# Attentio — Full Site Content > Attentio helps businesses find where AI can save time, then builds practical workflow automation that fits the way teams already work. - Website: https://attentio.ai - Email: hello@attentio.ai - LinkedIn: https://www.linkedin.com/company/attentioai/ - Headquarters: Minneapolis, MN, USA - Service area: United States --- ## Home (https://attentio.ai/) ### Hero **We set up AI for your business, so you don't have to figure it out.** You tell us what's eating up your time. We find the smartest place to start, build something that actually works, and keep improving it from there. ### Sound familiar? You're probably dealing with this right now: 1. **You're doing the same tasks over and over.** Stuff that should take five minutes somehow eats up your whole morning. 2. **Your tools don't talk to each other.** Things get lost between your inbox, your spreadsheets, and whatever app you used last. 3. **AI sounds great, but where do you even start?** Everyone's talking about it, but most of what's out there feels overcomplicated or irrelevant. ### How it works — Three steps. That's it. 1. **We find the pain point.** We look at how your business runs and find the thing that's wasting the most time. 2. **We build the fix.** No big rollout. We start with one thing that makes a real difference, fast. 3. **We keep making it better.** Once the first fix is working, we build on it based on what your business actually needs. ### Final CTA **Let's start with the thing that's bugging you the most.** Book a quick call. We'll figure out where AI can help. No jargon, no pressure. --- ## The Process (https://attentio.ai/process) ### The Process The key to success is starting focused and simple. Then expand. "Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away." — Antoine de Saint-Exupéry ### Detangling AI Transformation When you hear the words AI Transformation your immediate reaction is probably that it sounds too complex or too costly. We are here to show you it does not have to be either. Transformation starts with understanding your business in detail and finding the right entry points to drive real outcomes. Not more billables. Not a lengthy roadmap. We start small, prove value fast, and expand from there. ### Your Change Management Partner We are not a dev shop, an app developer, or an outsourced AI team. We do not build something, hand it over, and disappear. Our goal is to make sure your team actually understands, trusts, and gets the most out of what we build together. We sit alongside your team through deployment, provide the context and training needed to be self-sufficient, and stay involved until the solution is delivering the outcomes you expected. Your success is the measure of ours. ### Practical Systems Once we identify an operational bottleneck or an outcome that needs to be delivered inside your business, we build practical systems and workflows to get there. No overengineered solutions. No unnecessary complexity. Just reliable systems your team can trust, built around the way you already operate and designed to deliver a measurable return. ### Three steps. That is it. 1. **We find the pain** — We start by listening. Before we write a single line of code or make a single recommendation we spend time understanding how your business actually runs. We talk to the people doing the work, map where time is being lost, and identify the single highest-impact place to start. Most businesses have more than one problem worth solving. We help you focus on the one that moves the needle most right now. 2. **We build the fix** — No long rollout. No waiting six months to see value. We start on your highest-impact problem and get to work immediately. Solutions are built in days to weeks not months. We design around your existing processes so we do not disrupt what your team is already good at. No boiling the ocean. One focused solution with a clear path to value. Then we expand. 3. **We keep making it better** — Change does not happen overnight and we do not expect it to. We do not build something and leave you in flux. We partner with your team to keep improving what we have built, drive adoption, and listen to your feedback. Once we are reliably delivering on the first solution we branch out to tackle the next bottleneck and build from there. ### Final CTA Book a workflow review at https://attentio.ai/process. --- ## Origin (https://attentio.ai/origin) ### Research Driven. Application First. Model alignment, training, and system behavior We spent years studying how AI systems learn, adapt, and fail. That foundation shapes everything we do. We are not consultants who learned to use AI tools last year. We are researchers who decided it was time to put that knowledge to work where it actually matters. ### Taking Off the Lab Coat From controlled loops to operational reality AI innovation is largely happening in closed environments with little connection to how businesses actually operate. The companies driving the economy forward are not the ones with the most sophisticated models. They are the ones with the most pressing problems and the least support solving them. We are here to change that. ### What It Actually Means to Be AI First Real leverage over expensive distraction Being AI first means being honest about where AI creates real leverage and where it does not. In the right context it is a genuine force multiplier. In the wrong one it is an expensive distraction. We help you find the difference and we tell you the truth either way. ### Our Values Quality you feel in the work, not a list on a page If we have to list them here we are probably not living them. You will understand our values by working with us. What we can tell you is that we care about what we deliver and how we deliver it. We hold ourselves to a standard we would want to be held against. ### Final CTA **AI first means knowing when to deliver AI.