Chasm Security: Facing the Technology Startup's Dilemmas (B)
Bibliographic record
Abstract
This series of case studies describes the dilemmas encountered by Shenzhen Chasm Security Co., Ltd. (referred to as "Chasm Security") in its three rounds of funding since its establishment in 2012. As a company with Internet security technology as its core asset, it has five co-founders from three regions, and four of them have a strong technological background. Given the political and economic context of the China–U.S. trade war and the sensitivity of the information security industry, this entrepreneurship case series always generates vigorous and enthusiastic discussions. Case (A) focuses on the first two rounds of funding in mid-2016 and discusses various dilemmas faced by the founding team. First, should the equity split among the co-founders be based on instinct or logical rationale? Second, should the company's shareholding structure be more concentrated or distributed? Third, should President Zhi Wang exercise his veto power? Crucially, the judgment on the last question encompasses three other dilemmas: (1) whether to insist on his rationale on market positioning, or swallow his opposition for the sake of maintaining relationships; (2) whether to gain a firm foothold in the Chinese market, or pursue a broad global presence; and (3) whether to chase a high valuation and wealth, or retain company control. Case (B) is based on the China–U.S. trade war and discusses how this Chinese Internet security company with American capital raises Series C funding in the second quarter of 2018. Should the company seek dollar funding to grow in the global market, or opt for RMB funding to focus on the Chinese market? Of utmost importance is how to deal with a situation where U.S. investors are pessimistic about Chinese security companies, and RMB funds are unwilling to invest in businesses with a dollar funding structure. Case (C) introduces the latest progress of Chasm Security as of the end of 2018. It then poses a very inspiring question to comprehensively summarize the case series: "If the founders had a second chance, how would the game play out?"
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".