{"id":89691,"topic":"ai","source":"cio.com","title":"The cost of intelligence? - cio.com","url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","url_hash":"dc1292f2840071212b9f9920cd8cc5138cbc13c9","author":"","summary":"The cost of intelligence? cio.com","content":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?\nMy position is that the ultimate constraint on artificial intelligence may not be chips, algorithms, data or even electricity. It may be economics.\nWe are becoming extraordinarily good at producing machine intelligence. We are far less capable of measuring what that intelligence is actually worth.\nAnd that gap could become one of the defining economic problems of the AI era.\nWe are building factories for intelligence\nAI is usually described as software. Increasingly, that description is misleading.\nBehind every AI prompt is an enormous physical industrial system: semiconductors, electrical generation, transmission networks, data centers, cooling systems, storage, telecommunications, software and people.\nAI mega-data centers are, in effect, the factories of the Intelligence Economy. Instead of turning steel into automobiles, they turn electricity and computation into predictions, recommendations, decisions, software, images, knowledge and other forms of machine-generated intelligence.\nThis changes the economics of computing.\nIntelligence now has a cost of production.\nAnd unlike the Internet services we became accustomed to thinking of as almost weightless, AI consumes very tangible resources: capital, land, electricity, water, chips, networks and increasingly scarce technical talent.\nThat is why the enormous investment in AI infrastructure matters. We aren’t simply financing another generation of software. We are financing the industrialization of intelligence.\nThe AI infrastructure paradox\nThis leads to what I call the AI infrastructure paradox.\nThe world is committing extraordinary amounts of capital and physical resources to producing intelligence before we have developed equally sophisticated ways of measuring the economic value of the intelligence being produced.\nWe can measure GPU utilization.\nWe can calculate cloud costs almost to the penny.\nBut ask a corporation a different question — exactly how much incremental economic value did your AI produce? — and the answer frequently becomes much less precise.\nThat is a serious problem.\nA company processing billions of AI prompts is not necessarily creating more economic value than one processing a few million. Deploying thousands of copilots or agents tells us that AI is being used. It doesn’t tell us whether the company has become proportionately more productive, profitable, innovative or competitive.\nWe are becoming extraordinarily sophisticated at measuring the consumption of intelligence while remaining surprisingly primitive at measuring its economic productivity.\nSomeone ultimately has to pay\nThere is another side of the AI boom that receives far less attention: affordability.\nData centers cost money. Chips cost money. Electricity costs money. Water, networks, cybersecurity, governance, data engineering and specialized talent all cost money.\nThose costs cannot simply disappear.\nUltimately, they must be absorbed somewhere — by consumers through higher prices, enterprises through higher technology spending, shareholders through lower margins, or governments and taxpayers through subsidies and infrastructure investment.\nThis is where AI encounters an economic constraint that no increase in model intelligence can eliminate.\nIf the cost of intelligence grows faster than the value of intelligence, eventually something has to give.\nThis also challenges one of the most persistent assumptions of the technology industry: that technology inevitably gets cheaper.\nIndividual units of computing may indeed become cheaper. But organizations can still spend more because they consume vastly more computing, cloud capacity, cybersecurity, software, data and AI.\nI call this phenomenon IT Inflation.\nTechnology can simultaneously become more efficient and more expensive.\nThe important economic question, therefore, isn’t whether the cost of a token, GPU operation or gigabyte falls. It is whether the economic value produced by technology grows faster than the total cost of consuming it.\nThe community eventually gets a vote\nThe physical scale of AI creates another economic problem.\nThe costs and benefits of AI infrastructure frequently occur in different places.\nA community hosting a massive data center sees the land use, transmission lines, water consumption, electricity requirements and public incentives directly.\nBut much of the economic value may flow elsewhere — to technology companies, cloud providers, software developers, users and shareholders around the world.\nThat creates a reasonable local question:\nIf we absorb the costs, where is our share of the value?\nAs AI infrastructure expands, communities will increasingly demand answers. Data centers will require more than construction permits and electrical connections. They will increasingly require what might be called a social license to operate — evidence that the economic and societal benefits justify the resources being consumed.\nThe great measurement failure\nThis brings us to what may be AI’s biggest weakness.\nIt isn’t intelligence.\nIt is measurement.\nTraditional accounting was developed for an economy built primarily around physical assets, labor and financial capital. It tells us remarkably well what organizations spend and what they earn.\nBut technology has evolved from a supporting function into something closer to a factor of production.\nLike labor, technology performs work.\nLike capital, it requires investment.\nLike infrastructure, it enables economic activity.\nAI takes this one step further because organizations can increasingly acquire and consume machine intelligence almost as previous generations consumed electricity.\nYet our accounting and economic measurement systems have not caught up.\nThat is why I believe we need a discipline of technology economics.\nFrom technology management to technology economics\nTechnology economics asks a different set of questions from traditional IT management.\nNot simply: Does the system work?\nBut: What economic value does it create?\nNot simply: How much AI are we using?\nBut: How productive is that AI?\nNot simply: How much are we investing?\nBut: What return are we receiving relative to the capital consumed?\nThe framework I propose includes measures such as IT intensity, IT inflation, technology cost of capital, AI expense per share, AI value per share, margin per unit of IT intensity, the technology leadership index and gross domestic intelligence.\nConsider just two of these.\nAI expense per share asks shareholders to view AI investment in familiar economic terms: how much AI expenditure is effectively being borne per share?\nAI value per share asks the other half of the equation: how much measurable value is AI generating for those shareholders?\nSimilarly, margin per unit of IT intensity asks whether an organization is efficiently converting its technology dependency into economic performance.\nTwo companies can spend similar amounts on AI and produce dramatically different results. The winner shouldn’t be the company that consumes the most intelligence. It should be the company that converts intelligence into the most value.