Topic: AI opportunity broadening and sales capacity ramp
Key points:
AI opportunity has multiple layers: current layer is inference stacks in cloud (normal compute, CPUs, GPUs, databases, web servers); next layer is specialized GPU monitoring (new product announced at DASH); top layer is observability for nondeterministic AI applications (code written by AI agents, evaluated in production).
AI natives are already adopting; broader market is moving there, evidenced by broad adoption of API-gated AI models and coding agents in large enterprises.
Sales capacity: started increasing salespeople in last part of 2025; seeing evidence through new logo production and pipeline; signs that core capacity is becoming productive.
Mgmt stance: Bullish — sees AI as multi-layered growth driver with expanding market; sales ramp showing positive early signs.
Q2 — Sanjit Kumar Singh
Topic: Guidance strength vs. AI native cohort volatility; security business growth
Key points:
Guidance is one of the most impressive coming out of Q2 in a couple of years; AI cohort continues to grow rapidly with good market share wins.
Guidance incorporates conservative assumptions on AI native performance due to potential volatility in usage or unit rates (learned from previous cloud native cycle).
Security crossed $100 million threshold; three products, some reaching inflection point; successful at broad adoption with a few customers spending $1M+; next focus is standardized wall-to-wall adoption in large enterprises via go-to-market customizations.
Mgmt stance: Neutral on AI volatility — conservative guidance reflects learned risk; bullish on security — happy with proof points, but more work needed for enterprise-wide adoption.
Q3 — Matthew Vincent Martino
Topic: Enterprise vs. SMB consumption trends
Key points:
Usage trends across segments were roughly consistent with previous quarters.
Enterprise: saw some concentrated less consumption relative to a spike, but that stabilized.
SMB: small but gradual improvement in usage of products.
Mgmt stance: Neutral — no major change; enterprise stabilized, SMB gradually improving.
Q4 — Mark Ronald Murphy
Topic: AI research (Toto/BOOM) and R&D investment; operating income guidance
Key points:
AI automation opportunity broken into three categories: SRE (alert investigation/remediation), coding (fixing production issues), security (investigating signals). Released Openwave research models; next step is incorporating into product.
Toto model beats every other model in time series forecasting category; shows high-level performance.
R&D spending up noticeably in Q2 due to aggressive investment plan and successful recruiting (including Paris R&D center); includes more spending on AI training/inference (e.g., Toto training, agent simulations).
Q2 operating income increased 36% due to factors like timing of DASH ($13M), FX; good line of sight on R&D drivers and seasonality in OpEx.
Cloud usage optimization (dog-fooding) applied to gross margin and OpEx growth rates; successful in Q2 run rate, expected to continue.
Mgmt stance: Bullish on AI research — state-of-the-art results; neutral on R&D envelope — expects same investment level moving forward; positive on margin impact from cloud optimization.
Q5 — Koji Ikeda
Topic: Contract visibility with large AI native customers; security M&A strategy
Key points:
Cannot speak about specific customers; any individual customer can change behavior.
Strong product engagement from top customers; high retention product; many customers who churned to build themselves came back.
Short-term drops and long-term growth typical during renegotiations with increasing volume.
Security: $100M milestone, growing 40%; covers many product areas; needs both boring table-stakes features and AI-driven future investment.
Expect more M&A in security as in rest of business; many assets and opportunities to grow.
Mgmt stance: Neutral on contract visibility — confident in forecasting and long-term retention, but acknowledges short-term volatility; bullish on security M&A — open to acquisitions to expand capabilities.
Q&A Batch (6-10 of 11)
Q6 — Karl Emil Keirstead
Topic: AI-native 收入占比与利润率关系
Key points:
AI native 客户贡献 11% 收入；定价基于使用量（volume）和期限（term），与客户规模相关，而非 AI 属性