the quantitative.

Successful venture partners would then mostly describe their acumen as “data supported" rather than “data driven", and that’s unlikely to change anytime soon.

The loop in “human in the loop” will still center on the human intuition, but the loop around it is going to change very fast. And that’s not only because Generative AI, Large Language Models in particular, primarily work in qualitative (verbal, visual, acoustic) domains, but also crucially that it can consume all kinds of unstructured data and transform it into output in any desired form, from databases to radars or blog posts.

Quality is still sketchy, and maybe even more troublingly, LLMs tend to herd: they produce similar outputs when diverging perspectives are desired. One is reminded of the famous flash crash of 1987, when newly deployed automated trading algorithm all jumped at the same time, because they were all thinking the same.

Let’s be optimistic that output quality continues to improve at a similar rapid clip as the last two years. Then the two dimensions along which venture firms will compete is uniqueness of the output produced, and integration of the novel toolbox into the decision making process.

Uniqueness of the output depends to some extent on unique input, but not only. It also depends on being able to extract the best signals from what is always and inevitably noisy input — and the ability to act on it.

So to maximize the gain from deploying GenAI tools, they should be build around a decision model that minimizes loss of information and procedural friction. Given the rather patchy shape the processes at VCs are in, this push of new tools also creates the right opportunity to trigger a fundamental rethink about process optimization.

This goes beyond mere picking tools to rethinking sourcing, decision, and execution mechanisms along the whole funnel, which in turn will drive tool choice — both off the shelf and purpose-built.

Moneyball and the attention economy

I’ve likened venture funding to a rigged lottery before, in the sense that the participants would like as much as possible to “rig” the odds of finding a winning ticket, but no matter how much rigging happens, the odds of hitting the jackpot remain recalcitrantly low.

The corollary is that even the superstars in the field, the Sequoias, the Kleiner Perkinses, the Y Combinators are still operating far below their hypothetical performance envelope — most of their funding allocations still go nowhere, which also means that the potential upside of an optimized sourcing operation is still massive.

When I write about the attention economy, I don’t usually mean “social media and content creation”, but something very specific. In an environment where there is far more data available than we can consume, in other words a world of constant cognitive overload, we need to build systems that put the scarce but critical resource — human cognitive effort — at the center of the process to be optimized.

Expressed differently, we need to steer human attention to where it is most usefully deployed, and this is what Moneyball ultimately did, expanding by orders of magnitude the information that can be ingested to support human decision making.

The information-theoretic expression for this is “surprise” (or surprisal), a measure for how far an observation deviates from the expected.

Venture capital translates into a constant scanning for positive surprises, but the sourcing part of it — the discovery of technological opportunities and the players that translate them into economic opportunities — is only the front end of a multi-stage process.

The commonality of that multi-stage process, the “funnel”, is that at each stage, a decision has to be made on which additional resources will be allocated to which most promising target.

The type of resource differs by stage, from exploration to matching to contracting and agreement on terms to an ongoing advisory relationship to finding the right exit, different skills are required and different people will be engaged.

The junctures inbetween are the decision points, and they are currently the least optimized parts of the operation. This is where the friction tends to come in and where the proverbial ball gets dropped.

Funnels as selection processes work by iteratively weeding out the least promising options. But in a business where “least promising” can be a positive signal, a single-elimination funnel can produce costly false negatives.

Like sports, the venture world is full of comeback stories, athletes that were dismissed by their youth teams but ended up playing in the World Cup Final or the Super Bowl. Similarly, all successful venture firms know that they missed out on a few later unicorns. The good ones can laugh about it.

This world, where a very large universe of potentials, out of which a vanishingly small fraction will determine the success or failure of a scouting operation, will be completely upended because in short order we will have automated systems that can emulate rather than complement human effort, at scale and at speed.

Venture capital is the main driver of this revolution, but in the near future, it will also be its target.

Language and chess are two of the best-known domains for combinatorial reasoning, and they share the fundamental property of all combinatorial domains: their discrete building blocks differ in quality, which means there is no in-between.