\nAI needs its own measurement revolution\nEvery major economic transformation eventually produced new ways of measuring itself.\nIndustrialization gave us modern accounting.\nMass production drove the development of productivity measurement.\nGlobal finance produced increasingly sophisticated theories of capital, risk and return.\nThe Intelligence Economy will need its own measurement revolution.\nCompanies may eventually need something resembling a Technology Balance Sheet showing their productive technology assets and obligations, and a Technology Income Statement connecting technology expenditures with the economic value those expenditures generate.\nInvestors will increasingly expect companies to disclose not merely how much they are spending on AI, but what they are receiving in return.\nTechnology executives, consequently, will need to become economic leaders as well as technical leaders. Boards will increasingly expect CIOs and other technology leaders to explain affordability, productivity and value — not simply reliability, security, modernization and innovation.\nThe same problem exists at the national level.\nGDP measures economic activity remarkably well, but it was never designed to measure intelligence as a productive resource.\nThat suggests the need for complementary concepts such as gross domestic intelligence: an attempt to understand the intelligence capacity that increasingly enables economic activity.\nWhether that particular measure ultimately becomes standard is less important than the underlying idea.\nIf intelligence has become an economic resource, eventually we will have to measure it as one.\nThe question that comes after the hype\nNone of this argues that AI will fail.\nQuite the opposite.\nAI may transform productivity, science, medicine, education, finance, manufacturing and almost every other significant area of human activity.\nBut technological capability does not repeal economics.\nThe railroad was transformative. So was electricity. So were telecommunications and the Internet. Each also experienced periods when infrastructure investment ran ahead of sustainable economic returns.\nAI may follow a similar pattern.\nThe critical divide may therefore not be between companies that adopt AI and companies that don’t.\nIt may be between those that understand the economics of intelligence and those that simply consume more of it.\nWe already know how to make machines increasingly intelligent.\nThe next challenge is learning how to make that intelligence economically productive, affordable and sustainable.\nCommunities will want evidence of societal value. Investors will want evidence of shareholder value. Boards will want evidence of business value. Governments will want evidence of national value.\nAll of those demands eventually converge on one question:\nWhat is intelligence worth?\nThe industrial revolution created modern accounting. The information age created digital technology management. The Intelligence Economy may require technology economics.\nAnd the most important innovation of the AI era may ultimately turn out not to be another model, another chip or another trillion-dollar generation of data centers.\nIt may be the measurement system that finally allows us to answer the question behind all of them:\nDoes the value created by intelligence exceed the cost required to produce it?\nEverything else follows from the answer.","image_url":"https://www.cio.com/wp-content/uploads/2026/09/4225695-0-39385300-1790254909-original.jpg?quality=50&strip=all&w=1024","lang":"en","published_at":"2026-09-24T13:02:40+00:00","fetched_at":"2026-09-24T15:15:05+00:00","status":"read","starred":0,"extract_state":"ok","summary_auto":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?","cluster_id":null,"extract_retries":0,"extract_error":null,"contract_version":"news_item.v1","format_contract_version":"news_item_formats.v1","dedup_url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 10800 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":10800,"summary_length":320,"usable_text_length":10800,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":10800,"summary_length":320}},"news_item":{"id":89691,"canonical_url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","source_url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","title":"The cost of intelligence? - cio.com","source_name":"cio.com","author":null,"published_at":"2026-09-24T13:02:40+00:00","locale":"en","topic":"ai","tags":[],"rss_summary":"The cost of intelligence? cio.com","full_text":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?\nMy position is that the ultimate constraint on artificial intelligence may not be chips, algorithms, data or even electricity. It may be economics.\nWe are becoming extraordinarily good at producing machine intelligence. We are far less capable of measuring what that intelligence is actually worth.\nAnd that gap could become one of the defining economic problems of the AI era.\nWe are building factories for intelligence\nAI is usually described as software. Increasingly, that description is misleading.\nBehind every AI prompt is an enormous physical industrial system: semiconductors, electrical generation, transmission networks, data centers, cooling systems, storage, telecommunications, software and people.\nAI mega-data centers are, in effect, the factories of the Intelligence Economy. Instead of turning steel into automobiles, they turn electricity and computation into predictions, recommendations, decisions, software, images, knowledge and other forms of machine-generated intelligence.\nThis changes the economics of computing.\nIntelligence now has a cost of production.\nAnd unlike the Internet services we became accustomed to thinking of as almost weightless, AI consumes very tangible resources: capital, land, electricity, water, chips, networks and increasingly scarce technical talent.\nThat is why the enormous investment in AI infrastructure matters. We aren’t simply financing another generation of software. We are financing the industrialization of intelligence.\nThe AI infrastructure paradox\nThis leads to what I call the AI infrastructure paradox.\nThe world is committing extraordinary amounts of capital and physical resources to producing intelligence before we have developed equally sophisticated ways of measuring the economic value of the intelligence being produced.\nWe can measure GPU utilization.\nWe can calculate cloud costs almost to the penny.\nBut ask a corporation a different question — exactly how much incremental economic value did your AI produce? — and the answer frequently becomes much less precise.\nThat is a serious problem.\nA company processing billions of AI prompts is not necessarily creating more economic value than one processing a few million. Deploying thousands of copilots or agents tells us that AI is being used. It doesn’t tell us whether the company has become proportionately more productive, profitable, innovative or competitive.\nWe are becoming extraordinarily sophisticated at measuring the consumption of intelligence while remaining surprisingly primitive at measuring its economic productivity.\nSomeone ultimately has to pay\nThere is another side of the AI boom that receives far less attention: affordability.