TickerTain
TickerTain
NewsroomShortsPortfolioConvergence
NewsroomShortsPortfolioConvergence
$ANTHROPIC·$MA····$INTC····$BLUE-ORIGIN·$SPCX····$CRWV····$CRM····$MSFT····$NVDA····$ORCL····$CURSOR·$AAPL····$OPENAI·$AMZN····$UBER····$GOOGL····$META····$TSLA····$DATABRICKS·$PERPLEXITY·$LYFT····$NBIS····$TSM····$LITE····$ANDURIL·
$ANTHROPIC·$MA····$INTC····$BLUE-ORIGIN·$SPCX····$CRWV····$CRM····$MSFT····$NVDA····$ORCL····$CURSOR·$AAPL····$OPENAI·$AMZN····$UBER····$GOOGL····$META····$TSLA····$DATABRICKS·$PERPLEXITY·$LYFT····$NBIS····$TSM····$LITE····$ANDURIL·
←

cj desai

T2 · manager / operator

CJ Desai became CEO of MongoDB in November 2024 after a career in product and engineering at Oracle, Symantec (under John Thompson), and ServiceNow (under Fred Luddy). He emphasizes customer-driven innovation, meeting 10-12 customers weekly, and positioning MongoDB as the operational data layer for AI agents.

8 calls·8 names·50% bull·last heard 2 months ago·Sourcery VC
track record

no scored calls yet — needs a stated position or a categorical verdict, with a matured window vs SPY

top calls

highest conviction · one per company
1sthigh conviction
$MDBMongoDBposition

MongoDB CEO sees three AI customer waves driving foundational data layer demand

MongoDB's scale-out document architecture, multicloud/on-prem flexibility, and vector search position it as the operational data layer for frontier labs, AI-native startups, and enterprises building agentic applications.

Sourcery VC2026-07episode →
2ndhigh conviction
$ELEVENLABSElevenLabs

ElevenLabs runs 50M+ AI agents on MongoDB, validating architecture for agentic workloads

ElevenLabs' scale — over 50 million agents running on MongoDB — demonstrates the database's ability to handle massive agentic workloads with unstructured data, vector search, and real-time requirements.

Sourcery VC2026-07episode →
3rdmedium conviction
$SNOWSnowflake

MongoDB CEO differentiates: Snowflake is analytical OLAP, MongoDB is real-time OLTP

Snowflake serves analytical workloads (OLAP) for business analysts, while MongoDB serves real-time operational workloads (OLTP) like credit card transactions and AI agent memory — distinct categories with different latency requirements.

