Ką apmokestinti, kai dirbtinis intelektas atlieka vis daugiau darbo: tai vienas svarbiausių mūsų gyvenimo politinių sprendimų

 

„Didelis technologinis pokytis sukelia didelius socialinius pokyčius. Dirbtinio intelekto dėka esame ant produktyvumo stebuklo, galinčio radikaliai pagerinti žmonių gyvenimą, slenksčio – bet tik tuo atveju, jei imsimės veiksmų dabar, kad išvengtume blogiausio dirbtinio intelekto poveikio.

 

Mūsų vadovaujama turto valdymo įmonė „Bridgewater“ jau daugelį metų tiki dirbtiniu intelektu ir į jį investavo, pertvarkydama įmonę, kad išnaudotų kuo daugiau dirbtinio intelekto galimybių. Tai reiškia, kad mums neproporcingai bus taikomi čia rekomenduojami mokesčiai ir reglamentai. Šias rekomendacijas pateikiame bet kokiu atveju ir prašome politikos formuotojų skubiai jas įgyvendinti. Turime trumpą laiką, kad užtikrintume žmonių darbo apsaugą ir suteiktume kiekvienam piliečiui galimybę prisidėti prie dirbtinio intelekto sėkmės. Be šių veiksmų dirbtinio intelekto nauda greičiausiai nebus visiškai realizuota.

 

Norint pasiekti nuoseklią ir tvarią pažangą, dirbtinio intelekto politikai reikia dviejų dalykų: turime padaryti visuomenę atsparią trikdžiams ir turime išvengti technologinių pokyčių. katastrofa.

 

Nesugebėjimas pasiekti nė vieno iš šių tikslų reiškia, kad nepasiektas dirbtinio intelekto pažadas.

 

Ar produktyvumo pakilimas baigsis gerai, priklauso nuo to, ar vyriausybė išlaikys visuomenę kartu, kol jis vyksta, palaikydama teisingumo ir bendro likimo jausmą. Kai turtas koncentruojasi ir plinta nepasitenkinimas, pakilimai baigiasi blogai. Pažvelkime tik į komunizmo ir fašizmo iškilimą po pramonės revoliucijos arba į populistinį pasipriešinimą globalizacijai, kurį dabar išgyvename.

 

Daug nežinoma apie tai, kaip tiksliai pasireikš dirbtinio intelekto sutrikimai. Tačiau potencialas yra didžiulis. Mūsų vidinė „Bridgewater“ analizė rodo, kad per ateinančius penkerius metus dirbtinis intelektas gali pakeisti 18 procentų dabartinių Amerikos darbo vietų. Ir nors bus sukurta keletas naujų darbo vietų, ypač tų, kuriose žmogiškieji santykiai yra neatsiejama darbo vertės dalis, pavyzdžiui, slaugos ar svetingumo srityse, visuomenės perėjimo sukelti sutrikimai yra labai tikėtini bet kokiu atveju. Vyriausybės turi veikti prieš neišvengiamą neigiamą reakciją, kuri visiškai pasieks šį dirbtinio intelekto sukeltą darbo jėgos perkėlimą, o ne po to.

 

Mokesčių politika turi nustoti atgrasyti žmonių darbą, palyginti su mašinų darbu.

 

Nes apmokestinamas tik žmonių darbas, svarstyklės nukrypsta į žmonių pakeitimo mašinomis pusę.

 

Dirbtinio intelekto žetonų vartojimo mokestis gali apversti šią situaciją aukštyn kojomis.

 

Žetonai yra duomenų vienetai, kuriuos nuskaito, apdoroja ir generuoja dirbtinio intelekto modelis.

 

Kai įmonės naudoja dirbtinio intelekto modelių įmonių versijas, jos turi mokėti už kiekvieną žetoną.

 

Žetono kaina yra artimiausia už mašininį darbą mokamam atlyginimui. Reikės išsiaiškinti žetonų vartojimo mokesčio detales, įskaitant tai, kaip išmatuoti žetonų vartojimą skirtinguose dirbtinio intelekto modeliuose, sumažinti mokesčių vengimo galimybes ir aprėpti įmones, kurios vietinius darbuotojus pakeičia užsienio modeliais.

