Blog, page 6
Amit's notes
My personal views only.
AI anxiety is not about the technology - it is about losing control
AI anxiety affects 75 percent of employees according to EY research, but they are not afraid of algorithms. They are afraid of losing control over how AI reshapes their work and their future.
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Unit economics of generative AI products - why most lose money
Most generative AI products have negative unit economics and lose money on every user. Even OpenAI and Anthropic are losing billions despite massive revenue. Here is the uncomfortable reality about AI product profitability and what it takes to build sustainable businesses.
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Your AI steering committee needs power, not just opinions
Most AI steering committees fail because they are designed to discuss, not decide. ISO/IEC 42001 requires clear decision-making authority over the AI lifecycle, and IAPP research finds 77% of organizations still building their AI governance. The difference between effective and ineffective committees is not expertise - it is authority.
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How we replaced our SOC 2 compliance platform with AI and Google Drive
SOC 2 compliance platforms charge thousands annually for what is essentially organization software. We moved to a Git repository, Google Drive for auditor access, and AI for the tedious work. The only cheque we write now goes to our CPA firm for the actual audit.
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How to build a free website with Astro and Cloudflare Pages using Claude Code
Build a production-grade personal website with Astro and Cloudflare Pages at zero hosting cost. Claude Code handles all setup, configuration, and deployment without needing DevOps experience.
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Forward deployed engineer: Why this role demands real technical depth
Forward deployed engineers bridge the gap between software platforms and customer reality. The role, pioneered at Palantir and seeing 800% growth in job postings, fails catastrophically when filled by people without real coding skills.
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AI errors need AI-level explanations
AI systems fail gradually and partially, not in clear binary states like traditional software. Most AI proof-of-concepts never reach production. The model gives a plausible answer missing important context, latency spikes but stays under timeout limits, outputs degrade invisibly. Your AI error handling must match this reality.
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AI success metrics: the complete guide
Most teams measure AI wrong, tracking model accuracy instead of business outcomes. A Forbes study found 39% of executives cite measuring ROI and business impact as a top challenge. This guide covers the four measurement layers that matter, dashboard design for decisions, and why infrastructure determines what you can measure.
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Azure OpenAI vs OpenAI: the enterprise decision
Azure OpenAI offers over 100 Microsoft compliance certifications but trails OpenAI on new platform features and APIs. It is insurance, not improvement. Here is how to choose.
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Build vs buy AI - why your leadership does not understand either choice
Companies waste millions choosing build or buy based on cost spreadsheets. RAND Corporation research shows more than 80 percent of AI projects fail at twice the rate of regular IT projects. The real decision is whether your middle managers understand AI well enough to use whatever you build or buy.
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Career paths in the AI era - embrace AI or be replaced by someone who does
The real career threat is not AI replacing you - it is being replaced by someone who learned to work with AI while you did not. The World Economic Forum projects 22 percent of jobs will be disrupted by 2030. Here is how to build career resilience through human-AI collaboration.
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Chain-of-thought prompting for business users
Chain-of-thought prompting is debugging for AI decisions. IBM research confirms it boosts performance on complex reasoning by making each logical step visible and auditable before the answer lands.
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Claude Artifacts for enterprise workflows - replacing expensive tools with AI
Mid-size companies spend tens of thousands annually on workflow tools that fragment their operations. Claude Artifacts offers a unified AI-powered workspace, with some teams achieving full ROI within three months.
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Claude Code test generation - the 80% coverage sweet spot
Your codebase sits at 40% test coverage, three people understand your critical systems, and hiring QA engineers costs more than your tooling budget. Claude Code test generation writes thorough tests that catch edge cases developers miss, and those tests double as living documentation for teams too small for dedicated QA.
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Creating effective AI simulations for training
University of Chicago research reveals people learn less from their own failures than successes due to ego protection. The solution is not avoiding mistakes but designing AI training simulations that create safe environments where controlled failure accelerates learning without the psychological cost.
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Knowledge graphs vs vector search: Why the hybrid approach wins
Choosing between knowledge graphs and vector databases is a false choice. Knowledge graphs excel at structured relationships while vector databases handle semantic similarity, but the HybridRAG study shows combining both delivers measurably better accuracy on complex queries. Here is how to decide which approach fits your specific problem.
