FDE in Four Cities
From June 6 to July 4, 2026, HA7CH ran four closed-door FDE meetups in Shenzhen, Shanghai, Hangzhou, and Beijing, one Saturday afternoon each, 129 builders in total. This report is compiled from the live discussions at all four sessions. It is among the earliest firsthand testimony we know of on China's FDE ecosystem.
Two things up front. First, attendance figures come from our registration system (31 in Shenzhen, 31 in Shanghai, 32 in Hangzhou, 35 in Beijing), and every price, revenue, and timeline figure quoted in this report comes verbatim from what was shared in the room, with no extrapolation. Second, everything has been anonymized: all views are attributed uniformly to ha7ch guild members, with only industry and background descriptions retained. No real names, company names, or school names appear, with the exception of public industry facts about OpenAI and Anthropic.
1. Why Now: Real Business, No Industry Yet
FDE, Forward Deployed Engineer. A job title that until recently existed only on Silicon Valley careers pages was discussed, debated, and priced, intensively, across four afternoons in four Chinese cities. It still has no settled definition in China: there is no standard rate card, no established path into the work, not even a Chinese name everyone agrees on.
But it already has real contracts: a 20,000-yuan all-inclusive textile outsourcing job, a roughly 200-million-yuan domestic-stack IT (xinchuang) deal, an ERP subscription taking a 5 percent cut pegged to headcount reduction, and e-commerce gigs starting at 500 yuan apiece. What this report wants to record is precisely this moment: real business, no industry yet.
The backdrop is a set of mismatches. At the Shenzhen session, a guild member with a cloud-vendor background offered his read: since the new generation of models shipped late last year, coding agents' capabilities have exploded, but user-facing applications have not followed. Compute consumption is concentrated in a small number of users, product and engineering teams face layoffs precisely because of AI, and nearly every startup in Silicon Valley is crowding toward FDE. Capability in surplus, deployment in deficit, investment and returns badly mismatched. Standing in the gap of that mismatch is the FDE.
A member at the Hangzhou session who watches the industry from a big-tech vantage point pushed the judgment further: the last generation of software was a tools business, while this generation of AI business delivers "revenue growth" or "cost cuts and layoffs" as the product itself. Big tech has already started cutting staff as coding gets penetrated, and more than 90 percent of industries have not been penetrated at all. His exact words were logged on the spot as a quotable line: "These two years are the biggest blue ocean there is. You have to grab it."
What the four sessions produced is not secondhand trend commentary but firsthand testimony from the people doing the delivery, carrying payment receipts, failure postmortems, and layoff guilt. What follows is a city-by-city walk-through.
2. Shenzhen #001: On the Industrial Belt, Build First, Talk Money Later
June 6, a Saturday afternoon, 31 people.
Shenzhen was a classic industrial-belt scene, with the highest case density of the four cities, and every case came wrapped in words with body heat: on-site postings, commissions, headcount efficiency, layoffs. The opening introductions alone showed the spectrum: someone using large models to crack RGB-to-CMYK color conversion for the textile industry, someone doing marketing for a companion-robot company, someone doing RAG research, someone in cloud pre-sales, someone who had moved from sales into FDE. The ratio of traditional-industry people to AI practitioners was far more balanced than you would expect.
Start with the small cases. A member with an indie-developer streak hand-built a photo auto-classification tool in a few days, solving a friend's real pain point: a traditional retail sales rep who had to manually sort two to three thousand photos a month. The tool pairs a vision model with local algorithms, and the friend just plugs in his own API key. A pricing debate broke out on the spot: what should this thing cost, priced by the sales rep's hourly wage, or by some other anchor entirely? No conclusion, but everyone in the room realized that pricing is this industry's biggest blank space right now. Another member had done a wilder job: automating invoice issuance for an accounting firm, using an agent to drive the browser through the entire flow, and the project is live. A third member, formerly in financial analysis, used AI to write code that made his own job so efficient he simply quit, then built a website and food-delivery system for relatives running restaurants overseas, solving unstable delivery fees with fixed delivery zones and prepaid pricing, and built a Telegram bot for staff to self-select shifts with automatic payroll settlement. Not one of these jobs appears on any job board, but they are real FDE business.