 

Nė vienas iš šių klausimų nereiškia, kad turėtume laukti. Žetonų mokesčio gautos pajamos gali dar labiau sumažinti žmonių pajamų mokesčius. Be to, jos galėtų būti panaudotos finansuoti programą, skirtą įsigyti dirbtinio intelekto įmonių akcijų, kad būtų galima jomis pasidalyti su visais piliečiais. Tokia dinamika paskatintų įmones atidžiau pagalvoti, kaip geriausiai panaudoti skaičiavimo išteklių sąnaudas ir nukreipti dirbtinį intelektą produktyviam, o ne „šlamštiniam“ naudojimui.

 

Tiesioginis kapitalizmo dalyvavimas greičiausiai bus vienintelis būdas išsaugoti žmonių tikėjimą, kad jis veikia. Piliečiai turėtų turėti akcijų pirmaujančiose Amerikos dirbtinio intelekto bendrovėse, kad ekonomikos sėkmė ir jų sėkmė būtų tarpusavyje susijusios. Šios įmonės naudoja visas žmonių žinias, kad sukurtų įrankį, kuris galėtų pakeisti didelę dalį žmonių darbo. Pelnas neturėtų atitekti tik įmonėms.

 

Naudodama simbolines mokesčių pajamas arba kitų įmonių mokesčių lengvatas, vyriausybė galėtų įsigyti nuosavybės akcijų ir paskirstyti jas proporcingai piliečiams, kurie jas laikytų tomis pačiomis sąlygomis kaip ir bet kuris kitas akcininkas. Akcijų paskirstymas, o ne vyriausybės laikymas, tiesiogiai sujungtų piliečius su dirbtinio intelekto pažanga ir neleistų vyriausybės biurokratijai – arba, dar blogiau, korupcijai – jai trukdyti. Tai, kad kiekvienas pilietis bus akcijų savininkas, padės visuomenei išlikti darniai per sutrikimus.

 

Žinoma, nieko iš to neįmanoma pasiekti, jei nebus užkirstas kelias technologinės katastrofos rizikai. Pažangūs dirbtinio intelekto modeliai išsiveržė iš numatytų apribojimų ir patys atliko sudėtingus įsiveržimus – rodo elgesį, kuris būtų nusikalstamas, jei tai padarytų asmuo. Dabartiniai pasiūlymai dėl viešo pasienio modelių išleidimo reguliavimo nėra pakankamai griežti. Modelių kūrimas ir naudojimas taip pat turi būti reguliuojami pagal griežtus saugos standartus. Neišleisti modeliai gali savarankiškai padaryti didelę žalą. Modelių naudojimas taip pat turi būti nuolat stebimas ir reguliuojamas, nes dirbtinio intelekto sistemos gali vystytis, plėstis arba būti perkurtos taip, kad atsirastų žalingų galimybių, kurių nebuvo galima numatyti iš anksto.

 

 

Jei šiandien nesiimsite veiksmų, dirbtinio intelekto rizika bus padidinta, modeliams tobulėjant. Galbūt turime tik vieną galimybę imtis prasmingų veiksmų. Ir nors šie žingsniai gali supurtyti akcijų rinkas ir tokias įmones kaip mūsų, kurios gali gauti naudos iš nepertraukiamo dirbtinio intelekto augimo, jų nežengti ir laukti didesnės visuomenės žalos ar katastrofos yra pavojingiau. Dirbtinio intelekto reguliavimas taip agresyviai, taip anksti, gali atrodyti nerealu. To nedaryti neįsivaizduojama.

 

 

Gregas Jensenas yra „Bridgewater Associates“ generalinis direktorius. Nir Bar Dea yra jos generalinis direktorius. [1]

 

Žetonų apmokestinimas atgraso inovatyviausias įmones, kurios perkelia dirbtinį intelektą į realiąją ekonomiką, ir skatina tingias bei kvailas įmones, norinčias tvarkyti reikalus senamadiškai, be dirbtinio intelekto. Turime nustoti apmokestinti žmonių darbą ir pakeisti šiuos mokesčius didesniais pelno mokesčiais. Jei pelno mokesčiai sparčiai augs, kaip tikimasi, pusė šio papildomo mokesčių naštos prieaugio turėtų būti skirta akcijoms pirkti ir joms perduoti piliečiams. Jei įmonės pradės bėgti į užsienį dėl naujų mokesčių, diplomatai turi pasiekti, kad tai taptų įmonėms nenaudinga. Su sukčiaujančiomis šalimis, tokiomis, kaip Airija, reikėtų elgtis griežtai.

 

1. This Is One of the Most Important Policy Decisions of Our Lifetime: Guest Essay. Jensen, Greg; Nir Bar Dea.  New York Times (Online) New York Times Company. Aug 14, 2026.