** Book a workflow review. --- ## Perspectives (https://attentio.ai/perspectives) Ideas, experiments, and lessons — unfiltered thoughts from our work driving practical AI adoption. ### Featured post **Be Like Water** — SaaS asked your business to take the shape of the software. AI is finally flipping that. Here is what "be like water" actually looks like in 2026. ### Latest posts - **What Is Vibe Consulting? The Risks of AI-Driven Advisory Work** — AI can compound an expert's judgment, or manufacture the appearance of having it. The difference is who's at the keyboard. --- ## AI Readiness Assessment (https://attentio.ai/ai-readiness) ### Hero **How AI-ready is your organization?** The value isn't in the chat window. It's in the workflows, data, and systems that run your business. Companies getting real returns from AI aren't the ones with a tab open. They're the ones who have embedded the technology into how their operation actually works. Attentio's AI readiness assessment shows you where your organization stands today and where AI can be built in to do real work. ### Stats - **95%** of enterprise generative AI pilots fail to produce measurable P&L impact *(MIT NANDA, 2025)* - **2%** of organizations rank as "highly ready" to scale the AI they're implementing *(F5 State of AI App Strategy, 2025)* - **16%** of knowledge workers say their company has well-documented workflows *(Lucid AI Readiness Report, 2025)* ### The Research — The AI readiness gap is real, and measurable. MIT's NANDA initiative published *The GenAI Divide: State of AI in Business 2025* in August. After 150 leader interviews, 350 employee surveys, and a review of 300 public deployments, the researchers found that 95% of enterprise generative AI pilots fail to produce measurable P&L impact. Only 5% drive meaningful revenue, against $30 to $40 billion in enterprise spending. The reason is not model quality. It is what the MIT authors call a "learning gap" between the tools and the workflows they sit inside. Other 2025 research from F5, Cisco, McKinsey, Lucid, and Deloitte points to the same conclusion. Key findings: - **F5, 2025**: 96% implementing, 2% ready to scale. Adoption is nearly universal. Operational readiness is almost nonexistent. - **Cisco AI Readiness Index, 2025**: 76% of Pacesetters have fully centralized data vs. 19% overall. The companies pulling ahead have done the unglamorous data work first. - **McKinsey State of AI, 2025**: 88% use AI in at least one function. Only 39% see enterprise EBIT impact. Usage is not value. Most deployments never reach the bottom line. - **Lucid AI Readiness Report, 2025**: 61% say strategy is misaligned with operational capabilities. Leadership ambition is outrunning what the operation can actually support. - **Deloitte State of AI in the Enterprise**: 66% report productivity gains. Only 20% are growing revenue. Productivity is easy to claim. Revenue is the real measure. ### Why It Happens AI readiness is the gap between buying tools and getting value from them. The pattern is consistent across companies of every size. Off-the-shelf tools are flexible enough for individuals but stall against company-wide workflows. Custom internal builds run for months before hitting the integration, data, or governance walls that the pilot phase never had to clear. Buying from specialized vendors and partnering for delivery succeeds about 67% of the time. Internal builds succeed about a third as often. ### What you walk away with - **A clear-eyed analysis** — Where the most important problems live in your business, and which ones are actually ready for AI to solve. - **A clear path forward** — Our team works alongside yours to build the solutions. If the foundations aren't there, we tell you which ones to fix first. Either way, you leave with a plan, not a sales pitch. ### Final CTA **Schedule your AI readiness assessment.** Thirty minutes to find out where your organization actually stands. --- ## AI Strategy Consulting Minneapolis (https://attentio.ai/ai-strategy-consulting-minneapolis) ### Hero **Your business is leaving time and money on the table. We'll show you exactly where AI turns that around.** We find the highest-impact opportunity in your operations and deliver a solution that proves itself fast. No long rollouts. Just results. ### Industry stats - **$3.70** returned for every dollar invested in AI by early adopters. *(Fullview AI Statistics, 2025)* - **15.8%** average revenue increase reported by companies implementing AI. *(Articsledge AI Business Analysis, 2025)* - **54%** of business leaders say their company won't stay competitive without AI. *(Mercer, 2025)* ### Pain points we hear - **Time going to the wrong things.** Your best people are buried in tasks that should run themselves while real growth work waits. - **Tools that don't talk to each other.** Work slips between inboxes, spreadsheets, and apps. Every handoff costs time and opportunities. - **AI matters, but where does it pay off?