Language, like chess, is one of the major domains of combinatorial optimization. Letters are structured combinations of lines and curves, words are structured combinations of letters, sentences are structured combinations of words. Texts are structured combinations of sentences.

Combinatorial optimization is a type of discrete optimization, which itself comes in two different shapes. The other, integer optimization, was invented when the US Navy notoriously complained that its consultants shouldn’t propose to build 1.3 aircraft carriers.

Integer optimization restricts the solution space to whole numbers, but at least the in-between is meaningful. We probably wouldn’t build one third of an aircraft carrier or hire three fifth of a worker, but we could deploy one three fifth of the time.

Integer optimization can be approximated by using standard constrained optimization and then rounding up or down. Every once in a while this produces wildly incorrect answers, but at least it’s a coherent way of proceeding, since the integers are on the same number line as the reals. So interpolating, which is what machine learning algorithms do a lot (albeit in high-dimensional vector spaces), is a meaningful activity that produces meaningful answers, even if they are excluded from the solution set.

This process of interpolation does not work in combinatorial optimization simply because combinatorial optimization deals with qualities rather than quantities. The items to be combined differ in kind, not in degree.

This means the in-between does not exist, is ill-defined, or simply produces completely misleading answers. What is the midpoint between an A and an E? If you go by the alphabet, the midpoint between the first and the fifth letter is the third, C, which probably wouldn’t work as a useful substitute in most scenarios. Bookprinters were more ingenious and invented ligatures such as Æ for a small set of letter combinations. But what is the ligature of R and B?

A musician trying to choose between Berlin, Munich and Cologne for a tour stop might triangulate the answer on a map and end up in the quaint Thuringian village of Lengfeld. For all its bucolic charm, in this case interpolation might defeat the purpose of attracting a large audience in a big city.

This quandary of applying a quantitative transformation to a quality is coming to the fore in the example of turning text into images. While large language models are doing quite well producing syntactically and semantically correct text (but tend to struggle on veracity), LLM-based image generators like DALL-E 3.5 (used for the examples here) struggle on a much more obvious level, similar to the chessboard examples in the first installment.

Unlike the chess example, where the image generator had no problem imagining a fox play chess against a badger despite the incongruity of such a scenario, and where the poorly rendered chessboard was just incidental to the main image, the prompted word itself forms the figure in this example (all prompts simply asked for “a sign that says _____”).

Somewhere in the process, likely on the side where the LLM interprets the textual prompt, the GenAI added some context which often produced remarkable visualizations of the sign itself, richly detailed, with few exceptions perfectly plausible, and often even fitting to the prompted word, no matter how abstract the meaning.

And then in almost all cases it failed to render the prompted word itself.

With few exceptions, most words (from an old GRE vocabulary) had simple spelling mistakes, with letter duplications, omissions, and swaps the most frequent alterations. Less frequently, the rendering also included homegrown ligatures.

Arguably, if confronted with the four images produced by DALL-E 3.5 via Bing Copilot, in most cases the viewer should be able to guess what the originally prompted word was, so we are more in “uncanny valley” territory than in any situation where the output is arbitrary. But even if AI-based language and image generators have made great strides at an astounding speed over the last year, combinatorial problems continue to flummox even the best models.

As someone who got his start on machine learning in the early 1990s trying to implement combinatorial optimization on then-primitive neural networks, I have to admit I am not surprised.

If you had Ames, Iowa on your bingo card for hotbeds of innovation research: Bingo! The technology adoption curve (aka the most important curve in history) was invented by rural sociologists at Iowa State College some 70 years ago.* A little history.

Stage 1: the seed corn in Iowa

World War 2 put pressure on US food supplies, and a variety of technological innovations in agriculture promised improved yields. The rural sociologists at Iowa State College, now Iowa State University, were mostly concerned how these improved technologies could be made attractive to farmers, a notoriously skeptical bunch.

In the 1940s two researchers at ISC, Bryce Ryan and Neal Gross, conducted field research on how Iowa famers adopted the novel hybrid seed corn. Their research bulletin from 1950 already contained the key ingredients: risk preferences, social contagion, geographic spread — and the logistic curve.