Now the big cases. A member with deep roots in petrochemical IT shared three projects: using RAG to relieve in-house technical experts drowning in repeated Q&A, later extended to external pre-sales; a foreign-trade order-tracking system stalled at half-finished because email formats were too unstable; and AI-assisted decision-making layered onto an existing equipment management system, which landed smoothly. A member who spent years on core trading systems at a foreign bank drove the AI transformation of the trading stack, building a prediction system and a trade-flow agent. After launch it processed a heavy daily volume of trades, the bank subsequently cut staff, and he called himself "the guilty one," half in jest. A member from cross-border e-commerce turned product-selection logic and customs paperwork into formulas plugged into an ERP: shipping workflows for over a hundred SKUs a week used to consume enormous labor, automation cut labor needs by about 60 percent, some staff were laid off, some moved to operating the system, and the transition took about two months. A tech-company CTO put it most bluntly: after going AI-native, they can finish in three or four days what used to take more than half a year, their internal workflows are automated, and efficiency gains and layoff anxiety are two sides of the same coin.
One unresolved deal shows the boundary conditions of this market: a highly digitized manufacturing plant with in-house systems wants to bring in AI, an edge-deployment project with expected returns of 3 to over 5 million yuan. But the terms are harsh: no data goes to the cloud, deployment must be on-device, and the technology vendor must hand over the intellectual property. The original team passed; another member in the room said on the spot that he wanted it. Same clause, some see risk, others see a ticket in.
What made Shenzhen distinctive was industry, academia, and research at the same table, a rare sight. The associate dean of an engineering school at a university in South China launched a project-based FDE training program on the spot: recruit students across majors, bring in FDEs with real delivery experience to teach, form teams to work on real corporate projects, students get jobs, companies get hands, and he solicited instructors then and there. The supply side was just as lively: a fresh master's graduate from a Singapore university, short on internships, built three B2B POCs on his own and brought them to the room job-hunting; big-tech people who had quit cold because of the agent explosion were looking for direction; a member born after 2000 with a game-engine background had already built an AI avatar of his mother for foreign-trade lead generation. A member doing engineering-survey digitization inside his company voiced the common plight of the internal reformer: he has built plenty of tools, but efficiency gains inside the company carry limited economic value and little bargaining power, and he is wavering on going independent. The room's advice was one line: get out and make money.
Business models got their most direct and most unruly airing here: revenue shares pegged to layoffs or growth, technology for equity, AI roll-ups (raise capital to acquire traditional businesses, retrofit them with AI to lift margins, then keep rolling up more; startups in the US are already running this play), and splitting a project into training, coaching, and vision-selling as three separately billed phases. Pricing anxiety ran under the whole session, from "too embarrassed to charge" to "price by the client's anxiety level." The room also logged its expectations for the competition ahead: a price war may come, and the industry will move toward consolidation and standardization.
3. Shanghai #002: The Craft of Getting Paid
June 13, a Saturday afternoon, 31 people.
Shanghai was an intensely pragmatic business session, centered not on technology but on collections. The finance concentration was markedly higher than elsewhere: an asset-allocation product manager from a foreign bank, an undergraduate with nine finance internships, and students with brokerage backgrounds shared the room, putting constraints on the table you rarely hear elsewhere: heavy regulation, traceability and auditability, on-premise deployment, policy review for robo-advisory.
Start with the definition. A member with an organizer's background drew a line for FDE: an FDE is not an outsourcer who standardizes data, builds databases, or deploys chatbots, but a role that shortens a company's decision loop. His example was an airline: can we fly in this rain, how do we handle an emergency. Once AI is deployed, what shrinks is the decision chain, not document-processing time. Another member's project made the definition concrete: he is building an agent for a professional racing team, handling PR and logistics, making advance decisions on crew lodging and cargo transport. His summary stuck with the room: AI efficiency does not necessarily mean making the car faster; it means faster decisions and better-rested drivers.