** Most AI projects never deliver. Real impact comes from knowing exactly where to start. ### Our approach — AI Strategy Consulting Built for Minnesota Businesses Most conversations about AI start with the tools and work backwards to the business. We do the opposite. We start with your operations and build only what reliably delivers an outcome. If technology is not the right answer we will tell you that too. - **Operations First** — We map where time is lost, revenue leaks, and where work should run itself. AI only comes in if it's the right tool for the outcome. - **Built to Work** — We ship one solution, prove it works inside your business, then build on it. No long rollouts. - **Adoption** — Building it is half the job. Adoption is the other half. We work alongside your team through the transition so it sticks naturally, not by force. ### The process 1. **Find the Opportunity** — We map your operations and pinpoint the single highest-impact place AI can drive results. 2. **Build the Solution** — No drawn-out rollout. We ship one thing that moves the needle fast and prove it works. 3. **Keep Improving** — Once the first win lands, we build on it. Your operation gets smarter and more profitable. ### FAQ **Q: What does an AI strategy consultant actually do?** We look at how your business runs, find where time and revenue are being lost, and identify the single highest impact place to apply technology first. Then we build it. No generic frameworks. No strategy documents. Just a working solution inside your actual operations. **Q: How long does it take to see results?** Most clients see measurable impact within 30 days of the first build. We start with the highest impact opportunity specifically so you see results before committing to anything long term. The goal is a fast, provable first win. **Q: How is Attentio different from other AI consulting firms?** Our team started in the research space studying how AI models learn and adapt. We are applying those learnings and our own ongoing research to deliver practical outcomes for real businesses, something that cannot be accomplished in a lab alone. Most AI consulting starts with the tools and works backwards to the business. We do the opposite. We start with your operations, build only what reliably delivers an outcome, and stay involved after the build to make sure it keeps working. We measure our success by your results, not the complexity we introduce. **Q: Do I need technical knowledge to work with Attentio?** None. We handle all of the complexity on our side. Your team stays focused on the work that grows the business. Everything we build is designed to be used and understood by the people actually doing the work, not just the people who built it. **Q: What kinds of businesses do you work with?** We work with businesses of every size and industry. What they tend to have in common is a real operational problem that is costing them time, money, or both. We are selective about the engagements we take on because we are committed to delivering something that genuinely moves the needle. If we do not believe we can have a meaningful impact on your business we will tell you that upfront. The right fit matters more to us than the number of clients we work with. **Q: What does the first engagement look like?** It starts with a workflow review. We spend 30 minutes learning about your business and where the biggest opportunity lives. From there we go deeper, spending time with you and your team, in person if needed, to understand the practical realities of how you actually work. That context shapes everything we build. Once we have a clear picture we identify the highest impact place to start and propose a first build around that. You see exactly what we are going to do and why before anything gets built. **Q: What if AI isn't the right solution for my problem?** We will tell you. We do not apply technology for the sake of it. If a simpler solution solves the problem better we will say so. Our reputation is built on delivering outcomes that work, not on selling engagements that sound impressive. ### Final CTA **Start with the opportunity that matters most.** Book a workflow review at https://attentio.ai/ai-strategy-consulting-minneapolis. --- ## Perspectives / Be Like Water (https://attentio.ai/perspectives/be-like-water.html) > Category: Perspectives | Date: May 1, 2026 | 8 min read When Bruce Lee was asked to explain his philosophy on adaptability, he gave one of the more famous answers in modern popular culture: > "Be water, my friend. Empty your mind. Be formless, shapeless, like water. You put water into a cup, it becomes the cup. You put water into a bottle, it becomes the bottle. Put water into a teapot, it becomes the teapot." He was talking about fighting. But if you have been running a business lately, you probably hear something different in it. The world around you has been reshaping itself faster than most organizations can keep up with. Customer expectations are shifting, and the technology businesses are starting to rely on is almost unrecognizable from what it was just a few years ago. For most of the last fifteen years, the response to shifts in the market followed this pattern: You bought software. Then you bought more software. You wired the new software to the old software using middleware and integration platforms. When that broke, you bought a fourth tool to monitor the first three. According to BetterCloud's State of SaaS research, the average company runs 106 SaaS applications, with large enterprises above 5,000 employees averaging 131. Roughly half of those licenses go unused. That is the visible cost. The invisible one is the operational drag: workflows split across tools that were never meant to talk to each other, with seams patched by whichever