From tokens to trust: AI's US$2.5 trillion reckoning

Abhas Ricky

Jun 22

The era of speed and access is ending. What replaces it will define enterprise winners and losers.For two years, enterprise AI strategy ran on a single instinct: to get to the frontier fastest. The ...

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What replaces it will define enterprise winners and losers.For two years, enterprise AI strategy ran on a single instinct: to get to the frontier fastest. The ...</p>\n\n<a href=\"https://www.theedgesingapore.com/digitaledge/artificial-intelligence/tokens-trust-ais-us25-trillion-reckoning?utm_source=Blog&utm_medium=RSS&utm_campaign=Tiger_Brokers_app_RSS\">Source Link</a>\n\n</div>\n","source":"edge_highlight","html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>From tokens to trust: AI's US$2.5 trillion reckoning</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\nFrom tokens to trust: AI's US$2.5 trillion reckoning\n</h2>\n\n<h4 class=\"meta\">\n\n\n2026-06-22 06:26 GMT+8&nbsp;&nbsp;&nbsp;<a href=https://www.theedgesingapore.com/digitaledge/artificial-intelligence/tokens-trust-ais-us25-trillion-reckoning?utm_source=Blog&utm_medium=RSS&utm_campaign=Tiger_Brokers_app_RSS><strong>Abhas Ricky</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>The era of speed and access is ending. What replaces it will define enterprise winners and losers.For two years, enterprise AI strategy ran on a single instinct: to get to the frontier fastest. The ...</p>\n\n<a href=\"https://www.theedgesingapore.com/digitaledge/artificial-intelligence/tokens-trust-ais-us25-trillion-reckoning?utm_source=Blog&utm_medium=RSS&utm_campaign=Tiger_Brokers_app_RSS\">Source Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"API","symbol_name":"声网","start_time":0,"source_url":"https://www.theedgesingapore.com/digitaledge/artificial-intelligence/tokens-trust-ais-us25-trillion-reckoning?utm_source=Blog&utm_medium=RSS&utm_campaign=Tiger_Brokers_app_RSS","article_id":"2645074632","we_media_id":null,"thumbnails":[],"rights":{"source":"edge_highlight","url":"https://www.theedgesingapore.com/digitaledge/artificial-intelligence/tokens-trust-ais-us25-trillion-reckoning?utm_source=Blog&utm_medium=RSS&utm_campaign=Tiger_Brokers_app_RSS","rn_cache_url":null,"directOrigin":true},"url":"https://stock-news.laohu8.com/highlight/detail?id=2645074632","pubTimestamp":1782080761,"columns":[],"sourceInfo":{"source_id":"edge_highlight","name":"Edge"},"weMediaInfo":null,"summary":"The era of speed and access is ending. What replaces it will define enterprise winners and losers.","collect":0,"end_time":0,"defaultTopTitle":"theedgesingapore.com","property":[],"viewcount":null,"language":"en","relate_stocks":{"API":"声网","CHAT":"ROUNDHILL GENERATIVE AI & TECHNOLOGY ETF","BK4023":"应用软件","ARTY":"ISHARES FUTURE AI & TECH ETF","BK4535":"淡马锡持仓","AIPO":"Defiance AI and Power Infrastructure ETF","BK4503":"景林资产持仓","KYC":"Corgi Digital Banking & Fintech Infrastructure ETF","AGIX":"通用人工智能 ETF-AGIX","BK4551":"寇图资本持仓"},"translate_title":"从代币到信任：人工智能2.5万亿美元的清算","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"API":1,"KYC":1,"CHAT":1.5,"AGIX":1.5,"AIPO":1.5,"ARTY":1.5},"content_text":"The era of speed and access is ending. What replaces it will define enterprise winners and losers.For two years, enterprise AI strategy ran on a single instinct: to get to the frontier fastest. The default path was a public cloud account, an application programming interface (API) key from OpenAI or Anthropic, and a willingness to absorb cost in exchange for speed. That instinct produced extraordinary experimentation - and it is now hitting a wall.\nGartner forecasts worldwide AI spending will reach US$2.52 trillion ($3.26 trillion) in 2026, up 44% year on year, with US$1.37 trillion of that flowing into AI infrastructure alone.\nIn fact, in mid-2025, they  claimed that AI for procurement has entered a \"Trough of Disillusionment,\" where scaling depends on predictable ROI rather than visionary pilots. The pressure has shifted from how fast enterprises can pilot AI to whether they can sustain, govern, and defend it in production.\nFrom frontier access to inference economics\nWe are moving from AI 1.0, where access to cutting-edge models was the differentiator, to AI 2.0, where inference economics, data gravity, latency, and control decide the outcome.\nToken prices have fallen roughly tenfold every year since 2021, yet total AI spend at most enterprises has gone up, not down, because more capable models invite more ambitious workflows.\nAnthropic, OpenAI, and Mistral are now stratifying offerings between flagship reasoners and lower-cost workhorses precisely because customers refuse to pay flagship prices for every task. McKinsey's 2025 State of AI survey confirms the pattern - adoption is broadening, but scaled impact remains elusive at most organisations. The question chief information officers (CIOs) are asking is no longer which model but which workload runs where, at what cost, under whose policy.