The hardest-won material came from traditional-industry veterans. A member with over a decade in intelligent retrofits did a postmortem on smart-mine projects: hardware-inclusive projects carry heavy upfront work, errors and costs slip out of control easily, acceptance criteria are vague, and the client's leadership can seize on any problem as a reason to delay payment. His most extreme sample: a mining project in western China dragged on for years without completion, over client-side execution and budget problems, and the hardware warranty expired in the meantime. His countermeasure is not technical but relational: build communication with every level of the client organization, and screen out unsuitable industries and business types from the start. An operator at a state-owned IT company used a domestic-stack IT (xinchuang) deal worth hundreds of millions of yuan as his example and delivered a more structural judgment: FDE does not fit state-owned institutions. Pitching cost savings and efficiency to them goes nowhere; settlement requires layer upon layer of approval, and the logic never closes. State-owned enterprises (SOEs) answer upward, not downward, so the acceptance criterion is whether the people above are satisfied. If you must do it, get alignment with the very top first and push top-down.
A member selling integrated solutions shared two ledgers, one about profit, one about cost. The profit ledger: an integrated solution bundling high-end life-support equipment with a prediction system, negotiated at 1 to 1.5 million yuan per case, with clear delivery boundaries and fat margins. But at 60 to 70 percent completion, the project died quietly, tangled in the cadence of official red-header documents, school resources, and annual budgets. The cost ledger: he converted a data-annotation line to agents, and error handling genuinely got faster, but model consumption ran 30,000 dollars that month, more than the original small-scale cloud bill, and every workflow still needed human review at the end. Uncontrollable cost directly killed his ability to quote future deployments.
Organizational problems were put squarely on the table here. A member automating the full sales pipeline said his system targets industrial firms' bidding and tender workflows and achieves essentially zero employee input, but the moment employees learned the agent would replace their work, friction appeared instantly. The room's solution was surprisingly mature: partner with firms specializing in labor arbitration and workforce transition, and settle compensation, redeployment, and non-compete questions with the CEO and HRBP during the organizational diagnosis phase; transfer whoever can move to other business units, retrain whoever can be retrained. Another member shared a lesson from logistics: from efficiency pitch to delivery, blocked at every turn, and the final landing was to extract the features as plugins grafted onto the client's existing ERP. Anything that touches core systems, like finance-business integration, do not touch if you can avoid it.
On playbooks, an AI tooling company that got into FDE work early laid out its path: enter the enterprise with generic needs first, help the client bank quick wins and build trust, then capture high-margin work through deep needs. A member with a consulting background doing enterprise diagnosis added a feature of China's toB ecosystem: the product is sold to the decision-maker, not the user. Data security came up again and again: one team doing AI work for a leading consumer brand runs all client-document redaction on local models; another member mentioned a regional SOE's AI transformation project with budget in hand but little grasp of agent technology, so market education happens one training session at a time.
Two details explain why non-technical factors matter. One member dissected why Microsoft Copilot sells well despite not being great: SLA breaches come with compensation, and the brand itself is a procurement rationale. You cannot build looking only at the technology; companies of different sizes are buying service and certainty. Another member cited a choice from the aerospace world: for the reliability and stability of long-cycle projects, they would rather stick with old software versions and forgo the latest technology. Put the two together and you have the real procurement logic of toB.
Supply and demand closed the loop inside the room itself: an energy-storage materials company came looking for an algorithm partner, wanting to start at the mine with ore-sorting and process optimization; a doctoral student from a traditional engineering background, who had only touched vibe coding in April or May, was picked up on the spot by a team hiring interns. What lingered after the session was a genuine appetite for organization: one member proposed founding an FDE industry association to unify understanding and set standards.
4. Hangzhou #003: A Grassroots Blue Ocean, AI Landing in the Capillaries
June 27, a Saturday afternoon, 32 people.
Hangzhou was the most grassroots, most e-commerce-flavored session of the four, with the widest price spectrum. At one end, fast small jobs at 500 to 5,000 yuan apiece: a member who taught himself coding out of cross-border e-commerce operations now works as an FDE at an e-commerce company. His business lines are content replication (cloning viral product videos), RPA automation (simulating manual web operations), and data dashboards (as he put it, the thing scrappy small-town bosses welcome most). He prices by the client's budget, installation and training included, gets clients through short-video platforms and job boards, and screens them with almost disarming bluntness: use dressed-up open-source projects and demos to filter out the non-payers first. At the other end, a member with real nerve landed a CRM-plus-B2B-inquiry customer-profiling project through connections just two weeks after arriving in Hangzhou: 50,000 yuan for phase one, roughly 100,000 in total, a team of two or three.