engineer happened to be around when something broke. This was the deal we made. SaaS gave us speed and modularity in exchange for fit. A vendor would build something for the broad middle of the market, and you would adapt your processes to match. If the tool covered 50% of what you actually needed, that was considered reasonable. Sixty percent was a win. The remaining gap was your team's job to close with spreadsheets, Zapier, and patience. A lot of that vendor debt never went away. It accumulated. Every layer of duct tape stayed in production long after the person who taped it together had moved on. ## What changed At the height of SaaS, the marginal cost of customization was high. Asking a vendor to build a feature for one customer made no economic sense. The shape of the cup was fixed because changing it was too expensive. That math has flipped. In GitHub's controlled research, developers using AI coding assistants completed tasks 55% faster, with average completion times falling from 2 hours 41 minutes to 1 hour 11 minutes. GitHub also reports that Copilot now generates an average of 46% of the code written by users on its platform, with Java developers reaching 61%. Take that out of the developer world for a second. What used to be a six-week build is now a six-day build. What was a custom integration project quoted at $40,000 is something a small team can prototype on a Tuesday afternoon. The economics of "make it fit" have changed by an order of magnitude. Sequoia's Julien Bek captured the broader shift in a March 2026 piece called Services: The New Software. His opening line: the next trillion-dollar company will be a software company masquerading as a services firm. The math behind that claim is striking. For every dollar businesses spend on software, they spend six on services. AI is starting to collapse that ratio. Work that used to require teams of people is becoming software, and software is reaching into the much larger services budget. The implication for buyers is just as real. Solutions can now form around how your business actually operates, instead of you forming around the software. In practical terms, the fit you should expect is no longer 50%. With the right approach, it is 80% or higher. ## You can be like water, literally Here is where the metaphor stops being decorative and starts being something you can actually do. A business that is "like water" is not one that flexes for the sake of flexing. Water has a property worth thinking about. It takes the shape of whatever it is in, but it remains water. It does not become the cup. The cup just becomes its boundary. When the conditions change, water moves. The companies that will win the next decade share that property. They are clear on what they are and what makes them genuinely different from everyone else in their category. The shape of the operations around that core can change quickly. The core does not. Until recently, that was a nice idea with no real path to execution. The cost of reshaping your operations was too high to do it often. The tools and the partners to do it well were not available at any reasonable price. So most companies talked about agility and practiced rigidity. They subordinated their operational details to whichever platform they bought. You got Salesforce, and your sales process was now whatever Salesforce thought a sales process was. You got NetSuite, and your finance team learned to think in NetSuite's data model. The platform was the lake. You were a fish in it. Going forward, the lake can move with you. That is a real shift, and it has implications for how you make decisions about technology and partners. ## Reading the current faster than your competition One immediate consequence is that the cost of paying attention has dropped. When acting on a trend used to require six months of platform reconfiguration, most companies did not bother. They reacted late or not at all. The trend would be played out by the time the new workflow was live. Now, a retailer noticing a shift in how customers ask about a product category can stand up a new internal tool to track and respond in days. A clinic seeing new patterns in patient intake can adjust its workflow without filing a ticket with a vendor in Atlanta. The companies that take this seriously will be the ones who reposition fastest when something in their market moves. If your competitor can adapt in weeks and you take quarters, the gap compounds. Quickly. ## Knowing what you are not Now, the part that most thought leadership posts skip. Being like water sounds liberating until you actually have to run a company. Most operators reading this already know the bottleneck is not enthusiasm for AI. It is bandwidth. You have a product to ship, and a team that is already running close to full. The opportunity to reshape your operations exists, but the hours to do it do not. This is where the philosophy gets practical. Adapting quickly does not mean doing everything yourself. It means knowing what you should hold close and what you should hand off. The work that requires your specific expertise, customer relationships, and judgment is the work you cannot delegate. The work that is real but not strategic is exactly the kind of work that should sit with someone whose entire job is to do it well. Historically, "bringing in help" meant one of two unappealing options. You could hire a big consulting firm that arrived with a slide deck and a methodology and tried to reshape your business to match. Or you could hire a body shop that gave you developers