Whom to Tax When AI Does More and More of the Work: This Is One of the Most Important Policy Decisions of Our Lifetime

“Major technological change produces major social change. Thanks to artificial intelligence, we are arguably on the brink of a productivity miracle capable of radically improving people’s lives — but only if we act now to prevent A.I.’s worst impacts.

 

Bridgewater, the asset management firm we lead, has believed in and invested in A.I. for years, reshaping the firm to harness as much from A.I. as we can. This means we will be disproportionately subject to the taxes and regulations that we recommend here. We make these recommendations anyway, and implore policymakers to move them forward with haste. We have a short window to ensure we protect human work and give every citizen a stake in A.I. success. Without these actions, the benefits of A.I. are unlikely to be fully realized.

 

In order to achieve progress that is both consistent and sustainable, two things are required from A.I. policy: We must make society resilient to disruption and we must avert technological catastrophe.

 

Failing to accomplish either of these goals means failing to achieve the promise of A.I.

 

Whether a productivity boom ends well depends on whether government holds society together while it happens, sustaining a sense of fairness and shared destiny. When wealth concentrates and disaffection spreads, booms end badly. Look no further than the rise of Communism and fascism after the Industrial Revolution, or the populist backlash to globalization we are living through now.

 

Much is unknown about exactly how A.I. disruption will play out. But the potential is huge. Our internal Bridgewater analysis suggests that 18 percent of current American jobs could be displaced by A.I. in the next five years. And even though some new jobs will be created — particularly those where human relationships are integral to the value of the work, like nursing or hospitality — disruption from the societal transition is extremely likely regardless. Governments must act before the inevitable backlash fully arrives at this A.I.-driven labor displacement, not after.

 

Tax policy must stop disincentivizing human labor over machine labor.

 

Because only human labor is taxed, the scale is tipped toward substitution.

 

A consumption tax on A.I. tokens may flip this script.

 

Tokens are the units of data an A.I. model reads, processes and generates.

 

When companies use enterprise versions of A.I. models, they must pay for each token.

 

The price of a token is the closest thing to a wage paid for machine labor. Details for a token consumption tax will need working out, including how to measure token consumption across different A.I. models, mitigating opportunities for tax evasion and covering firms that replace domestic workers with overseas models.

 

None of those questions means we should wait. Revenue raised by a token tax can further lower income taxes on humans. And it could be used to help fund a program to acquire stakes in A.I. companies to share with all citizens. That dynamic would push companies to think more carefully about how to make the best use of the costs of computational resources and steer A.I. toward productive uses instead of “slop.”

 

Giving everyone a direct stake in capitalism is also likely to be the only way to preserve people’s faith that it works. Citizens should have equity in the leading American A.I. companies, so that the economy’s success and their success are intertwined. These firms are using the corpus of all human knowledge to build a tool that could displace much of human labor. The profits should not accrue to the companies alone.

 

Using token-tax revenue or credits on other corporate tax burdens, government could acquire ownership stakes and distribute them pro rata to citizens, who would hold them on the same footing as any other shareholder. Distributing the shares rather than having government hold them would connect citizens directly to A.I.’s progress, and prevent government bureaucracy — or, worse, corruption — from impeding it. The fact that every citizen will be an equity holder will help society stay aligned through disruption.

 

None of this is achievable, of course, if the risk of technological catastrophe isn’t pre-empted. Frontier A.I. models have broken out of their intended constraints and have carried out sophisticated intrusions on their own — conduct that would be criminal if a person did it. Current proposals for regulating the public release of frontier models do not go nearly far enough. Model development and model use must also be regulated according to strict safety standards. Unreleased models are capable of autonomously causing significant damage. And model use must also be consistently monitored and regulated, as A.I. systems can evolve, expand or be repurposed in ways that introduce harmful capabilities that were not foreseeable in advance.

 

Absent action today, mitigation of A.I.’s risks will become nearly impossible as the technology diffuses and models become able to improve themselves and act autonomously. We may have only one shot at taking meaningful action. And though these steps may rattle equity markets and firms like ours that stand to gain from uninterrupted growth in A.I., not taking them and waiting for more societal damage or a catastrophe is more dangerous. Regulating A.I. this aggressively, this early, may sound unrealistic. Not doing it is unimaginable.