\nData centers cost money. Chips cost money. Electricity costs money. Water, networks, cybersecurity, governance, data engineering and specialized talent all cost money.\nThose costs cannot simply disappear.\nUltimately, they must be absorbed somewhere — by consumers through higher prices, enterprises through higher technology spending, shareholders through lower margins, or governments and taxpayers through subsidies and infrastructure investment.\nThis is where AI encounters an economic constraint that no increase in model intelligence can eliminate.\nIf the cost of intelligence grows faster than the value of intelligence, eventually something has to give.\nThis also challenges one of the most persistent assumptions of the technology industry: that technology inevitably gets cheaper.\nIndividual units of computing may indeed become cheaper. But organizations can still spend more because they consume vastly more computing, cloud capacity, cybersecurity, software, data and AI.\nI call this phenomenon IT Inflation.\nTechnology can simultaneously become more efficient and more expensive.\nThe important economic question, therefore, isn’t whether the cost of a token, GPU operation or gigabyte falls. It is whether the economic value produced by technology grows faster than the total cost of consuming it.\nThe community eventually gets a vote\nThe physical scale of AI creates another economic problem.\nThe costs and benefits of AI infrastructure frequently occur in different places.\nA community hosting a massive data center sees the land use, transmission lines, water consumption, electricity requirements and public incentives directly.\nBut much of the economic value may flow elsewhere — to technology companies, cloud providers, software developers, users and shareholders around the world.\nThat creates a reasonable local question:\nIf we absorb the costs, where is our share of the value?\nAs AI infrastructure expands, communities will increasingly demand answers. Data centers will require more than construction permits and electrical connections. They will increasingly require what might be called a social license to operate — evidence that the economic and societal benefits justify the resources being consumed.\nThe great measurement failure\nThis brings us to what may be AI’s biggest weakness.\nIt isn’t intelligence.\nIt is measurement.\nTraditional accounting was developed for an economy built primarily around physical assets, labor and financial capital. It tells us remarkably well what organizations spend and what they earn.\nBut technology has evolved from a supporting function into something closer to a factor of production.\nLike labor, technology performs work.\nLike capital, it requires investment.\nLike infrastructure, it enables economic activity.\nAI takes this one step further because organizations can increasingly acquire and consume machine intelligence almost as previous generations consumed electricity.\nYet our accounting and economic measurement systems have not caught up.\nThat is why I believe we need a discipline of technology economics.\nFrom technology management to technology economics\nTechnology economics asks a different set of questions from traditional IT management.\nNot simply: Does the system work?\nBut: What economic value does it create?\nNot simply: How much AI are we using?\nBut: How productive is that AI?\nNot simply: How much are we investing?\nBut: What return are we receiving relative to the capital consumed?\nThe framework I propose includes measures such as IT intensity, IT inflation, technology cost of capital, AI expense per share, AI value per share, margin per unit of IT intensity, the technology leadership index and gross domestic intelligence.\nConsider just two of these.\nAI expense per share asks shareholders to view AI investment in familiar economic terms: how much AI expenditure is effectively being borne per share?\nAI value per share asks the other half of the equation: how much measurable value is AI generating for those shareholders?\nSimilarly, margin per unit of IT intensity asks whether an organization is efficiently converting its technology dependency into economic performance.\nTwo companies can spend similar amounts on AI and produce dramatically different results. The winner shouldn’t be the company that consumes the most intelligence. It should be the company that converts intelligence into the most value.\nAI needs its own measurement revolution\nEvery major economic transformation eventually produced new ways of measuring itself.\nIndustrialization gave us modern accounting.\nMass production drove the development of productivity measurement.\nGlobal finance produced increasingly sophisticated theories of capital, risk and return.\nThe Intelligence Economy will need its own measurement revolution.\nCompanies may eventually need something resembling a Technology Balance Sheet showing their productive technology assets and obligations, and a Technology Income Statement connecting technology expenditures with the economic value those expenditures generate.\nInvestors will increasingly expect companies to disclose not merely how much they are spending on AI, but what they are receiving in return.\nTechnology executives, consequently, will need to become economic leaders as well as technical leaders. Boards will increasingly expect CIOs and other technology leaders to explain affordability, productivity and value — not simply reliability, security, modernization and innovation.\nThe same problem exists at the national level.\nGDP measures economic activity remarkably well, but it was never designed to measure intelligence as a productive resource.\nThat suggests the need for complementary concepts such as gross domestic intelligence: an attempt to understand the intelligence capacity that increasingly enables economic activity.\nWhether that particular measure ultimately becomes standard is less important than the underlying idea.\nIf intelligence has become an economic resource, eventually we will have to measure it as one.\nThe question that comes after the hype\nNone of this argues that AI will fail.\nQuite the opposite.\nAI may transform productivity, science, medicine, education, finance, manufacturing and almost every other significant area of human activity.\nBut technological capability does not repeal economics.\nThe railroad was transformative. So was electricity. So were telecommunications and the Internet. Each also experienced periods when infrastructure investment ran ahead of sustainable economic returns.\nAI may follow a similar pattern.\nThe critical divide may therefore not be between companies that adopt AI and companies that don’t.\nIt may be between those that understand the economics of intelligence and those that simply consume more of it.\nWe already know how to make machines increasingly intelligent.\nThe next challenge is learning how to make that intelligence economically productive, affordable and sustainable.\nCommunities will want evidence of societal value. Investors will want evidence of shareholder value. Boards will want evidence of business value. Governments will want evidence of national value.\nAll of those demands eventually converge on one question:\nWhat is intelligence worth?