who would build whatever you specified, including the wrong thing if that was what you specified. Both models had the same underlying flaw. They started with what the firm wanted to sell and worked backward to your problem. The output looked impressive in the deck. It rarely fit how the business actually ran. ## The newer model: partners who shape themselves to you The version of partnership that makes sense in this era inverts the relationship. You bring in people whose expertise is technical and operational, not strategic. They are not trying to tell you what your business should be. They are trying to understand how it actually works, and then build solutions that match. This sounds like a small distinction. In practice it is the whole game. A partner who shapes themselves to your operations does a few things differently. They spend time understanding your business before recommending anything. They build with the assumption that your team will keep doing the work, and the technology should make that work lighter rather than replace the people who understand it. When the market moves and your operations need to shift, they shift with you instead of explaining why their initial blueprint is still correct. You can usually tell which kind of partner you are dealing with within the first few conversations. The wrong kind shows up with a product pitch and tries to map your situation onto it. The right kind asks questions, takes notes, and is comfortable saying they need to understand more before they propose anything. They are also comfortable telling you when something is not worth doing at all. This is the model that fits the era we are now in. Less "buy the platform and conform." More "build the platform around how you already work, with help from people who know how to build." ## What this looks like in practice Step back and consider what a company that takes this seriously actually does. It stops thinking of software purchases as long-term marriages. The strongest tools in the stack are the ones that can be replaced or reshaped as needs change. Lock-in gets treated as a cost rather than a feature. It invests in understanding its own operations more deeply than its vendors do. The knowledge of how the business actually works lives inside the company, not in a consultant's deliverable from three years ago. It picks partners who can move at the speed of the market rather than the speed of a procurement cycle. When something needs to change, the answer is not "let's revisit this in the next quarterly planning session." The answer is "what would we want to be true by next month?" And it accepts that some part of the operation will always be in motion. That is not a problem to solve. It is the shape of working in this decade. ## Be water, my friend Bruce Lee's original point was that rigidity loses fights. A fighter who has a fixed set of moves can be read and countered. A fighter who responds to what is actually happening in front of them, in the moment, has an enormous advantage. The same is true of businesses now. The advantage no longer goes to the company with the most comprehensive five-year technology roadmap. It goes to the company that can read the current and reposition without breaking what made it valuable in the first place. The good news is that the tools to do this exist in a way they did not before. The harder news is that adopting them well requires a kind of clarity about your own business that most organizations have never had to develop. You have to know what you are and where your real expertise sits. Once you know that, the rest can flow around it. That is, more or less, what being like water looks like in 2026. Stay clear about the core. Let the shape change. Bring in people who help you do both. --- ## Perspectives / What Is Vibe Consulting? (https://attentio.ai/perspectives/what-is-vibe-consulting.html) > Category: Perspectives | Date: May 6, 2026 | 3.5 min read In February 2025, Andrej Karpathy coined the term vibe coding. Vibe coding is where you describe, in natural language, what you want AI to build and watch the AI generate code, and accept what comes back without reading the diffs. Collins Dictionary made it Word of the Year for 2025. By late that year, developers on LinkedIn were renaming themselves "vibe code cleanup specialists" as a joke that had gotten all too real. A parallel pattern is forming in consulting. Some have started calling it vibe consulting. The category is still being defined, so let's start there. ## What vibe consulting actually is Vibe consulting is the practice of using AI to extrapolate a consultant's expertise. Research, market sizing, competitive analysis, strategic frameworks, and implementation plans, all of which used to require years of pattern recognition, are now a prompt away. At its best, this expands what a small expert team can deliver. At its worst, it lets people who lack the underlying expertise sound like they have it. The early warning signs are public and not coming from small or unknown firms. In May 2026, Sherwood News reported on a GPTZero investigation that found 60% of the references in a 44-page EY Canada advisory report were hallucinated. Citations linked to articles returning 404 errors. A McKinsey attribution traced back to a "Loyalty Economics Report" that did not exist. The same investigation found 19 hallucinations in a separate Deloitte report. Deloitte later issued a partial refund to the Australian government. A few weeks earlier, the law firm Sullivan & Cromwell acknowledged hallucinations