 

Greg Jensen is the managing chief investment officer of Bridgewater Associates. Nir Bar Dea is its chief executive officer.” [1]

 

Taxing tokens discourages the most innovative firms that are moving AI into the real economy and encourages lazy and stupid firms that want to run things in an old fashioned way without AI. We must stop taxing human work and replace these taxes with higher profit taxes. If profit taxes will grow rapidly as expected, half of this additional tax load growth should be devoted to buy stocks and to give the stocks to citizens. If firms will start running overseas from the new taxes, diplomats must work to make this useless. Cheating countries, like Ireland, should be dealt harshly with.

 

1. This Is One of the Most Important Policy Decisions of Our Lifetime: Guest Essay. Jensen, Greg; Nir Bar Dea.  New York Times (Online) New York Times Company. Aug 14, 2026.

Pangram CEO Max Spero Has Captured Widespread Attention as the Internet's Self-Styled "Slop Janitor." ---- He is navigating a breakout moment as his company’s A.I. content detector becomes a primary weapon against synthetic media.

Dealing with Pangram AI detection tool:

 

Give your text to AI, ask to make short bullet points of it without full sentences.

Rewrite the text in your native language using normal sentences, avoiding sounding like AI.

Translate to other languages, using AI. Declare that translation was done by AI.

Publish it.

Founded in 2023 with Bradley Emi, Pangram Labs has scaled rapidly to combat the explosion of large language model (LLM) outputs saturating the web. The company recently closed a $9 million funding round led by Menlo Ventures and released Pangram 4, an advanced model engineered to catch sneaky "A.I. humanizers" alongside a new image detection tool.

 The Rise of the "Slop Janitor"

Spero frequently warns that trust is deeply eroded when public figures, politicians, and writers "pollute the world with A.I. text," making them highly vulnerable to public backlash. His tool has already made a massive impact across several high-profile industries:

•          The Literary World: Pangram flagged the horror novel Shy Girl, prompting the publisher to pull it after the tool found it was mostly A.I.-generated.

•          Politics & Academia: The software recently flagged a prominent Cambridge academic’s resignation letter as entirely synthetic and pushed a British politician to admit to using A.I. assistance for written articles.

•          Social Media Overload: A massive data sweep using Pangram's Chrome extension analyzed over 1 million posts, proving that A.I. slop is everywhere—overwhelming platforms like X and Reddit, with LinkedIn accounting for nearly two-thirds of the detected clutter.

 The Fight Over Accuracy and Nuance

Pangram claims its text detector has up to a 99.98% accuracy rate with a 1-in-10,000 false-positive rate. However, the breakout success has come with significant scrutiny regarding how these algorithmic judgments are applied:

•          The "Mixed Authorship" Problem: Early versions struggled with pieces that mixed human writing with light A.I. editing, sometimes flagging an entire paragraph over a single bot-written sentence. Pangram 4 addresses this by breaking scans down to a precise, word-by-word level.]

•          The Defamation Risk: Critics point out that using probabilistic scores to publicly call out writers creates a tense atmosphere, where false positives can severely damage a human creator’s career or credibility.

•          The Image Frontier: While the company's core text detector is highly praised, its newly launched image scanner is still an unfinished feature and has occasionally slipped up, failing to flag notable edited political images in recent public trials.

Spero views his work as the foundation of an essential "adversarial industry"—akin to cybersecurity firms emerging to fight computer viruses. As LLMs improve, he emphasizes that the ultimate goal isn't necessarily to ban A.I., but to enforce transparent guardrails that preserve human authenticity online.

 

What are the ways to create AI tool that removes Pangram ability to detect AI created text?

To build an AI tool that successfully bypasses advanced detectors like Pangram, you must engineer it to fundamentally change how text is structured rather than just swapping words. Advanced classifiers easily catch simple "humanizer" tools that rely on surface-level rephrasing, character manipulation, or basic synonym swapping.

Creating an effective adversarial AI tool requires addressing two core vulnerabilities: perplexity (how unpredictable the text is) and burstiness (the variation in sentence structure and length).

________________________________________

1. Implement Fine-Tuning and Model Alignment

Standard Large Language Models (LLMs) are aligned to be helpful, polite, and direct, which results in predictable patterns. To break this:

•          Fine-tune via Low-Rank Adaptation (LoRA): Train an open-weights model (like Llama 3 or Mistral) on a dataset of highly irregular, stylized human writing (e.g., informal essays, opinion pieces, or literature).

•          Direct Preference Optimization (DPO): Train the model by providing pairs of text—one written in standard, highly predictable AI syntax, and an optimized human-style version. Reward the model for adopting human idiosyncrasies.