The cases in between were all over the map. A fresh graduate joined an enterprise-services firm as an FDE and was dispatched shortly after joining to a temple posting, teaching the monks to use AI and configuring systems. His field report: the masters are easygoing and highly receptive, but everything is hand-holding. A solutions engineer covering general industry did a postmortem on why a steel-mill project would not move: not because the technology fell short, but because the client had no confidence in its own data quality and the veteran operators feared being replaced by AI. The project died before the technology got a chance. An engineer doing high-performance computing at a major tech company had used agents to automate the long pipeline of algorithm deployment, but inside the big company he cannot use the latest models, budgets are tight, and there is little room to adjust internal processes. His self-assessment: close to the technology, far from the market, here to hear stories of FDEs dealing with clients on the front line.
Career changers set the tone of this session. A financial accountant at a chain business, keeping books for hundreds of stores, first squeezed efficiency out of software, then used an AI coding assistant to automate 60 percent of his work, then built small websites and browser extensions. His colleagues' fear of AI is exactly what showed him the FDE direction; his worry is his non-technical background. A Java developer with five years' experience wants to move into FDE, gets leads by posting in content communities, and has already built a dashboard for a friend's factory; a retail stock trader also came to him asking for quantitative trading code. A member with over five years of AI algorithm deployment in chip manufacturing wants to run a one-person company and admitted he understands neither client acquisition nor business. A member on a large-model team at an SOE software unit, working on efficiency for new-energy-vehicle software development, is stuck on requirements comprehension and test quality evaluation, and walked away with a usable testing idea from the room: traffic replay plus verification. Everyone was asking the same question: I have this half of the skill set, how do I get the other half?
The supply-demand mismatch was most visible in this session. A founder whose main business is cloud computing said his channels have accumulated a large client base, the AI deployment demand is real and well-budgeted, but his team is strong on business development and short on delivery confidence. A member selling inside the ecosystem of a leading foreign-trade B2B platform was the exact inverse: the platform is pushing AI products hard this year, he has started selling, his clients have a rough grasp of AI but no deployment guidance, and he has client resources but no technology, weighing whether to close the technical gap himself or find a technical co-founder. One side has work and no hands; the other has hands and no work. What separates them is the trust and pricing machinery this industry has not yet grown.
Hangzhou contributed several judgments that reached room-wide consensus. First, engineering is no longer the moat: agents will readily help you, and engineers on generic stacks are easy to find, but people who understand a specific business domain are hard to find. The FDE's core moat is domain business knowledge. Second, business data matters more than business logic: most corporate data cannot be fed to agents as-is, and data infrastructure is itself the opportunity. Third, clients pay for exactly two things: ROI they can compute, or making the boss happy. SOE deals that come with detailed PRDs are easy to execute and easy money, but they barely differ from traditional outsourcing and command no premium. Fourth, AI transformation must be driven by the boss in the top seat; bottom-up pushes rarely move, and before signing, check your counterpart's authority. The acquisition and screening playbook was equally blunt: for clients with no budget and no understanding, charging a consulting fee first is the best filter.
One overseas reference kept being cited: OpenAI and Anthropic have each formed joint ventures with PE firms to lift portfolio-company margins, in essence using capital relationships to remove transaction friction and accelerate AI penetration. Silicon Valley startups have the last generation's toB muscle and VC backing, which makes FDE comparatively easy; Chinese VCs care more about grand trends, the FDE ramp is long, and the toB narrative is systematically undervalued in China. On billing, a member who serves as COO of a Shenzhen hardware company while running an FDE practice offered his three-part kit: train management and staff separately, start billing the moment the diagnostic phase begins on-site, then switch to project-based fees. The team formula also converged in this session: a domain expert plus an engineer with project experience.
5. Beijing #004: Systems, Organizations, and Client Psychology
July 4, a Saturday afternoon, roughly 30 people.
