As base AI models become commoditized, the key competitive advantage will be the unique, proprietary context an enterprise builds. This 'organizational brain,' composed of customer data, internal knowledge, and past learnings, will be more valuable than the plug-and-play model itself.

Despite clear ROI, Glean's founder argues current AI costs are "absurdly expensive," citing a single internal engineering triage agent that cost one million dollars per month. He believes this is a historical anomaly and predicts that competition and open source will force inference prices to drop by orders of magnitude.

The key to cost-effective enterprise AI isn't more compute, but better context management. By pre-caching and structuring data, Lovelace AI achieves results comparable to frontier models with less than 1% of the compute cost, avoiding expensive "just-in-time" processing for every query. This shifts the bottleneck from query-time to ingestion-time.

Glean's co-founder argues that most enterprise tasks don't require expensive frontier models. Open-source alternatives are now capable enough for the vast majority of use cases. The primary adoption driver has shifted from data privacy to pure cost savings, as enterprises seek to control skyrocketing AI bills.

Consumer AI like ChatGPT has broad context but lacks the specific depth needed for business problems. To get great results from enterprise AI, you must provide it with deep, rich context like unified customer data, campaign history, and internal team conversations. Quality output is a direct function of context depth.

Enterprise AI vendors are moving beyond simple search or chat applications. The real value and defensibility lie in the underlying 'context engine' that connects and understands siloed company data, user activity, and permissions. This engine provides the accuracy and relevance that generic LLMs fundamentally lack.

Glean's strategy extends beyond being a user-facing app. It positions itself as a centralized system of intelligence that provides superior, offline-processed context to other AI front-ends like Claude or Cursor, a rapidly growing use case.

The future of AI at work belongs to platforms with the richest shared business context, not just the best LLM. A proprietary data model like Asana's Work Graph, which maps goals and tasks, creates a compounding advantage by feeding AI agents the specific data needed to be effective and improve over time.

Mike Cannon-Brookes posits that business acceleration from AI equals `intelligence * context`. Instead of relying solely on large context windows, Atlassian's strategy is to create a rich, pre-indexed "Teamwork Graph." This graph connects code, org charts, and skills, providing cheaper, faster, and more relevant answers from AI agents.