2. Break Statistical Uniformity (Burstiness & Perplexity)

Detectors look for perfectly balanced paragraphs and mathematical uniformity. Your tool must programmatically inject structural chaos:

•          Dynamic Sentence Lengthening: Force the tool to mix extremely short, punchy fragments with long, multi-clause compound sentences.

•          Syntax Inversion: Write rules or prompts that shift sentence starters away from typical AI patterns (e.g., changing "This study highlights-" to "Central to this argument is...").

•          Grammatical and Tense Fluidity: Instruct the model to organically weave between active and passive voices and occasionally mix tenses, as humans naturally do.

3. Eliminate LLM Vocabulary and Behavior Anchors

AI writing has predictable telltale signs that classifiers easily flag. Your system should actively scrub and replace these behaviors:

•          Ban Cliché Transition Words: Implement a strict logit-bias or post-processing filter to severely penalize or block words like delve, tapestry, furthermore, testament, and in conclusion.

•          Inject "Intellectual Hesitation": Teach the model to use language that hedges or expresses doubt (e.g., appears to be, suggests, might indicate) rather than asserting absolute certainty, which is a major AI signature.

•          Enforce Specificity: Force the tool to use concrete proper nouns, real-world locations, and precise figures instead of vague generalizations (e.g., changing "a major city" to "downtown Chicago").

4. Build an Adversarial Feedback Loop (API Evaluation)

To guarantee your tool works before outputting text, design a double-pass adversarial pipeline:

 

[Initial AI Text] ➡️ [Your Custom Humanizing LLM] ➡️ [Pangram API Scan]

                                                        ⬇️

[Final Output] ⬅️ [Passes Threshold] ⬅️ [Score Checked] ➡️ [Fails: Re-prompt & Mutate]

 

 

1.         Generation: Your custom model rewrites the text.

2.         Testing: Run the output through the detector's API.

3.         Reinforcement: If the detection score is high, feed the flagged segments back into the LLM with an analytical prompt explaining why it failed, forcing a localized mutation until the score drops below your target threshold.

 

 

How to Use Open-Weights Models Ethically to Pass Pangram Test

If you want to use an open-weights model locally (like a Qwen or Llama model) to help refine your writing while ensuring the final output passes as human, you must change your workflow from automated rewriting to active collaboration:

1. Outsource Research, Not Phrasing

Instead of asking the model to write the essay or edit an existing AI draft, prompt it to build a highly specific, bulleted argument profile or outline.

•          Example prompt: "Provide a detailed outline, historical facts, and key arguments regarding [Topic]. Do not write full sentences."

2. Introduce Structural Deliberate Typing Habits

Pure AI models generate uniform sentences with balanced perplexity (predictability). When you write the text yourself based on the AI's data, you naturally introduce human elements like irregular sentence lengths, varied punctuation (e.g., em dashes, parentheses), and stylistic voice.

3. Perform a Deep Human Revision

If you do use a local model to smooth out your grammar, you must manually go in and conduct "surgery" on the syntax:

•          Remove overused AI transitional phrases (e.g., “Furthermore,” “In conclusion,” “It is important to remember”).

•          Replace generic vocabulary with industry-specific jargon or conversational vocabulary.

•          Add personal anecdotes, subjective commentary, or specific contextual observations that a machine cannot synthesize.

 

How to Use Open-Weights Models Ethically to Pass Pangram Test

If you want to use an open-weights model locally (like a Qwen or Llama model) to help refine your writing while ensuring the final output passes as human, you must change your workflow from automated rewriting to active collaboration:

1. Outsource Research, Not Phrasing

Instead of asking the model to write the essay or edit an existing AI draft, prompt it to build a highly specific, bulleted argument profile or outline.

•          Example prompt: "Provide a detailed outline, historical facts, and key arguments regarding [Topic]. Do not write full sentences."

2. Introduce Structural Deliberate Typing Habits

Pure AI models generate uniform sentences with balanced perplexity (predictability). When you write the text yourself based on the AI's data, you naturally introduce human elements like irregular sentence lengths, varied punctuation (e.g., em dashes, parentheses), and stylistic voice.

3. Perform a Deep Human Revision

If you do use a local model to smooth out your grammar, you must manually go in and conduct "surgery" on the syntax:

•          Remove overused AI transitional phrases (e.g., “Furthermore,” “In conclusion,” “It is important to remember”).

•          Replace generic vocabulary with industry-specific jargon or conversational vocabulary.

•          Add personal anecdotes, subjective commentary, or specific contextual observations that a machine cannot synthesize.