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LangChain vs LlamaIndex vs building it yourself
AI frameworks promise to simplify development, but they often add more complexity than they remove. LangChain has 90M+ monthly downloads yet introduces major overhead, LlamaIndex excels at data connection, while direct API implementation provides clarity and control. Here is when each approach actually makes sense for your team.
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Legacy modernization with AI - why augmentation beats replacement
A 2022 vFunction and Wakefield Research survey found 79% of modernization efforts fail to deliver expected outcomes. AI augmentation offers a safer path for mid-size companies to modernize legacy systems by building intelligent capabilities on top of existing systems instead of expensive rip-and-replace approaches.
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Multi-agent orchestration - the complexity trap
Multi-agent AI systems promise specialized intelligence but deliver exponential complexity. Salesforce research shows agents achieve only 58 percent success on single tasks and adding orchestration doubles the failure rate. Most mid-size companies need one capable agent, not coordinated swarms.
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Multimodal AI is about context, not features
Multimodal AI combining text, vision, and speech sounds powerful until you see the 10x token cost increase. With models like GPT-5.6 and Claude, real value comes from modalities that inform each other, not from stacking capabilities.
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Open source vs proprietary AI models - why free costs more
Open source AI models look free until you add infrastructure, staffing, and maintenance. With RAND Corporation noting in its August 2024 report that by some estimates over 80 percent of AI projects fail, most mid-size companies find proprietary solutions cost less overall.
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Retail AI: from customer service to inventory
Everyone builds chatbots while inventory sits overstocked and schedules waste labor. Backend retail AI operations, from SAP to Kroger, deliver measurable ROI that customer-facing features cannot match. Inventory forecasting cuts stockouts sharply, scheduling trims labor costs, and loss prevention stops billions in shrinkage. The wins hide in operations, not conversations.
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Start manufacturing AI with quality control, not predictive maintenance
Most manufacturers chase predictive maintenance for their first manufacturing AI project when quality control delivers results ten times faster. Companies like BMW use computer vision that catches defects humans miss and pays back in months, not years. Start with cameras on one production line, not facility-wide sensor networks.
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Support beats features every time - the real ai vendor evaluation checklist
Most AI vendor evaluation checklists obsess over model capabilities while ignoring what actually determines success: whether the vendor picks up the phone when your implementation breaks at 3am. RAND Corporation research shows more than 80% of AI projects fail, and 85% of companies miss AI cost forecasts by more than 10%.
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The AI tools graveyard: Why 90% fail and how to pick the survivors
Most AI tools will not exist in three years. The economics are brutal: Stanford HAI data shows private investment in generative AI has grown more than eightfold while startups burn through cash twice as fast as a decade ago. Here is how to spot which ones survive and avoid betting your operations on doomed solutions.
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The post-transformation reality nobody budgets for
After spending on digital transformation, most companies discover they have earned the right to transform again. S&P Global research shows only 5% of companies generate value from AI at scale. Here is what happens when consultants leave and why continuous evolution beats episodic overhauls.
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The real AI assistant problem no one talks about
Everyone is jumping between ChatGPT, Claude, Gemini, and Perplexity, but constant AI assistant switching costs up to 40 percent of productive time and is destroying the very productivity gains AI was supposed to deliver
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The true cost of AI - why human time is your biggest expense
Most AI budgets focus on software and infrastructure while ignoring the massive human time investment. RAND research confirms more than 80% of AI projects fail, and 85% of organizations misestimate project costs because they do not count employee hours, integration work, productivity losses, and opportunity costs. Here is a framework for calculating the true total cost of AI implementation.
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Using Claude Code for legacy modernization - 90 days does not finish it, but proves it is possible
Stop thinking 90 days will complete your COBOL to cloud migration. Utah took 18 months and AWS Transform cut Toyota timeline by 50%. Use that time to prove legacy modernization works, build organizational confidence, and create momentum for the multi-year migration ahead.
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Why most AI strategies are venture capital theater
Most AI strategies are elaborate 50-slide performances designed to impress investors and boards. Steve Blank calls this innovation theater. RAND reports more than 80% of AI projects fail because the boring operational work that creates actual value gets ignored.
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