Sourcery VC2026-07episode →

most discussed · click a bar to filter

  • $ELEVENLABS
  • $SNOW
  • $DATABRICKS
  • $SPCX
  • $ORCL

recurring themes

  • AI Infrastructure1
  • Data Center Infrastructure1
  • Sovereign AI1
  • AI Agents1
  • AI Economics & Business Models1
8 total
$ELEVENLABS
ElevenLabs
HIGHcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake
ElevenLabs runs 50M+ AI agents on MongoDB, validating architecture for agentic workloads
ElevenLabs' scale — over 50 million agents running on MongoDB — demonstrates the database's ability to handle massive agentic workloads with unstructured data, vector search, and real-time requirements.
"when we look at the 11 lab story, they have north of 50 million agents depending on when you look at it all running on MongoDB. Data is the unsung hero and data is that... And the…"
14:27
$SNOW
···
Snowflake
MEDcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake
MongoDB CEO differentiates: Snowflake is analytical OLAP, MongoDB is real-time OLTP
Snowflake serves analytical workloads (OLAP) for business analysts, while MongoDB serves real-time operational workloads (OLTP) like credit card transactions and AI agent memory — distinct categories with different latency requirements.
"MongoDB is a operational database or a real-time database. So credit card transactions or anything real time, that's what we do. The category is called online transaction processi…"
11:19
$DATABRICKS
Databricks
MEDcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake
MongoDB CEO groups Databricks with Snowflake as analytical layer, not operational
Databricks, like Snowflake, targets analytical processing (OLAP) rather than the real-time operational data layer (OLTP) where MongoDB competes for AI agent workloads.
"If you want analytical data layer where you can ask a question a business analyst internally will ask a question for those kind of use cases you use those other companies database…"
11:19
$SPCX
···
SpaceX
LOWcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake
MongoDB CEO: Space-based data centers physically possible, not bearish on concept
Physics-wise, orbital data centers with continuous solar power could solve AI's energy constraint; SpaceX already building ground data centers for Anthropic and exploring space-based capacity.
"physics- wise, at least from what I have read, it seems like it is there is a nonzero chance of it succeeding, meaning it will succeed once you put enough uh um attention, resourc…"
34:40
$ORCL
···
Oracle
MEDcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake
MongoDB CEO contrasts modern autoscale with Oracle's expensive DBA model
AI-native companies refuse to hire expensive Oracle-style DBAs; they demand databases that self-manage and autoscale via software, creating a structural shift away from legacy operational models.
"in database world when I was at Oracle, the most expensive person you can get is called Oracle DBA, database administrator. And they were the most expensive people that you have t…"
27:28
$PLTR
···
Palantir
MEDcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake
Hyperscalers adopting Palantir's forward-deployed engineer playbook for AI customers
Major cloud providers are copying Palantir's model of embedding engineers directly with customers to help them navigate complex AI infrastructure deployments, signaling enterprise AI adoption complexity.
"lot of announcements from hyperscalers on forward deployed engineers they are using Palunteer's uh playbook uh from my perspective right and the reason is because customers ers ar…"
20:54
$MDB
···
MongoDB
HIGHcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake· position
MongoDB CEO sees three AI customer waves driving foundational data layer demand
MongoDB's scale-out document architecture, multicloud/on-prem flexibility, and vector search position it as the operational data layer for frontier labs, AI-native startups, and enterprises building agentic applications.
"MongoDB stands for humongous, which most people don't know. So humongous database that as you scale, you should feel comfortable as an AI company that you can scale with MongoDB..…"
3:59
$COGNITION
Cognition
MEDcj desai·Sourcery VC·2 months ago·Data is Back: MongoDB, Databricks, Snowflake
Cognition's Devon agent first task was spinning up MongoDB, signaling developer preference
The fact that Cognition's AI software engineer Devon chose MongoDB as its first database setup task indicates strong developer ergonomics and default preference in AI-native tooling.
"Scott was one of the first interviews that we did. And it was funny because we were talking about Devon and the comeback of Devon. And the first task that Devon did was spin up Mo…"
1:46
9
AI Infrastructuretailwind
MongoDB positioned as foundational memory layer for AI agentic applications
As AI shifts from model-centric to agent-centric, the operational data layer storing unstructured data, vectors, and conversation memory becomes critical infrastructure; MongoDB's scale-out architecture and multicloud deployment make it a default choice for frontier labs and AI-native companies.
9
Data Center Infrastructuretailwind
Hyperscaler capacity constraints driving enterprise on-prem and multicloud comeback
Top hyperscalers are turning away even top-50 customers due to GPU/power shortages, forcing Fortune 500 firms to repatriate workloads to on-prem data centers and adopt multicloud strategies — reversing the decade-long cloud-first trend.
8
Sovereign AItailwind
Data sovereignty regulations forcing on-prem AI deployments in Europe and regulated industries
French and EU regulations mandate data residency, compelling large enterprises to run AI workloads on-premises rather than public cloud — creating structural demand for databases that operate identically across cloud and sovereign environments.
8
AI Agentstailwind
Agentic workloads exploding: ElevenLabs runs 50M+ agents on MongoDB
The agent economy is generating unprecedented data volumes — each agent creates continuous streams of unstructured data requiring real-time storage, vector search, and memory retrieval — creating a massive tailwind for operational databases built for scale.
8
AI Economics & Business Modelstailwind
Software-writing-software collapses experimentation cost, making speed the paramount competitive factor
As AI generates code, the marginal cost of innovation drops and experimentation cycles compress; companies must pivot roadmaps weekly based on customer feedback rather than annual planning, favoring platforms that autoscale and self-manage.
7
Enterprise AI Adoptionmixed
Enterprises in early experimentation with complex multi-model agentic architectures
Large enterprises are building agentic stacks with 50+ components (LLMs, frameworks, vector DBs, guardrails) but have not yet deployed customer-facing agents at scale; architecture complexity is growing rapidly as they solve for observability, security, and deterministic outcomes.
7
Open Source AImixed
Enterprise model usage remains mixed: no standardization on open vs closed source
Customers deploy a heterogeneous mix of open-source (Hugging Face, domain-specific) and proprietary models (OpenAI, Anthropic) based on use case; coding agents favor closed models, while specialized tasks use open source — no winner-take-all yet.
6
Space Economytailwind
Orbital data centers physically viable to solve AI energy constraint, per MongoDB CEO
Space-based compute with continuous solar power could bypass terrestrial energy bottlenecks; SpaceX already building ground data centers for Anthropic and exploring orbital capacity — physics supports the concept, execution risk remains.