\nThe industrial revolution created modern accounting. The information age created digital technology management. The Intelligence Economy may require technology economics.\nAnd the most important innovation of the AI era may ultimately turn out not to be another model, another chip or another trillion-dollar generation of data centers.\nIt may be the measurement system that finally allows us to answer the question behind all of them:\nDoes the value created by intelligence exceed the cost required to produce it?\nEverything else follows from the answer.","excerpt":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?","extraction":{"state":"ok","confidence":0.9,"error":null,"explanation":"High confidence: full text extraction produced 10800 characters.","diagnostics_url":"/api/diagnose?url=https%3A//www.cio.com/article/4225695/the-cost-of-intelligence.html","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 10800 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":10800,"summary_length":320,"usable_text_length":10800,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":10800,"summary_length":320}}},"display_formats":["compact","card","full","digest_section","json"]},"daily_stack_record":{"title":"The cost of intelligence? - cio.com","url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","summary":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?","source":"cio.com","date":"2026-09-24T13:02:40+00:00","content":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?\nMy position is that the ultimate constraint on artificial intelligence may not be chips, algorithms, data or even electricity. It may be economics.\nWe are becoming extraordinarily good at producing machine intelligence. We are far less capable of measuring what that intelligence is actually worth.\nAnd that gap could become one of the defining economic problems of the AI era.\nWe are building factories for intelligence\nAI is usually described as software. Increasingly, that description is misleading.\nBehind every AI prompt is an enormous physical industrial system: semiconductors, electrical generation, transmission networks, data centers, cooling systems, storage, telecommunications, software and people.\nAI mega-data centers are, in effect, the factories of the Intelligence Economy. Instead of turning steel into automobiles, they turn electricity and computation into predictions, recommendations, decisions, software, images, knowledge and other forms of machine-generated intelligence.\nThis changes the economics of computing.\nIntelligence now has a cost of production.\nAnd unlike the Internet services we became accustomed to thinking of as almost weightless, AI consumes very tangible resources: capital, land, electricity, water, chips, networks and increasingly scarce technical talent.\nThat is why the enormous investment in AI infrastructure matters. We aren’t simply financing another generation of software. We are financing the industrialization of intelligence.\nThe AI infrastructure paradox\nThis leads to what I call the AI infrastructure paradox.\nThe world is committing extraordinary amounts of capital and physical resources to producing intelligence before we have developed equally sophisticated ways of measuring the economic value of the intelligence being produced.\nWe can measure GPU utilization.\nWe can calculate cloud costs almost to the penny.\nBut ask a corporation a different question — exactly how much incremental economic value did your AI produce? — and the answer frequently becomes much less precise.\nThat is a serious problem.\nA company processing billions of AI prompts is not necessarily creating more economic value than one processing a few million. Deploying thousands of copilots or agents tells us that AI is being used. It doesn’t tell us whether the company has become proportionately more productive, profitable, innovative or competitive.\nWe are becoming extraordinarily sophisticated at measuring the consumption of intelligence while remaining surprisingly primitive at measuring its economic productivity.\nSomeone ultimately has to pay\nThere is another side of the AI boom that receives far less attention: affordability.\nData centers cost money. Chips cost money. Electricity costs money. Water, networks, cybersecurity, governance, data engineering and specialized talent all cost money.\nThose costs cannot simply disappear.\nUltimately, they must be absorbed somewhere — by consumers through higher prices, enterprises through higher technology spending, shareholders through lower margins, or governments and taxpayers through subsidies and infrastructure investment.\nThis is where AI encounters an economic constraint that no increase in model intelligence can eliminate.\nIf the cost of intelligence grows faster than the value of intelligence, eventually something has to give.\nThis also challenges one of the most persistent assumptions of the technology industry: that technology inevitably gets cheaper.\nIndividual units of computing may indeed become cheaper. But organizations can still spend more because they consume vastly more computing, cloud capacity, cybersecurity, software, data and AI.\nI call this phenomenon IT Inflation.\nTechnology can simultaneously become more efficient and more expensive.\nThe important economic question, therefore, isn’t whether the cost of a token, GPU operation or gigabyte falls. It is whether the economic value produced by technology grows faster than the total cost of consuming it.\nThe community eventually gets a vote\nThe physical scale of AI creates another economic problem.\nThe costs and benefits of AI infrastructure frequently occur in different places.\nA community hosting a massive data center sees the land use, transmission lines, water consumption, electricity requirements and public incentives directly.\nBut much of the economic value may flow elsewhere — to technology companies, cloud providers, software developers, users and shareholders around the world.\nThat creates a reasonable local question:\nIf we absorb the costs, where is our share of the value?\nAs AI infrastructure expands, communities will increasingly demand answers. Data centers will require more than construction permits and electrical connections. They will increasingly require what might be called a social license to operate — evidence that the economic and societal benefits justify the resources being consumed.\nThe great measurement failure\nThis brings us to what may be AI’s biggest weakness.\nIt isn’t intelligence.\nIt is measurement.\nTraditional accounting was developed for an economy built primarily around physical assets, labor and financial capital. It tells us remarkably well what organizations spend and what they earn.\nBut technology has evolved from a supporting function into something closer to a factor of production.\nLike labor, technology performs work.\nLike capital, it requires investment.\nLike infrastructure, it enables economic activity.\nAI takes this one step further because organizations can increasingly acquire and consume machine intelligence almost as previous generations consumed electricity.\nYet our accounting and economic measurement systems have not caught up.\nThat is why I believe we need a discipline of technology economics.\nFrom technology management to technology economics\nTechnology economics asks a different set of questions from traditional IT management.