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56% of CEOs reported ZERO revenue from AI after 12 months. More than half of 4,454 CEOs surveyed by PwC across 95 countries came back with nothing. No revenue gain. No cost reduction. Only 12% got both. PwC calls that group the vanguard. Here's the part that should bother you more than the 56%: these are CEOs. The people with the most visibility into their own numbers, the most resources to measure anything they want, and an entire year to find evidence. They still couldn't prove a single dollar saved or earned. If the people at the top of the org chart can't answer this after twelve months, what makes you confident your own AI spend is any different? You don't need to wait 12 months to find out where you stand. We built the AI ROI Index to benchmark your AI against 1,500+ enterprise peers using live production data, showing you the hard-dollar return you can defend to your board. Just enter your company URL to get a free board-ready report in under 3 minutes. Get your report here: https://lnkd.in/gUTCwMCy

Meta launches its first coding agent. Muse Code (now in beta) is their direct counter to Claude Code and OpenAI's Codex. It handles large codebases by splitting work across parallel sub-agents. Zuckerberg said testing had it "build six features for a game simultaneously with no collisions." The real selling point? Cost. A cheaper way to generate code faster. Notice what none of these three companies are competing on. Right now, the whole AI space is racing on speed, context windows, and price per token. Nobody is racing to prove that the output was worth creating in the first place. Cheaper code still has to ship, get adopted, and hold up in production before it's worth anything. A discount on the tool doesn't put a single dollar back on your P&L. Only measuring the outcome does that. Three coding agents, three companies, and not one of them will tell you if the work they generate is actually paying off in your company. But we built something that will. It's called the AI ROI Index. It benchmarks your AI against 1,500+ enterprise peers using live production data, then maps out where value is trapped in actual dollars. Just enter your company URL, and you’ll get a free board-ready report in under 3 minutes. Get your report here: https://lnkd.in/gFaqsjW6

Grok is back, and it's coming for everyone. SpaceXAI just shipped Grok 4.5, its strongest coding and agentic model yet. Beats GPT-5.5 and Opus 4.8 on several engineering benchmarks. Priced at $2 and $6 per million tokens. By every spec, it's a genuinely strong model. Here's the question none of that answers: will it save or make you money? Every model launch resets the same conversation inside enterprises. Someone on the team wants to try it. Budget gets approved. Adoption climbs. And six months later, nobody can say whether it actually changed a business outcome, or just changed which vendor logo shows up on the invoice. That's not a Grok problem. It was true for GPT-5.5, and it'll be true for whatever ships next month. The model is never the bottleneck. Measurement is. This is why we built a tool called the AI ROI Index. So that you can find out is your AI producing value or just spending money. Benchmarked against 3M+ real users, 100,000+ agents, and 1,500+ enterprise companies. Try it for Free here: https://lnkd.in/geAPwGDt

How to Measure the ROI of AI? (Most Companies Get This Wrong) Why do 95% of enterprise AI programs fail to deliver measurable value? Because almost nobody measures ROI correctly in the first place. Here's what I see most companies doing wrong: 1. They measure adoption, not outcomes. "500 employees are using the AI tool" is not a business result. The question isn't who's using it, it's what it's worth. 2. They ignore the full cost stack. Compute, tokens, engineering time, maintenance. Most ROI numbers only count the visible spend and quietly drop the rest. 3. They self-report their own maturity. A survey where you grade your own homework and get a Strong/Weak label isn't evidence. It's an opinion with a scorecard attached. 4. They count pilots as wins. A pilot in a sandbox has generated exactly $0 of production value. If it hasn't shipped, it hasn't paid back anything yet. So how do you measure it correctly? Here's how we do it at Weaver: 1. Measure where value is trapped. Tie every capability to a dollar figure: trapped value, exposure, or unrealized EBITDA. Not a label, a number. 2. Benchmark against real peers, not yourself. Score against live production data from 1,500+ enterprise peers. Not a self-reported survey pretending to be evidence. 3. Prioritize by payback period. Walk into the board with the three initiatives that pay back fastest, not a 50-page roadmap nobody will read. If you want to know where you actually stand, we built a free tool for that. See where you rank with the AI ROI Index, scored against: • 3M+ users • 100,000+ agents • 1,500+ enterprise companies Free, done in a few minutes: https://lnkd.in/guT53zQ2

AI has no scoreboard. So we built one, and made it free. 95% of business AI programs fail to deliver measurable value. Boards are approving AI budgets at record levels, and most executives still can't answer two basic questions: 1. Are we getting a return? 2. And are we falling behind? We've normalized spending millions on AI while flying completely blind. Imagine running finance without accounting, or marketing without analytics. That's where most organizations are with AI today. So we built the AI ROI Index, and made it free! Six dimensions: 1. strategy, 2. governance, 3. technology, 4. data, 5. workforce readiness, 6. and production deployment. You get a peer percentile against 1,500+ enterprise organizations, an estimate of where AI value is being created or lost, and the three highest-impact actions to improve. It's calibrated on one of the largest enterprise AI production datasets anywhere: • 3 million enterprise users, • 100,000 deployed AI agents, • 2 years of live usage. Real behavior and real outcomes. This isn't another AI maturity survey. It's a living benchmark. We intend to keep it free, permanently. One more thing, because it matters: your data is confidential. We will never sell it, share it, or train on it. Trust is the whole model. Check and see where you stand: https://lnkd.in/gr4NCv6P

Uber burned its entire 2026 AI budget in four months on Claude Code. Here's how it happened... Uber gave 5,000 engineers Claude Code access. Four months later, their full-year AI budget was gone! In December 2025, Uber rolled out Claude Code to its engineering team. By February, 32% were using it, by March, 84%. Per-engineer spend ran $150 to $250 a month typically, with heavy users hitting $500 to $2,000. By April, Uber CTO confirmed the entire 2026 budget was exhausted. So what did Uber get for it? COO Andrew Macdonald said: "That link is not there yet." 70% of committed code is now AI-generated. Nobody can draw a straight line from that number to a single measurable gain in what customers actually get. That's not a Claude problem, or an Uber problem. It's what happens anywhere adoption outruns proof. The question was never "how many engineers are using it", it was always "what did it produce." Nobody asked the second question until the budget was already gone. This is why we built a tool called the AI ROI Index. It benchmarks your AI against 1,500+ enterprise peers on live production data. You find out, in dollars, whether what you've built is making money or just costing more to manage. Free and it takes only a few minutes: https://lnkd.in/gMPaDHZE