in a filing it submitted to a federal bankruptcy court. These are not no-name shops. EY's consulting arm generated $16.4 billion in revenue last year. Deloitte and Sullivan & Cromwell are equally established. If it is happening there, it is happening everywhere. ## Human judgment is not going away The dominant story we keep hearing is that AI will eventually replace the individual consultant entirely. The data so far points the other way. In July 2025, METR published a randomized controlled trial on AI coding tools. Sixteen experienced open-source developers worked through 246 real tasks from their own mature projects. Half the tasks were randomly assigned to use AI (Cursor Pro with Claude 3.5 and 3.7 Sonnet), half were not. Before starting, the developers predicted AI would make them 24% faster. After finishing, they estimated AI had sped them up by 20%. The actual measured result: developers using AI took 19% longer to complete their tasks. Read that twice. Experienced developers, using the best available coding tools on their own code, all believed AI was speeding them up. It was slowing them down. The researchers attribute the gap to validation overhead. AI produced plausible-looking suggestions that needed careful checking, and the checking took more time than the suggestions saved. It is fair to ask what this means for the rest of this post. If experts get slower with AI, what is the case for using it at all? The honest answer is that METR does not say AI never helps experts. It says AI does not automatically help experts. The developers in the study used AI on tasks where verification cost more than the assistance was worth. The skill that produces gains, when gains are real, is the skill of choosing which tasks AI should touch and which it should not. That is itself a judgment call, and it is exactly the call that vibe consultants are not equipped to make. The implication for consulting is direct. The work AI is "doing for you" is work you still have to verify. If you have the expertise to verify it and the judgment to know when to apply AI in the first place, you can compound your output. If you do not, you are publishing whatever the model gives you and hoping. That is vibe consulting, and the EY and Deloitte cases are what it looks like in the wild. ## Where AI actually helps This is not a case against AI in advisory work. It is a case against using AI without judgment to know where it can be best applied. The win, when it exists, looks something like this. The operator decides what to draft from scratch, what to outline before handing off to AI, and when to skip using AI. Inside that frame, AI surfaces precedents from adjacent industries that a consultant would not have searched on their own. It generates counterarguments to a recommendation faster than a workshop room could. The operator filters what is useful from what is wrong, and ships work that would otherwise have taken a team of analysts a month. The difference is the human in the loop, and the difference before that is which tasks the human hands to the model. An experienced operator will notice that the AI's recommended cost-cutting plan eliminates a small team whose work does not appear in financial reports but quietly keeps the company compliant with regulations. A consultant without that operating background will not. The first uses AI to compound their judgment. The second uses it to manufacture the appearance of having it. ## The overconfidence problem There is one more pattern worth knowing about, because it makes the verification problem harder. A Carnegie Mellon study published in Memory & Cognition in July 2025 found that LLMs do not adjust their confidence after performing poorly. They often become more sure of themselves, not less. In one experiment, Google's Gemini predicted it would identify about 10 out of 20 hand-drawn sketches correctly. It got fewer than one right. Afterward, it estimated it had gotten about 14 right. That is the model you are taking advice from. Even when it is wrong, it sounds certain. Humans signal uncertainty through tone and hesitation. AI does not. A confident-sounding LLM answer with confident-sounding citations is exactly what the EY and Deloitte reports were made of. ## The optimistic future None of this is an argument against AI in consulting. It is an argument about who should be using it and how. PwC's April 2026 AI Performance Study of 1,217 senior executives found that 74% of the economic value being created by AI is being captured by just 20% of organizations. The pattern in that data is consistent. The winners are not the ones with the most advanced tools. They are the ones who know their domain and use AI to do more of what they already do well. The future of consulting is not vibe consulting. It is quieter and more selective. Experienced operators, choosing carefully where AI earns its keep and where it does not, delivering work that used to require teams several times their size. A small firm with deep expertise can compete with global brands because the volume work, on the tasks where AI actually accelerates it, is no longer the constraint. The expertise is. The discipline is the one programmers are working out right now. Review what the model produces and test it against your own knowledge of the domain. Trust it where you can verify it. The consultants who hold that line will scale. The ones who let the model do their thinking will publish reports with citations that do not exist. > "The choice is not hard. The value is in requiring a human to make it."