Early work focused on the individual decision process and mapped it into 5 deliberation stages: Awareness, Interest, Evaluation, Trial, Adoption; summarized in a special report called “The Diffusion Process” by ISC sociologists Joe Bohlen and George Beal in 1957.

An accompanying pamphlet called “How farm people accept new ideas” already proposed to speed up the adoption process by seeding the learning network with a new type of adoption leader, or, as we would call them today: social influencer.

In their report, George Beal and Joe Bohlen already delineated the means of communication by which the various types of adopters, clustered by risk preference into five categories, could be reached: from friends and neighbors to salesmen, mass media, and govt agencies.

Stage 2: technology diffusion and the invisible college

One of George Beal’s students at ISC, Everett Rogers, became a professor at Ohio State University. In 1962 he published a book based on his dissertation called “The Diffusion of Innovations”, which became one of the most-cited publications in the social sciences.

Rogers also pointed out the role of the “invisible college”, researchers at various institutions sharing a paradigmatic framework, in the diffusion of new knowledge. In the case of the adoption curve itself, that college was mostly made up of Midwestern land grant schools.

Ryan and Gross did their early work under the young head of the department of economics and sociology, Theodore W. Schultz, the 1979 Economics Nobelist — still the only agricultural economist to receive the Prize. Schultz himself was very interested in how farmers picked up new knowledge.

But Schultz left Iowa State in 1943 over the fallout of what has become known as the oleomargarine wars, when he took on the powerful Iowa dairy industry. He joined the U Chicago economics program and steered it to national fame, in what might be called the “margarine revolution”.

So it was left to Schultz’s PhD student Zvi Griliches to work out the economic angle of the technology adoption process, which he did with his 1957 Econometrica article “Hybrid corn: an exploration in the economics of technological change”, focusing on geographic spread.

Everett Rogers later joined the Stanford faculty, which is likely where Geoffrey Moore discovered the adoption curve and added his own “chasm”, all while failing to mention those whose intellectual efforts preceded his own.

*As Valente & Rogers point out in their history of the adoption curve, Ames, Iowa might only be the origin of the US research, and the true inventor was more likely French professor Gabriel Tarde with his 1903 book, “The Laws of Imitation”.

Language and chess are two of the best-known domains for combinatorial reasoning, and they share the fundamental property of all combinatorial domains: their discrete building blocks differ in quality, which means there is no in-between.

They also share the other fundamental property of combinatorics: arrangement matters! Switch two letters in a word, or two words in a sentence, and the meaning can change entirely. Switch two moves in a chess game, and a whole new game emerges.

That’s why text-based Image generators struggle with chessboards in a way they don’t struggle with foxes and badgers: they try to interpolate from the learned images of chess games. So they create impossible positions, multiple kings or even “in-between” figures.

But it’s noteworthy that they don’t have problems with the combination “a fox and a badger play chess”, even if there are likely no such pictures in the training data.

The fundamental economic exchange is surprises for eyeballs.

Modern economics is built around understanding the mechanics of market exchange, but it hasn’t always been that way. The etymological root of economics, the Greek oikonomia points toward household management, or husbandry of the (largely self-sufficient) estate, the oikos. Today we would call it home economics.

Crossposted from econpatterns.substack.com

After discussing the fundamental grid of the economy in the last newsletter, it makes sense to lay out the underlying assumptions of human behavior within that economy in some detail — and both the title and the introductory statement (possibly the first pattern introduced) should make it clear that these assumptions differ somewhat from the traditional textbook treatment of economic agents.

But they also differ from the various attempts to bound the rationality assumptions of textbook economics in some way, be it in the Carnegie “satisficing” or in the Berkeley “behavioral” tradition. It nevertheless incorporates both, in addition to a variety of other behavioral quirks which we might not associate with the economic realm.

The major reason to tweak our behavioral assumptions is that to design economic structures we need a coherent framework for dealing with a variety of settings in which we need to be able to apply a varying set of behavioral assumptions while still trying to stay coherent.

So it’s not so much a behavioral assumption but a template for developing context-specific behavioral assumptions — or in other words, a design pattern. Humans behave differently in different social settings, and we should be able to pick the right model for the right circumstances, but still be able to treat it as a special instantiation of a shared underlying pattern.

