Titles are drawn verbatim from what clients ask us. Each piece answers exactly one question, with established facts and clear boundaries — nothing unverifiable like "we're true professionals." Articles are signed by Chen Ye, credentials included; quoting and reposting are welcome (with attribution).
Full articles are currently published in Chinese. English summaries below; the originals open in Chinese.
Gartner surveyed 2,002 CIOs: only 13% have realized significant value from AI. A Alibaba Cloud CIO, after months of dialogue with 40 leading enterprises, described enterprise AI as a quagmire. Four institutional sources published within the same week all point to the same conclusion — and validate five judgments we have been making for a year: settle the accounts first, people and organization decide the outcome, AI must learn your company's context, stitch don't rebuild, and execution is the scarce asset.
FDEs solve requirement translation, but the capability to deliver always stays with the vendor. Rifeng's first AI backbone training camp points to a second answer: 50 business backbones, 7 real problems, 72 confirmed scheduling rules — scenario-group co-creation builds the landing capability inside your own organization. Plus a four-question self-check before you start.
A supply-demand ratio of 0.28, job postings up roughly 729% in a year, an average salary of RMB 408k: the FDE boom is real. We unpack the skill structure inside public job descriptions, offer enterprises a decision rubric across three paths — hire, train, or buy outcomes — and give individuals four calibration questions before switching careers.
IDC predicts 40% of Asian organizations will miss their AI goals by 2026, and CIOs who skip data-debt repairs face a further 50% rise in AI failure rates. Four numbers decoded into two hurdles — tool fragmentation and data debt — plus three moves you can start within a week.
A dedicated State Council document → a CPC Central Committee document → a reaffirmation from both General Offices 12 days later: "AI Plus" has been reworded from "deepen implementation" to "full implementation," and "encouraging qualified enterprises" is gone. The three documents read together: the state is setting the tone, building ladders, and laying down rules.
What an AI coding tool really gains is the operating rights to a computer: auto-archiving, second-level retrieval, memory handovers across days, scheduled publishing in the small hours. A firsthand record of the author's own workstation, run for over a month, with three judgments for enterprise adoption.
A seven-step playbook "distilled from 100 FDE projects" is making the rounds. The list isn't wrong — the mistake is mistaking conclusions for a path. The real threshold is learning to read the enterprise as a machine: budgets, performance reviews, approval flows, and the history behind the processes. Plus three slow-and-steady suggestions for entering the field.
After Meta Muse and its open-source clones flooded every feed, the author installed them — then deleted them. Three everyday scenarios — a daughter's exam mistakes, a dental medical record, a boss's go/no-go call — make the point: a general assistant gets you the admission letter; one that knows your trade gets you the diploma.
Capital has lit a fire under China's FDE market: annual salaries pushing RMB 1 million to poach talent, Haizhi Technology up 250% on its first trading day, a supply-demand ratio of 0.28 — 3.5 openings chasing one person. Buyers should stop planning to build in-house and ask how to buy instead: three questions to tell genuine forward deployment from high-end outsourcing.
AWS is spending $1 billion to build a forward-deployed engineering organization, OpenAI is pouring in $4 billion, Anthropic has opened an "AI medical school": the decisive battleground has moved to "getting on-site." Silicon Valley has yet to deliver a third-party-verified return, while in Chinese factories Midea already has an audited answer sheet. Plus three must-ask questions when choosing an AI vendor.
Most AI projects that can't be accounted for have their roots before go-live: metric definitions, statistical scope, and comparison periods never nailed down. A baseline-freeze form in three parts, countersigned by both sides — no baseline, no kickoff. Plus a one-sentence account for converting improvements into money. Two tables (part 2).
The first table for AI adoption: a role-by-role task inventory. Five questions that collect facts, not wishes; ranked by the cost of errors rather than frequency; worked out to order of magnitude and no further. Two tables (part 1).
A breakdown of the AI quality-inspection results Lao Gan Ma has made public: RMB 5.4 billion in revenue, a 0.02% defect rate, 100% traceability — and the sequence behind them. AI is an amplifier, not an error-corrector. Plus what small and mid-sized manufacturers can learn, what they needn't, and three self-check questions.
A well-known architect forbids his AI from writing code directly — unsatisfactory generations get thrown away in whole batches. Seven judgments unpack "the easier generation gets, the more verification is worth," plus a real lesson from our own automation pipeline and three adoption suggestions.
Starting from two a16z articles six years apart: do a task twice, and write it down as a process; write a process down twice, and turn it into an agent. Plus the three gates a digital employee must pass before starting work.
The China Academy of Industrial Internet and JD Industrial jointly released a study on quantifying AI value: the evaluation lens shifts from "how much AI you used" to "how much money you earned back." The report's published methodology, plus the hands-on practices we add from our own deliveries along a four-step measurement path.
"Should we do this, which one first, how do we know it worked" broken down into checkable judgment items — each with a fallback for when the condition isn't met.
How application requirements differ between the national and provincial levels, the common reasons applications get returned, and the few details that genuinely sway the review experts.
Common point-losers broken down by how the score is weighted, a priority order for improvement paths, and which changes don't pay off within a year.
Nine out of ten disappointing IT projects fall into three pitfalls: never thought through, never absorbed, never nurtured. Plus a 7-question self-check before the system goes in, and the new variable of the AI-writes-code era.