\nThe next best action test\nTake a familiar enterprise use case: a bank delivering the next best action - the in-app, in-branch, or call-centre recommendation served in milliseconds against a customer's live context. The best banks we work with proved that personalisation at this layer can lift revenue by 5-15%. A  global bank we work with launched an AI assistant that has already resolved more than 1.5 million customer inquiries in its first year.\nHowever, the inference math is unforgiving. A single agentic decision can chain five to twenty model calls, each carrying its own context window. The gap between US$0.50 and US$3.30 per million input tokens - trivial in a single-turn demo - becomes the difference between a margin-positive feature and one that quietly burns capital across hundreds of millions of customer events.\nRecent analyses suggest enterprises running a single top-tier model for every task are overspending 40-85% on inference. Decagon, after re-architecting onto an open-source multi-model stack on Nvidia Blackwell, dropped cost per voice query six times. The next best action isn't a marketing decision anymore; it's a unit economics decision, made one routed token at a time.\nSovereignty becomes the strategy\nThe public cloud versus private AI debate is no longer ideological - it is workload-specific, and geopolitics has entered the equation.\nThe EU AI Act's high-risk obligations enter full force in August 2026, carrying fines up to 35 million or 7% of global turnover; France and Germany are tilting national procurement toward Mistral and open-weight sovereign stacks.\nIn Asia, AI regulation is taking shape in markedly different ways. Singapore's Model AI Governance Framework and IMDA testing tools have become regional templates.\nIn the east, Japan's AI Promotion Act layers sector rules atop voluntary guidance; South Korea's AI Basic Act mandates liability insurance for high-risk systems.\nMeanwhile, India launched its sovereign LLM at the February 2026 AI Impact Summit and is channeling $1.25 billion into the IndiaAI Mission, with the DPDP Act phasing through 2027.\nChina's state-directed open-source push, Indonesia's PDP Law, and Australia's pragmatic sector approach complete a map where no two jurisdictions look alike - and 96% of APAC organizations planned to increase AI investment, mostly via hybrid infrastructure. A single-cloud, single-jurisdiction AI architecture is now a structural liability.\nOur hybrid inference work with Nvidia, and the broader shift toward on-prem AI for regulated workloads, is a direct response.\nThe real moat is above the model\nThe hardest lesson of the past 18 months is that model commoditisation does not reduce enterprise complexity but relocates it. Open weights from Mistral or DeepSeek cut experimentation cost, but orchestration, governance, evaluation, and integration burdens move up the stack and sit with the buyer.\nThe same dynamic is now playing out in physical AI and defense tech. Physical Intelligence, Figure AI, and Skild AI are pushing robot foundation models into factories, fulfillment centres, and homes where latency, sovereignty, and data residency matter more than benchmark scores.\nFei-Fei Li's World Labs is building the spatial intelligence layer - world models that perceive and reason in 3D - that will anchor the next generation of industrial digital twins.\nPalantir and Anduril have built entire franchises around the assumption that the control plane, not the model, is the durable advantage.\nEnterprise leaders should be measuring unit economics per useful task, operational burden per deployed agent, and the ratio of inference spent on the governance scaffolding around it. That ratio is typically 1:5 or worse.\nWhat's next for banks, telcos, factories\nA second architectural shift is arriving: sub-quadratic attention. Approaches from DeepSeek, Google, and Cartesia are collapsing the cost of long-context reasoning by orders of magnitude, with recent benchmarks showing 100 times to 300 times cost reductions at comparable accuracy.\nFor large banks, this turns whole-portfolio risk modeling, multi-decade fraud pattern detection, and cross-jurisdiction Know-Your-Customer (KYC) into single-pass operations rather than chunked retrieval workarounds.\nFor telcos, agentic network operations, predictive maintenance, and multi-year customer journey reasoning become economically defensible at scale.\nFor manufacturers, full-plant simulation and supply-chain disruption forecasting shift from periodic batch jobs to continuous reasoning.\nThe architecture that wins will not be the one with the cheapest token. It will be the one that places compute closest to the data, under the right jurisdiction, with governance that holds. Sustainable, sovereign, controlled - that is the new triad. The enterprises that build for it now will define the next decade.\nAbhas Ricky is the chief strategy officer at Cloudera","kind":"highlight","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":false,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"}}}