\nNot simply: Does the system work?\nBut: What economic value does it create?\nNot simply: How much AI are we using?\nBut: How productive is that AI?\nNot simply: How much are we investing?\nBut: What return are we receiving relative to the capital consumed?\nThe framework I propose includes measures such as IT intensity, IT inflation, technology cost of capital, AI expense per share, AI value per share, margin per unit of IT intensity, the technology leadership index and gross domestic intelligence.\nConsider just two of these.\nAI expense per share asks shareholders to view AI investment in familiar economic terms: how much AI expenditure is effectively being borne per share?\nAI value per share asks the other half of the equation: how much measurable value is AI generating for those shareholders?\nSimilarly, margin per unit of IT intensity asks whether an organization is efficiently converting its technology dependency into economic performance.\nTwo companies can spend similar amounts on AI and produce dramatically different results. The winner shouldn’t be the company that consumes the most intelligence. It should be the company that converts intelligence into the most value.\nAI needs its own measurement revolution\nEvery major economic transformation eventually produced new ways of measuring itself.\nIndustrialization gave us modern accounting.\nMass production drove the development of productivity measurement.\nGlobal finance produced increasingly sophisticated theories of capital, risk and return.\nThe Intelligence Economy will need its own measurement revolution.\nCompanies may eventually need something resembling a Technology Balance Sheet showing their productive technology assets and obligations, and a Technology Income Statement connecting technology expenditures with the economic value those expenditures generate.\nInvestors will increasingly expect companies to disclose not merely how much they are spending on AI, but what they are receiving in return.\nTechnology executives, consequently, will need to become economic leaders as well as technical leaders. Boards will increasingly expect CIOs and other technology leaders to explain affordability, productivity and value — not simply reliability, security, modernization and innovation.\nThe same problem exists at the national level.\nGDP measures economic activity remarkably well, but it was never designed to measure intelligence as a productive resource.\nThat suggests the need for complementary concepts such as gross domestic intelligence: an attempt to understand the intelligence capacity that increasingly enables economic activity.\nWhether that particular measure ultimately becomes standard is less important than the underlying idea.\nIf intelligence has become an economic resource, eventually we will have to measure it as one.\nThe question that comes after the hype\nNone of this argues that AI will fail.\nQuite the opposite.\nAI may transform productivity, science, medicine, education, finance, manufacturing and almost every other significant area of human activity.\nBut technological capability does not repeal economics.\nThe railroad was transformative. So was electricity. So were telecommunications and the Internet. Each also experienced periods when infrastructure investment ran ahead of sustainable economic returns.\nAI may follow a similar pattern.\nThe critical divide may therefore not be between companies that adopt AI and companies that don’t.\nIt may be between those that understand the economics of intelligence and those that simply consume more of it.\nWe already know how to make machines increasingly intelligent.\nThe next challenge is learning how to make that intelligence economically productive, affordable and sustainable.\nCommunities will want evidence of societal value. Investors will want evidence of shareholder value. Boards will want evidence of business value. Governments will want evidence of national value.\nAll of those demands eventually converge on one question:\nWhat is intelligence worth?\nThe industrial revolution created modern accounting. The information age created digital technology management. The Intelligence Economy may require technology economics.\nAnd the most important innovation of the AI era may ultimately turn out not to be another model, another chip or another trillion-dollar generation of data centers.\nIt may be the measurement system that finally allows us to answer the question behind all of them:\nDoes the value created by intelligence exceed the cost required to produce it?\nEverything else follows from the answer.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//www.cio.com/article/4225695/the-cost-of-intelligence.html","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 10800 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 10800 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":10800,"summary_length":320,"usable_text_length":10800,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":10800,"summary_length":320}},"tags":[]},"fallback_formats":["markdown","json","html"],"actions":{"read":"/item/89691","export_markdown":"/api/items/89691/export?format=markdown","export_json":"/api/items/89691/export?format=json","diagnose":"/api/diagnose?url=https%3A//www.cio.com/article/4225695/the-cost-of-intelligence.html"},"formats":{"full":{"id":89691,"title":"The cost of intelligence? - cio.com","url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","source":"cio.com","author":null,"published_at":"2026-09-24T13:02:40+00:00","locale":"en","topic":"ai","tags":[],"excerpt":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?","full_text":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?\nMy position is that the ultimate constraint on artificial intelligence may not be chips, algorithms, data or even electricity. It may be economics.\nWe are becoming extraordinarily good at producing machine intelligence. We are far less capable of measuring what that intelligence is actually worth.\nAnd that gap could become one of the defining economic problems of the AI era.\nWe are building factories for intelligence\nAI is usually described as software. Increasingly, that description is misleading.\nBehind every AI prompt is an enormous physical industrial system: semiconductors, electrical generation, transmission networks, data centers, cooling systems, storage, telecommunications, software and people.\nAI mega-data centers are, in effect, the factories of the Intelligence Economy. Instead of turning steel into automobiles, they turn electricity and computation into predictions, recommendations, decisions, software, images, knowledge and other forms of machine-generated intelligence.\nThis changes the economics of computing.\nIntelligence now has a cost of production.\nAnd unlike the Internet services we became accustomed to thinking of as almost weightless, AI consumes very tangible resources: capital, land, electricity, water, chips, networks and increasingly scarce technical talent.\nThat is why the enormous investment in AI infrastructure matters. We aren’t simply financing another generation of software. We are financing the industrialization of intelligence.\nThe AI infrastructure paradox\nThis leads to what I call the AI infrastructure paradox.