Inferring　the　developmental　potential　of　single　cells　from　scRNA-Seq　data　and　reconstructing　the　pseudo-temporal　path　of　cell　development　are　fundamental　but　challenging　tasks　in　single-cell　analysis.　Although　single-cell　transcriptional　diversity　(SCTD)　measured　by　the　number　of　expressed　genes　per　cell　has　been　widely　used　as　a　hallmark　of　developmental　potential,　it　may　lead　to　incorrect　estimation　of　differentiation　states　in　some　cases　where　gene　expression　does　not　decrease　monotonously　during　the　development　process.　In　this　study,　we　propose　a　novel　metric　called　single-cell　transcriptional　complexity　(SCTC),　which　draws　on　insights　from　the　economic　complexity　theory　and　takes　into　account　the　sophisticated　structure　information　of　scRNA-Seq　count　matrix.　We　show　that　SCTC　characterizes　developmental　potential　more　accurately　than　SCTD,　especially　in　the　early　stages　of　development　where　cells　typically　have　lower　diversity　but　higher　complexity　than　those　in　the　later　stages.　Based　on　the　SCTC,　we　provide　an　unsupervised　method　for　accurate,　robust,　and　transferable　inference　of　single-cell　pseudotime.　Our　findings　suggest　that　the　complexity　emerging　from　the　interplay　between　cells　and　genes　determines　the　developmental　potential,　providing　new　insights　into　the　understanding　of　biological　development　from　the　perspective　of　complexity　theory.　Graphical　Abstract