Beijing had the highest resource density and the widest spectrum. The first speaker of the day was a data annotator: he automated his job of copy-pasting evaluation sets from spreadsheets into chat windows with a Python script, packaged it as an EXE, spotted the FDE direction and decided to pivot. He dressed up his resume, sent it out, and found real market demand, but every time someone asks "how do you charge," he has no answer. At the other end of the same afternoon: the founder of an AI brand-marketing company with tens of millions of yuan in annual revenue, there to recruit a technical co-founder; a medical information-services company with deep roots in hospital invoicing, looking to go deeper into hospital economic-operations management platforms; and a veteran with nearly 20 years in hotel franchising, carrying a product concept billed annually at 10,000 to 40,000 yuan per property, looking for a development team, with a goal stated at full volume: ride this to an IPO together.
Two young people's stories deserve their own note. One fresh graduate rebuilt a content-platform business with AI from scratch at an agency-side marketing firm, one person going head-to-head against what used to be a 30-person team. Batch generation of posts and data dashboards is already iterating, some of the numbers hold up, and the next step is taking on FDE contracts. A graduate student built an AI agent that helps foreign tourists hail rides, find restaurants, and plan itineraries, and is exploring overseas client acquisition. What they share is that neither waited for anyone's permission: build it first, then come to the room looking for an amplifier.
The discussion in this session leaned distinctly toward systems and organizations. A government-and-enterprise digitization vendor is building an FDE curriculum and co-developing standards and certification; they use telecom-carrier client relationships as a wedge to serve heavy-industry and energy companies in the north, and their stated ask was matching strong FDEs to large B-side orders. One member wants to build an FDE talent platform: sourcing, training, matching to enterprises, with personal-brand incubation on the side. A carmaker is pushing AI-native reform through org-structure changes, building a structured AI coding knowledge base. A large consumer-goods company is rolling out AI across every department, pulling staff from business lines while hiring outside. A business-analysis role at a big tech firm offered a quantified internal sample: over 50 percent efficiency gains across business-analysis workflows, though external tools were ultimately dropped over problems with the tools themselves. The shift in hiring criteria was named outright by a partner at a Series A AI hardware company: from engineer-leaning to product-manager-leaning, favoring young people with a hybrid background of big tech plus startup plus SE service experience. Another member in the room added a field observation: students from top universities really do learn faster and adapt across more surfaces. His scaling sample was an FDE company in a vertical hardware niche with roughly 20 million yuan in annual revenue, growing its client base through investor referrals; another acquisition reference was a content creator serving private equity and investment banking, where same-industry case studies carry far more trust than cross-industry ones.
The dissection of client psychology was the sharpest of the four cities. A member running multiple AI products (including an AI academic-writing product with steady revenue in the 100,000-yuan range) called it: some companies hire an FDE not for ROI but because the boss wants a person sitting there for peace of mind. So step one is figuring out whether the boss wants ROI or reassurance. A member who moved from vertical agents to B-side work testified with his own detour: a contract-review project stalled, and only when he pivoted to financial and business data analysis did things click. His summary: the boss cares about exactly three things, results, cost, and whether it can be replicated across other lines of business, and cares not at all how you built it. A member with a legal background added the other side: clients' expectations about where AI's capabilities end are generally fuzzy, so boundary management is itself part of the delivery.
Methodological convergence was clearest in this session. Step one of enterprise AI adoption is datafication: small companies start with a database or knowledge base; only at large companies do business-process and org redesign come into play. The knowledge base is the main event: heterogeneous data and expert knowledge must be consolidated into a form agents can call, and what bosses care about most is whether the knowledge base can evolve with the business. That currently has no general solution, and traditional RAG accuracy is degrading. One reusable sample was a knowledge base built for a professional racing team: tires, cars, and logistics all structured, wired into a collaboration platform for agentic search. The room's verdict: a knowledge base plus data skills can solve 80 percent of FDE project problems. Acquisition advice circled back to basics: run your first deal through channels where trust already exists, accumulate data and case studies, and spin up the flywheel. Picking the industry matters more than picking the client: sectors like home services, low-margin and thin on talent, were named as having limited ability to pay. And AI startups should head for incremental markets like overseas expansion and foreign trade, steering clear of the tangled interests that cost-cutting plays stir up in saturated markets.
6. Cross-City Comparison: Pricing, Acquisition, Pain Points, Talent
Start by laying the four cities' price points side by side. This may be the densest public sample of Chinese FDE pricing to date:
Shenzhen. Manufacturing plant edge-AI project: expected returns of 3 to over 5 million yuan, with harsh terms including no cloud for data and surrender of IP.