\nThe world is committing extraordinary amounts of capital and physical resources to producing intelligence before we have developed equally sophisticated ways of measuring the economic value of the intelligence being produced.\nWe can measure GPU utilization.\nWe can calculate cloud costs almost to the penny.\nBut ask a corporation a different question — exactly how much incremental economic value did your AI produce? — and the answer frequently becomes much less precise.\nThat is a serious problem.\nA company processing billions of AI prompts is not necessarily creating more economic value than one processing a few million. Deploying thousands of copilots or agents tells us that AI is being used. It doesn’t tell us whether the company has become proportionately more productive, profitable, innovative or competitive.\nWe are becoming extraordinarily sophisticated at measuring the consumption of intelligence while remaining surprisingly primitive at measuring its economic productivity.\nSomeone ultimately has to pay\nThere is another side of the AI boom that receives far less attention: affordability.\nData centers cost money. Chips cost money. Electricity costs money. Water, networks, cybersecurity, governance, data engineering and specialized talent all cost money.\nThose costs cannot simply disappear.\nUltimately, they must be absorbed somewhere — by consumers through higher prices, enterprises through higher technology spending, shareholders through lower margins, or governments and taxpayers through subsidies and infrastructure investment.\nThis is where AI encounters an economic constraint that no increase in model intelligence can eliminate.\nIf the cost of intelligence grows faster than the value of intelligence, eventually something has to give.\nThis also challenges one of the most persistent assumptions of the technology industry: that technology inevitably gets cheaper.\nIndividual units of computing may indeed become cheaper. But organizations can still spend more because they consume vastly more computing, cloud capacity, cybersecurity, software, data and AI.\nI call this phenomenon IT Inflation.\nTechnology can simultaneously become more efficient and more expensive.\nThe important economic question, therefore, isn’t whether the cost of a token, GPU operation or gigabyte falls. It is whether the economic value produced by technology grows faster than the total cost of consuming it.\nThe community eventually gets a vote\nThe physical scale of AI creates another economic problem.\nThe costs and benefits of AI infrastructure frequently occur in different places.\nA community hosting a massive data center sees the land use, transmission lines, water consumption, electricity requirements and public incentives directly.\nBut much of the economic value may flow elsewhere — to technology companies, cloud providers, software developers, users and shareholders around the world.\nThat creates a reasonable local question:\nIf we absorb the costs, where is our share of the value?\nAs AI infrastructure expands, communities will increasingly demand answers. Data centers will require more than construction permits and electrical connections. They will increasingly require what might be called a social license to operate — evidence that the economic and societal benefits justify the resources being consumed.\nThe great measurement failure\nThis brings us to what may be AI’s biggest weakness.\nIt isn’t intelligence.\nIt is measurement.\nTraditional accounting was developed for an economy built primarily around physical assets, labor and financial capital. It tells us remarkably well what organizations spend and what they earn.\nBut technology has evolved from a supporting function into something closer to a factor of production.\nLike labor, technology performs work.\nLike capital, it requires investment.\nLike infrastructure, it enables economic activity.\nAI takes this one step further because organizations can increasingly acquire and consume machine intelligence almost as previous generations consumed electricity.\nYet our accounting and economic measurement systems have not caught up.\nThat is why I believe we need a discipline of technology economics.\nFrom technology management to technology economics\nTechnology economics asks a different set of questions from traditional IT management.\nNot simply: Does the system work?\nBut: What economic value does it create?\nNot simply: How much AI are we using?\nBut: How productive is that AI?\nNot simply: How much are we investing?\nBut: What return are we receiving relative to the capital consumed?\nThe framework I propose includes measures such as IT intensity, IT inflation, technology cost of capital, AI expense per share, AI value per share, margin per unit of IT intensity, the technology leadership index and gross domestic intelligence.\nConsider just two of these.\nAI expense per share asks shareholders to view AI investment in familiar economic terms: how much AI expenditure is effectively being borne per share?\nAI value per share asks the other half of the equation: how much measurable value is AI generating for those shareholders?\nSimilarly, margin per unit of IT intensity asks whether an organization is efficiently converting its technology dependency into economic performance.\nTwo companies can spend similar amounts on AI and produce dramatically different results. The winner shouldn’t be the company that consumes the most intelligence. It should be the company that converts intelligence into the most value.\nAI needs its own measurement revolution\nEvery major economic transformation eventually produced new ways of measuring itself.\nIndustrialization gave us modern accounting.\nMass production drove the development of productivity measurement.\nGlobal finance produced increasingly sophisticated theories of capital, risk and return.\nThe Intelligence Economy will need its own measurement revolution.\nCompanies may eventually need something resembling a Technology Balance Sheet showing their productive technology assets and obligations, and a Technology Income Statement connecting technology expenditures with the economic value those expenditures generate.\nInvestors will increasingly expect companies to disclose not merely how much they are spending on AI, but what they are receiving in return.\nTechnology executives, consequently, will need to become economic leaders as well as technical leaders. Boards will increasingly expect CIOs and other technology leaders to explain affordability, productivity and value — not simply reliability, security, modernization and innovation.\nThe same problem exists at the national level.\nGDP measures economic activity remarkably well, but it was never designed to measure intelligence as a productive resource.\nThat suggests the need for complementary concepts such as gross domestic intelligence: an attempt to understand the intelligence capacity that increasingly enables economic activity.\nWhether that particular measure ultimately becomes standard is less important than the underlying idea.\nIf intelligence has become an economic resource, eventually we will have to measure it as one.\nThe question that comes after the hype\nNone of this argues that AI will fail.\nQuite the opposite.\nAI may transform productivity, science, medicine, education, finance, manufacturing and almost every other significant area of human activity.