Every layer of review makes you 10x slower

Viral Effects Are Not Network Effects

Virality and network effects are conflated by even experienced Founders, and it keeps them from developing the right strategies and playbooks.

https://n0.computer

Reducing net work for networks

Bridges & The Last Network Effect - augment

Modelling the viability of opportunity networks

How big should a peer-to-peer network be before it generates a steady stream of deals?

Estimating peer effects in noisy, low-rank networks via network smoothing

Peer effect estimation requires precise network measurement, yet most empirical networks are noisy, rendering standard estimators inconsistent. To address measurement error in networks, we propose a method to estimate peer effects in networks whose expected adjacency matrix is low-rank. Our key result shows that peer effects over a true unobserved network are asymptotically equivalent to peer effects over the expected adjacency matrix. This result reduces peer effect estimation in noisy networks to low-rank matrix estimation targeting the expected adjacency matrix. We develop our theory for weighted networks observed with additive noise, but simulations suggest approach can be applied more generally when there is a low-rank estimation method suited to a particular noise structure. We demonstrate via simulations that our approach applies to egocentric samples, aggregated relational data, and networks with missing edges, each requiring a different low-rank estimation method.

(PDF) Algorithmically Mediating Communication to Enhance Collective Decision-Making in Online Social Networks

PDF | Many collective decision-making contexts involve communication among group members. Sometimes this communication helps the collective reach an... | Find, read and cite all the research you need on ResearchGate

Bridges & The Last Network Effect · augment

A blog by Anuj Ahooja

More Versus Better: Artificial Intelligence, Incentives, and the Emerging Crisis in Peer Review | Organization Science

As the AI Task Force for Organization Science, we provide an early account of artificial intelligence’s (AI) impact on both submissions and reviews at a major academic journal. Submission volume ha...

Neural scaling law

In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, and training cost. Some models also exhibit performance gains by scaling inference through increased test-time compute (TTC), extending neural scaling laws beyond training to the deployment phase.

Slow down to speed up: so much has changed in 6 months’ time

An overview of what’s changed in engineering during the last six months, how various tech companies are changing how they work, and why slowing down could be a sensible strategy

Publish and Perish: How AI-Accelerated Writing Without Proportional Verification Investment Degrades Scientific Knowledge

Artificial intelligence tools are accelerating manuscript production far faster than peer review capacity can expand. Applying the theory of constraints from manufacturing science, we formalize this asymmetry through a minimal two-variable ordinary differential equation model coupling review queue evolution and verification quality degradation via an endogenous, queue-pressure-driven review AI adoption mechanism. The causal chain is: writing AI adoption increases submissions, growing the review queue, which drives reviewer AI adoption under pressure, degrading verification quality and reducing net knowledge output. Under empirically informed parameters (writing acceleration γ = 2.0, review acceleration δ = 0.5), the model predicts a deceptive honeymoon where knowledge output peaks at 1.10K0 (circa 2026), followed by paradox onset at t = 6 years (2028) and long-term degradation to 0.68K0 (32% loss), approaching a steady state of 0.60K0 (40% loss). The critical condition for net benefit is δ > γ; the current operating point lies deep in the paradox regime. Empirical validation against NeurIPS, ICLR, arXiv, and bioRxiv submission data shows qualitative consistency with observed post-ChatGPT acceleration patterns. Policy analysis reveals that only combined interventions such as review infrastructure investment paired with institutional quality standards can restore positive knowledge production.

How three lines of configuration solved our gRPC scaling issues in Kubernetes

It all started with a question I asked our senior software engineer: “Forget the speed of communication. Is it really better for you to…