Shenzhen. Cross-border e-commerce supply-chain automation: roughly 60 percent labor reduction, a transition of about two months, over a hundred SKUs a week.
Shenzhen. AI content for lead generation: 50,000 views in 4 days, converting directly into business inquiries.
Shanghai. Integrated high-end life-support equipment plus prediction system: 1 to 1.5 million yuan per case.
Shanghai. Domestic-stack IT (xinchuang) deal: contract around 200 million yuan.
Shanghai. Data annotation converted to agents: 30,000 dollars of model consumption in a single month, above the original cloud cost.
Shanghai. Long-term advisory retainers: on the order of a million yuan per year depending on company size, contracts typically signed for three years.
Hangzhou. Small e-commerce FDE jobs: 500 to 5,000 yuan apiece, installation and training included.
Hangzhou. CRM plus customer-profiling project: 50,000 yuan for phase one, roughly 100,000 in total, a team of two to three.
Beijing. Textile inkjet-positioning outsourcing: 20,000 yuan all-in, replicable and resellable to other textile mills.
Beijing. Yiwu manufacturing AI plus ERP: a subscription taking a 5 percent cut pegged to headcount reduction, 50,000 yuan a year.
Beijing. Hotel AI product concept: annual billing, 10,000 to 40,000 yuan per property.
Beijing. FDE company in a vertical hardware niche: roughly 20 million yuan in annual revenue.
Beijing. AI brand-marketing company: tens of millions of yuan in annual revenue.
Beijing. Consumer AI academic-writing product: steady revenue in the 100,000-yuan range.
On models, each city leaned differently, but together they form a complete map: project-based (person-days, feature points, all-inclusive), subscription (cost-savings cuts, annual billing), consulting-first (charge a consulting fee to screen clients, bill from the moment on-site diagnosis begins), lock-in (technology for equity, the overseas reference of PE joint ventures), and reuse (templatize solutions for resale, then charge for tokens once the work is banked). Two pricing anchors won recognition across cities: the digital employee's annual salary (proposed in Shanghai: price AI services at the salary of the role they replace, which bosses accept more readily), and back-calculating from cost savings (Shenzhen's layoff-ratio revenue share, Beijing's 5 percent subscription cut). The problems Shenzhen exposed (too embarrassed to charge, no standard for quotes) had structured answers by the time of Beijing, such as three-phase billing: corporate training, paid on-site diagnosis, then development, delivery, and maintenance. The first two phases can be bought separately, and for long-term maintenance the advice is to teach the client's internal IT to take over. But the question of how to convert workflow improvement into money went unanswered in all four cities.
On acquisition, the four cities' playbooks overlap heavily: content-based acquisition (short video, content communities; Shenzhen had the sample of 50,000 views in 4 days converting straight into inquiries), first deals through trusted personal networks (Beijing framed it as the starting point of the flywheel), channel reuse by industry veterans (20 years of hotel contacts, industrial-belt resources in Foshan and Yiwu), on-site entry followed by lateral expansion, and referrals backed by same-industry case studies. The difference is in client segments: Shanghai stressed SOE relationship management and the top-down play, Beijing added the route of entering government-and-enterprise accounts through carrier relationships, and Hangzhou was bluntest: charge a consulting fee first and keep the budgetless out.
The pain points sort into four layers, and every city named all four, just in different words. The demand layer: clients cannot articulate their pain, offering only broad notions like AI marketing or AI lead generation, with no acceptance criteria, so vendors dare not promise outcomes. The data layer: corporate data cannot be handed to agents as-is, sensitive data is hard to connect, and clients do not even trust their own data quality. The organizational layer: employee resistance, veteran obstruction, department walls; efficiency gains directly trigger layoff anxiety, and management and staff need two separate narratives. The technical-commercial layer: however high single-task success rates get, chaining tasks collapses the success rate of the full business scenario; model costs are uncontrollable; payment cycles slip; internal reformers have no bargaining power; and AI consulting resists scale.