\nBut technological capability does not repeal economics.\nThe railroad was transformative. So was electricity. So were telecommunications and the Internet. Each also experienced periods when infrastructure investment ran ahead of sustainable economic returns.\nAI may follow a similar pattern.\nThe critical divide may therefore not be between companies that adopt AI and companies that don’t.\nIt may be between those that understand the economics of intelligence and those that simply consume more of it.\nWe already know how to make machines increasingly intelligent.\nThe next challenge is learning how to make that intelligence economically productive, affordable and sustainable.\nCommunities will want evidence of societal value. Investors will want evidence of shareholder value. Boards will want evidence of business value. Governments will want evidence of national value.\nAll of those demands eventually converge on one question:\nWhat is intelligence worth?\nThe industrial revolution created modern accounting. The information age created digital technology management. The Intelligence Economy may require technology economics.\nAnd the most important innovation of the AI era may ultimately turn out not to be another model, another chip or another trillion-dollar generation of data centers.\nIt may be the measurement system that finally allows us to answer the question behind all of them:\nDoes the value created by intelligence exceed the cost required to produce it?\nEverything else follows from the answer.","reading_time_min":8,"extraction":{"state":"ok","confidence":0.9,"error":null,"explanation":"High confidence: full text extraction produced 10800 characters.","diagnostics_url":"/api/diagnose?url=https%3A//www.cio.com/article/4225695/the-cost-of-intelligence.html","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 10800 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":10800,"summary_length":320,"usable_text_length":10800,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":10800,"summary_length":320}}},"quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 10800 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":10800,"summary_length":320,"usable_text_length":10800,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":10800,"summary_length":320}},"actions":{"read":"/item/89691","export_markdown":"/api/items/89691/export?format=markdown","export_json":"/api/items/89691/export?format=json","diagnose":"/api/diagnose?url=https%3A//www.cio.com/article/4225695/the-cost-of-intelligence.html"}},"digest":{"id":89691,"title":"The cost of intelligence? - cio.com","url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","source":"cio.com","topic":"ai","published_at":"2026-09-24T13:02:40+00:00","excerpt":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:…","quality_bucket":"high","quality_reason":"High confidence: full text extraction produced 10800 characters.","reading_time_min":8,"cluster_id":null},"card":{"display_title":"The cost of intelligence? - cio.com","subtitle":"cio.com · 2026-09-24","summary":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited…","badges":["quality:high"],"links":{"read":"/item/89691","original":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","diagnose":"/api/diagnose?url=https%3A//www.cio.com/article/4225695/the-cost-of-intelligence.html"},"quality_warning":null},"export":{"title":"The cost of intelligence? - cio.com","url":"https://www.cio.com/article/4225695/the-cost-of-intelligence.html","summary":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?","source":"cio.com","date":"2026-09-24T13:02:40+00:00","content":"Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:\nDoes AI create more value than it costs?\nMy position is that the ultimate constraint on artificial intelligence may not be chips, algorithms, data or even electricity. It may be economics.\nWe are becoming extraordinarily good at producing machine intelligence. We are far less capable of measuring what that intelligence is actually worth.\nAnd that gap could become one of the defining economic problems of the AI era.\nWe are building factories for intelligence\nAI is usually described as software. Increasingly, that description is misleading.\nBehind every AI prompt is an enormous physical industrial system: semiconductors, electrical generation, transmission networks, data centers, cooling systems, storage, telecommunications, software and people.\nAI mega-data centers are, in effect, the factories of the Intelligence Economy. Instead of turning steel into automobiles, they turn electricity and computation into predictions, recommendations, decisions, software, images, knowledge and other forms of machine-generated intelligence.\nThis changes the economics of computing.\nIntelligence now has a cost of production.\nAnd unlike the Internet services we became accustomed to thinking of as almost weightless, AI consumes very tangible resources: capital, land, electricity, water, chips, networks and increasingly scarce technical talent.\nThat is why the enormous investment in AI infrastructure matters. We aren’t simply financing another generation of software. We are financing the industrialization of intelligence.\nThe AI infrastructure paradox\nThis leads to what I call the AI infrastructure paradox.\nThe world is committing extraordinary amounts of capital and physical resources to producing intelligence before we have developed equally sophisticated ways of measuring the economic value of the intelligence being produced.\nWe can measure GPU utilization.\nWe can calculate cloud costs almost to the penny.\nBut ask a corporation a different question — exactly how much incremental economic value did your AI produce? — and the answer frequently becomes much less precise.\nThat is a serious problem.\nA company processing billions of AI prompts is not necessarily creating more economic value than one processing a few million. Deploying thousands of copilots or agents tells us that AI is being used. It doesn’t tell us whether the company has become proportionately more productive, profitable, innovative or competitive.\nWe are becoming extraordinarily sophisticated at measuring the consumption of intelligence while remaining surprisingly primitive at measuring its economic productivity.\nSomeone ultimately has to pay\nThere is another side of the AI boom that receives far less attention: affordability.\nData centers cost money. Chips cost money. Electricity costs money. Water, networks, cybersecurity, governance, data engineering and specialized talent all cost money.\nThose costs cannot simply disappear.\nUltimately, they must be absorbed somewhere — by consumers through higher prices, enterprises through higher technology spending, shareholders through lower margins, or governments and taxpayers through subsidies and infrastructure investment.\nThis is where AI encounters an economic constraint that no increase in model intelligence can eliminate.\nIf the cost of intelligence grows faster than the value of intelligence, eventually something has to give.\nThis also challenges one of the most persistent assumptions of the technology industry: that technology inevitably gets cheaper.\nIndividual units of computing may indeed become cheaper. But organizations can still spend more because they consume vastly more computing, cloud capacity, cybersecurity, software, data and AI.