Alphabet tops $100B quarterly revenue for first time, cloud grows 34%

Original Article ↗ Hacker News Discussion ↗

GCP usability, deprecations, and the “treadmill”

  • Many users say GCP works well for core needs (VMs, storage, serverless), but repeated deprecations create large amounts of low‑value “busy work.”
  • Some frame this as a deliberate “fire and motion”–style tactic (constant change to slow competitors); others counter that internal platforms at big companies behave similarly due to evolving requirements and promotion‑driven development, not strategy.
  • AI is called out as especially bad: APIs and dependencies change so fast that work feels obsolete within months.

Console, tooling, and performance

  • Multiple commenters complain the GCP console is painfully slow; some prefer a GUI but feel pushed to CLI or Terraform.
  • The gcloud CLI is also seen as sluggish; debate over whether Python is at fault vs backend API latency.
  • Suggested mitigations: heavy use of Terraform, scripts, and avoiding the console for anything but one‑offs.

Managing stack rot: VMs vs managed services

  • One camp recommends building on plain Linux VMs to avoid provider deprecations; others argue this just shifts maintenance burden and can be worse when every VM becomes a snowflake.
  • Several advocate continuous, aggressive upgrading to keep technical debt small instead of letting systems drift for years.

Alphabet’s business model and market power

  • Commenters note Alphabet still derives the majority of revenue from ads; “search revenue” is widely interpreted as advertising.
  • Some describe Google as a “cash volcano” that allows mediocre planning and endless product churn without visible financial penalty.
  • Search and ads are criticized for effectively taxing “existence” on the web via brand‑keyword bidding and competitor targeting.

Cloud market dynamics and competition

  • GCP’s 34% growth is seen as impressive but from a smaller base; some believe AWS is slowly losing relative momentum, others argue the AI boom is simply expanding the whole cloud pie.
  • Opinions diverge on technical quality: some rate GCP’s infra and UX above AWS/Azure; others say Google’s support, enterprise focus, and sales execution are clearly weaker.

Cloud vs bare metal and “utility” analogies

  • Several want more appetite for owning servers again, warning about dependency on “silicon nimbus.”
  • Others prefer cloud as a utility, but argue vendor lock‑in prevents true commoditization.
  • Ideas floated: state‑run “utility clouds” for basic compute/storage; faster, more modular colo to rebalance power away from hyperscalers.

Google’s AI position and ad conflict

  • Many believe Google’s scale, cash, chips (TPUs), and engineering make it a top long‑term AI contender once easy funding for startups tightens.
  • Skeptics highlight missed opportunities (late to productized LLMs), internal flakiness, and a cultural tendency to kill or pivot products.
  • A major concern: tension between truly helpful AI and ad‑driven incentives. People anticipate AI assistants being polluted by sponsorship (“MLM‑friend” effect), though some argue every provider will face the same pressure and/or shift to subscriptions.
  • There’s debate over whether AI is winner‑takes‑all: some expect a few dominant incumbents (Google, Microsoft); others see room for multiple players and note deep, non‑LLM Google AI (Waymo, AlphaFold) as a separate advantage.

GCP as a product and enterprise vendor

  • One thread paints GCP as technically excellent but weakest on sales, support, and long‑term trust; AWS and Azure are described as more aggressive and responsive with enterprise features and deals.
  • Another thread, from experienced GCP users, reports high reliability at scale, strong UX, and believes cloud is one of the few Google products that “just works,” with App Engine cited as ahead of its time despite later strategic missteps.

Miscellaneous points

  • “Over 70% of Cloud customers use its AI products” is criticized as partially forced usage (e.g., AI‑fronted support flows).
  • TPUs are praised as good value but too hard to integrate into real workloads.
  • Some see Alphabet as analogous to past “safe bets” like IBM, warning that size and past success don’t guarantee future leadership.