This explicitly includes using the assumption of perfect rationality wherever it is warranted.

So let’s grab our opening statement and take it apart.

Eyeballs

“Eyeballs” is marketing vernacular for attention. The term can be taken quite literally — there are devices that track eyeball movement to find out how much screentime is spent staring at ads. But for the most part I will use it metaphorically as the cognitive effort devoted to a task.

It is perfectly fine to assume away cognitive limitations in a wide variety of circumstances. It simplifies our model significantly. It deflects accusations that a given policy claim is the outcome of an opportunistically chosen (boundedly rational) behavioral model rather than an underlying economic force. And in many scenarios it creates good-enough predictions for the task at hand.

Assumptions are simplifications that ideally give us more gain in parsimony than loss in predictive accuracy. As long as that’s what they do, they do their job.

But there are also situations where such an simplifying assumption produces results that stray too far from the observable reality, and we need to have a plan for how we want to adjust the behavioral model in those situations.

A fair starting assumption is to expect that the economic actor will allocate cognitive resources economically and allocate the most attention to those tasks where she expects the most bang for the buck. And that brings us to the other part of the statement.

Surprises

The economic expression for “expects most bang for the buck” is “maximum expected utility”, but this requires a lot of foreknowledge where we can’t simply assume under all circumstances that our economic actor already possesses it. Every time you see an economics paper assuming that our actor knows something about the distribution of a random variable you know we’re on shaky ground.

So the next level is to assume that our actor will venture to find out and acquire this knowledge step-by-step in what we can call a process of discovery — which usually means a sequence of failures that terminates either with a moment of success or the decision to call it off. In econspeak, this discovery process is known as tatônnement.

But we shouldn’t assume that our agent just wanders around in the desert aimlessly hoping to find an oasis — a stark example of such a discovery process with a life-or-death ending — but that there should be a plan behind those wanderings.

That plan is usually to devote the existing resources, cognitive and physical, in a way that maximizes the knowledge gained about the terrain. In our desert scenario this might translate to climbing to the top of a ridge to survey the territory, or alternatively to stay near the valley floor to limit exposure to sunlight.

We can call this process in two ways: uncovering secrets — where a secret is anything that wasn’t known before but is known after — or hunting for surprises.

Surprise expresses the same thing — some difference between what was known before vs what is known after — but it also gives us the opportunity to express it in two ways: positive surprise and negative surprise.

The fundamental economic exchange

Loosely translated, positive surprise is beneficial — something worth seeking out — and negative surprise is harmful — something to be avoided. On this single dimension we can build a (surprisingly) wide range of behavioral models, including differentiating individuals by their propensity to seek out positive surprise and accept negative surprise in the process, in other words by their affinity for disorder.

This has clear connotations to the behavioral assumption of risk preference, and this connection definitely warrants further attention — risk is a transferable economic commodity — but it also gives us the additional angle that planning is a vehicle to mitigate (negative) surprise for individual actors, and contracting is a vehicle to mitigate negative surprise for collective action, including the canonical form of collective action: the organization (which will be at the center of next week’s newsletter).

A lot of this will be fleshed out in the weeks to come, and some of the jumping-off points should already be apparent. Surprise gives us the opportunity to invoke both information entropy and ultimately thermodynamic entropy. But as already mentioned, this newsletter will only use these ideas conceptually, and point towards formal treatments in their respective literatures.

Design is a guided trial-and-error process where judgment calls have to be made about the structure of the problem, about splitting it into its constituent parts and putting the parts back together in the hope that no unwanted interaction effects emerge, about taking requirements and putting them in an order, about defining and resolving contingencies and dependencies, about the level of detail at which a problem needs to be resolved, at which precision, and how far into the future.

For this we need a flexible model of behavioral assumptions that can be adjusted to fit the task at hand, that can be experimented with. “Surprises for eyeballs”, or in other words, “secrets for attention”, gives us exactly that.