\nI call this phenomenon IT Inflation.\nTechnology can simultaneously become more efficient and more expensive.\nThe important economic question, therefore, isn’t whether the cost of a token, GPU operation or gigabyte falls. It is whether the economic value produced by technology grows faster than the total cost of consuming it.\nThe community eventually gets a vote\nThe physical scale of AI creates another economic problem.\nThe costs and benefits of AI infrastructure frequently occur in different places.\nA community hosting a massive data center sees the land use, transmission lines, water consumption, electricity requirements and public incentives directly.\nBut much of the economic value may flow elsewhere — to technology companies, cloud providers, software developers, users and shareholders around the world.\nThat creates a reasonable local question:\nIf we absorb the costs, where is our share of the value?\nAs AI infrastructure expands, communities will increasingly demand answers. Data centers will require more than construction permits and electrical connections. They will increasingly require what might be called a social license to operate — evidence that the economic and societal benefits justify the resources being consumed.\nThe great measurement failure\nThis brings us to what may be AI’s biggest weakness.\nIt isn’t intelligence.\nIt is measurement.\nTraditional accounting was developed for an economy built primarily around physical assets, labor and financial capital. It tells us remarkably well what organizations spend and what they earn.\nBut technology has evolved from a supporting function into something closer to a factor of production.\nLike labor, technology performs work.\nLike capital, it requires investment.\nLike infrastructure, it enables economic activity.\nAI takes this one step further because organizations can increasingly acquire and consume machine intelligence almost as previous generations consumed electricity.\nYet our accounting and economic measurement systems have not caught up.\nThat is why I believe we need a discipline of technology economics.\nFrom technology management to technology economics\nTechnology economics asks a different set of questions from traditional IT management.\nNot simply: Does the system work?\nBut: What economic value does it create?\nNot simply: How much AI are we using?\nBut: How productive is that AI?\nNot simply: How much are we investing?\nBut: What return are we receiving relative to the capital consumed?\nThe framework I propose includes measures such as IT intensity, IT inflation, technology cost of capital, AI expense per share, AI value per share, margin per unit of IT intensity, the technology leadership index and gross domestic intelligence.\nConsider just two of these.\nAI expense per share asks shareholders to view AI investment in familiar economic terms: how much AI expenditure is effectively being borne per share?\nAI value per share asks the other half of the equation: how much measurable value is AI generating for those shareholders?\nSimilarly, margin per unit of IT intensity asks whether an organization is efficiently converting its technology dependency into economic performance.\nTwo companies can spend similar amounts on AI and produce dramatically different results. The winner shouldn’t be the company that consumes the most intelligence. It should be the company that converts intelligence into the most value.\nAI needs its own measurement revolution\nEvery major economic transformation eventually produced new ways of measuring itself.\nIndustrialization gave us modern accounting.\nMass production drove the development of productivity measurement.\nGlobal finance produced increasingly sophisticated theories of capital, risk and return.\nThe Intelligence Economy will need its own measurement revolution.\nCompanies may eventually need something resembling a Technology Balance Sheet showing their productive technology assets and obligations, and a Technology Income Statement connecting technology expenditures with the economic value those expenditures generate.\nInvestors will increasingly expect companies to disclose not merely how much they are spending on AI, but what they are receiving in return.\nTechnology executives, consequently, will need to become economic leaders as well as technical leaders. Boards will increasingly expect CIOs and other technology leaders to explain affordability, productivity and value — not simply reliability, security, modernization and innovation.\nThe same problem exists at the national level.\nGDP measures economic activity remarkably well, but it was never designed to measure intelligence as a productive resource.\nThat suggests the need for complementary concepts such as gross domestic intelligence: an attempt to understand the intelligence capacity that increasingly enables economic activity.\nWhether that particular measure ultimately becomes standard is less important than the underlying idea.\nIf intelligence has become an economic resource, eventually we will have to measure it as one.\nThe question that comes after the hype\nNone of this argues that AI will fail.\nQuite the opposite.\nAI may transform productivity, science, medicine, education, finance, manufacturing and almost every other significant area of human activity.\nBut technological capability does not repeal economics.\nThe railroad was transformative. So was electricity. So were telecommunications and the Internet. Each also experienced periods when infrastructure investment ran ahead of sustainable economic returns.\nAI may follow a similar pattern.\nThe critical divide may therefore not be between companies that adopt AI and companies that don’t.\nIt may be between those that understand the economics of intelligence and those that simply consume more of it.\nWe already know how to make machines increasingly intelligent.\nThe next challenge is learning how to make that intelligence economically productive, affordable and sustainable.\nCommunities will want evidence of societal value. Investors will want evidence of shareholder value. Boards will want evidence of business value. Governments will want evidence of national value.\nAll of those demands eventually converge on one question:\nWhat is intelligence worth?\nThe industrial revolution created modern accounting. The information age created digital technology management. The Intelligence Economy may require technology economics.\nAnd the most important innovation of the AI era may ultimately turn out not to be another model, another chip or another trillion-dollar generation of data centers.\nIt may be the measurement system that finally allows us to answer the question behind all of them:\nDoes the value created by intelligence exceed the cost required to produce it?\nEverything else follows from the answer.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//www.cio.com/article/4225695/the-cost-of-intelligence.html","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 10800 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 10800 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":10800,"summary_length":320,"usable_text_length":10800,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":10800,"summary_length":320}},"tags":[],"format_contract_version":"news_item_formats.v1"}}}