The talent and transition signals escalated city by city. Shenzhen produced university project-based FDE training and fresh graduates job-hunting with self-built POCs. Shanghai is testing an "expert plus FDE plus intern" placement model across industry and academia, and a doctoral student who patched up his algorithm skills with vibe coding got snapped up in the room. Hangzhou proved the non-technical route works: a financial accountant who automated 60 percent of his job, e-commerce operators, and Java developers each found their own conversion. Beijing supplied the demand-side shift in standards: hiring is moving from engineer-leaning to product-manager-leaning, senior FDEs can go straight to co-founder seats, and curriculum and certification co-building is underway. The consensus running through all four cities: initiative first, domain knowledge is the scarce input, and pure engineering skill is being flattened by agents.
7. Trend Calls: The Next 6 to 12 Months
Call one: pricing will converge on anchors and then head into the eve of a price war. Shenzhen named the pricing problems born of being too embarrassed to charge, and the notes logged expectations of a price war and consolidation; Shanghai contributed the digital-employee-salary anchor and million-yuan-a-year retainers; Beijing produced the replicable 5-percent-of-layoffs subscription formula. Anchors will spread faster than practitioners expect, and the low end (the 500-to-5,000-yuan jobs) will be the first to turn cutthroat.
Call two: data infrastructure will displace agent development as the FDE's main battlefield. Hangzhou judged business data more important than business logic; Beijing concluded that a knowledge base plus data skills can solve 80 percent of project problems and that datafication is step one of enterprise AI adoption; Shenzhen argued that proprietary data is the entry point. Three cities converging independently on the same conclusion means the first line item on an FDE quote over the next six months will most likely be data governance, not agents.
Call three: client segmentation will become the line between life and death. Shanghai delivered the structural verdict that the cost-savings pitch goes nowhere in state-owned institutions; Hangzhou noted that SOE deals with detailed PRDs are easy money but barely differ from traditional outsourcing and command no premium; Beijing named low-margin sectors like home services as unable to afford FDEs and argued for incremental overseas markets. Teams that pick the wrong segment will be bled dry at collections (the mining project that dragged on for years without completion is the cautionary tale), and industry-screening ability will separate winners from losers before delivery ability does.
Call four: the efficiency-layoff paradox will move from a moral topic to a step in the delivery process. Shenzhen's bank engineer jokingly called himself the guilty one; Hangzhou's steel-mill project died on veteran resistance; Shanghai has already produced the mature practice of partnering with labor-arbitration and workforce-transition firms and settling compensation and redeployment with the CEO and HRBP during organizational diagnosis. Workforce-transition plans will enter the FDE's standard list of deliverables.
Call five: FDE talent supply will systematize within 6 to 12 months. Shenzhen's university project-based training, Shanghai's industry-academia placements, and Beijing's curriculum-and-certification co-building and talent-platform ventures are three stages of the same thing. Add the talent spillover from big-tech layoffs in coding roles and the demand-side shift from engineers to product managers, and the first cohort of formally trained FDEs will hit the market in the first half of next year.
Call six: the fight over paths to scale will decide whether capital shows up. Beijing's VCs admitted most are still in the getting-acquainted phase with no clear process; the worry that FDE looks like outsourcing and resists scale was relayed by founders in the room as the industry's standing question about FDE. Three answers have already surfaced: bank solutions for reuse and then charge for tokens, technology for equity, and the reference model of overseas model companies forming joint ventures with PE. Whichever produces a replicable sample first will decide whether China's undervalued toB narrative can flip within a year.
8. Closing
Four cities, 129 builders, four afternoons, assembling the earliest terrain map of China's FDE ecosystem: Shenzhen's industrial-belt street fight, Shanghai's collections sobriety, Hangzhou's grassroots blue ocean, Beijing's systemic ambition. The industry has no consensus even on its name, and meeting transcription tools cannot spell out the acronym, but it already has a real contract spectrum running from 500 yuan to 200 million.
ha7ch guild's next move is to turn this terrain map into infrastructure: keep running periodic closed-door sessions city by city, so supply and demand keep closing the loop in the same room; make hackathons and sprints the channel through which young people enter real enterprise sites; and bank the pricing anchors, client-screening methods, and delivery lessons that recurred across the four cities, so the next person converting to FDE does not have to start over from "too embarrassed to charge."
"These two years are the biggest blue ocean there is. You have to grab it." That line came from the Hangzhou session. We leave it here exactly as spoken, and we will come back in a year to check the answer.