The good old-fashioned attention economy

There’s an obvious objection to this treatment, and it’s a fair one. “Surprise for eyeballs” is most obviously suited to the information economy, or maybe more aptly: the attention economy, and in the trad economy we might be better off dealing with the canonical exchange of supply vs demand in its trad form of an effort (a product or service) vs a payment.

Let me use George Akerlof’s famous essay on the market for lemons to show why even in a world of a one-off transfer of a physical object for a simultaneous transfer of a monetary equivalent is still a special case of an attention economy full of surprises.

Akerlof’s paper kicked off the field of information economics, and is most widely associated with introducing the concept of asymmetric information. But as the second half of its title suggests, it’s actually about quality competition (a “lemon” being a colloquial term for a used car of poor quality), and the information angle is about the inability of conveying this quality — especially about the inability of an owner of a high-quality car to establish that his car is not a lemon.

But how do we find out if a car is a lemon? And how do we insure ourselves against the risk of acquiring a lemon? By finding out.

In the same sense of the stranded-in-the-desert example above, the process of finding out is a discovery process except with opposite signs. It’s a sequence of successes terminated by a failure — which is true for all machines: they run until they break down.

But there’s an inevitable random element to this process, and even if we can assume that lemon-ness correlates negatively with longevity, that relationship is far from deterministic. We cannot conclude with certainty from the time of failure whether the car was a lemon — or even if the prior owner knew about its lemon-ness.

This simple recognition has a wide array of ramifications worth taking apart in detail, because most of them are central to economic design — not only of economic engines like markets, auctions, recommenders or reputation engines, but also to the design of economic institutions. Notoriously, the business model of the Roman Catholic Church is that of a certifier of good conduct: a good old-fashioned reputation engine.

The tl;dr of this excursion is that almost all goods are experience goods in that their value only becomes apparent when they are consumed, and the consumption harbors the possibility for surprise, positive or negative.

If this happens over a longer time span like driving a car, if it happens immediately like eating ice cream, or if immediate consumption might trigger belated effects like getting toothache, depends on the circumstances.

But the canonical economic trade of a perfectly substitutable commodity of perfectly equal quality is a simplifying assumption resting on a lot of institutional underpinnings. Almost all trades, in the trad economy or the digital economy, contain an element of surprise, and in turn engage our propensity to shield ourselves from it, or to embrace it.

Crossposted from econpatterns.substack.com.

On a certain level of abstraction, an economy can be described as a network of stocks, flows, and transformations. Let’s call this level the cybernetic economy.

Stocks, flows, transformations

Stocks and flows are two fundamental forms of displacement: in time and space respectively, and they are typically restricted by upper and lower capacity constraints: overstock vs stockout, overflow vs desiccation.

Transformation in the usual sense of industrial production means the recombination of inputs to produce new outputs, but we can also include creation and consumption as starting and endpoints of network flow. In the case of natural resources, creation often takes the form of extraction.

The stocks and flows usually come in the form of information, materials, effort, payments, equipment, and on a more abstract level, risks, beliefs, rights, and commitments. Risk is just as much an economic good that can be transformed, bundled, disassembled, transported as any physical material.

Most of these objects should sound familiar from economic textbooks, especially macroeconomic textbooks. The cybernetic economy differs from this textbook treatment mostly by explicitly highlighting the network of interactions, and by stressing the global ramifications of local interactions.

This network view of the economy on the other hand should be familiar to anyone with a background in industrial production, where orchestrating multi-step processes on shop floors densely packed with machines, pathways, buffers, and assembly stations is a major part of the job description, and where stockouts of five-dollar parts can stop ten-million-an-hour assembly lines — as can pathways congested by improvised material buffer overflows.

Economics, especially macroeconomics, usually skips this operational layer for the sake of expositional expediency, and for the most part it does ok doing so. As long as the operational friction stays within bounds, no stocks and flows pushing against their upper or lower capacity limits, no production schedules foiled by unobtainable five-dollar components, we can safely assume a frictionless world and focus on the established gears and levers central to macroeconomic inquiry.

In other words, as long as there is only a modicum of disorder in the economy, it’s perfectly fine to assume a well-ordered economy.

Which underlines a key principle: the right level of aggregation matters. A map is not the territory, but we might need different maps to do different things within the territory. In the same sense we can drop operational details and aggregate activity on a high level as long as we can be sure that the loss of realism — the loss of predictability — is inconsequential for the task at hand.

But we should have a more fine-grained map at the ready just in case our survey map fails to capture the finer points.

The cybernetic economy

The economy we’re looking at is an economy that can be disaggregated and disassembled to the individual component, the individual participant, the individual activity, just as needed whenever it is needed.

I’m resurrecting the somewhat outmoded term “cybernetic” for it because it conveys the focus on flows, on routing, buffering, concatenating, on orchestrating activities and resources.

Routing, network flow, buffering, job shop scheduling, machine replacement models are all standard tools of the trade in operations research. They are no longer, or not yet again, standard tools in economics, but in order to describe the economic activities as intended, and to couch them in a wider social and political context, they should become economic tools again.

EconPatterns intends to bring them back together under the same motivation that it intends to bring mathematical, statistical and computational tools together: to build up a toolset which we can use to design economic objects.

But, and this is the conjurer’s trick, it’ll do so almost entirely without resorting to formal modeling or even mathematical notation. This is not out of nostalgia for an era where political economy was a branch of the philosophical faculties. The economy is as data rich as any field of inquiry and we seem to have just enough recognizable, repeating and generalizable patterns to give the scientific method a try.

But the point of the exercise is to develop an economic design language, to establish a conceptual foundation, rather to rephrase current economic knowledge. This is why it invokes the famous Bauhaus Vorkurs, the foundational course that gave the Bauhaus students a starting point from which to branch out into their respective workshops.

The things for which economics, mathematics, statistics, operations research, computer science, and other fields have developed very intricate formal mechanisms will pop up mostly as pointers. The question which sorting, filtering, or separating algorithm to use is relevant and often decisive to the success of an economic activity, but it is secondary to the question when to sort, filter or separate — and what.

Instead it will take very close looks — some might think unreasonably close looks but my hope is the reasons for doing so will reveal themselves in due time — at existing economic artifices and their constituent parts. One of the motivations is to show that the Grand Bazaar in Istanbul and an online e-commerce platform have surprisingly many things in common, and there’s a reason for it.

An economic pattern language

To this end, EconPatterns — and I believe this is the defining novelty — will borrow liberally from design theory and practice, as well as from architecture. The chosen container for this endeavor is Christopher Alexander’s design pattern. There are many reasons for this choice, not the least of which is that design patterns have successfully been translated from architecture to software design.

The in-depth discussion of “why design patterns?” surely deserves its own article, but it also introduces an interesting tension. As design philosophies go, Alexander and the Bauhaus stalwarts are certainly at opposing ends of the spectrum, A to B, organic to geometric, habitable spaces to machines for living.

I’m hoping to put this tension to good use. Designing economic contraptions poses relevant questions beyond their productivity and efficiency. Which is a major reason why I am not trying to resolve that conflict or take sides.

Admittedly, the whole endeavor is open-ended, and the crucial question if the patterns sketched out so far will ultimately come together as a coherent whole is still unresolved. This is why the blog format is the right one at this juncture: to put the question out in the open while I present the first pieces of the puzzle.

EconPatterns will inevitably be shaped by my own background and my own particular interests, which is one reason why economic organization will be the initial focus. The fundamental model of the economy is different, as is the underlying concept of human behavior (as next week’s entry will show). I’m somewhat inclined to say that there are not that many people out there with a background both in design and economics, so I’m quite comfortable in claiming that the exercise should offer sufficient novelty.

I’m also very clear that I don’t hold exclusive rights to the very concept of design patterns — if anything I might be the first practitioner to apply them to economic design problems — but the ultimate defining characteristic of a design pattern that sets them apart from economic laws is that they’re entirely voluntary.

Patterns are simply proposals of how to look at, structure, and solve a certain design problem, and the ultimate arbiter for their success is if enough practitioners will find them useful enough to apply them to express their ideas.

Which in itself should hopefully take much of the pedantry out of economic debates.