Graduating From Google 🎓
Seven Years Inside Google: An Education
Over seven years at Google, I moved across six unique roles, worked under thirteen managers, and operated from 8 offices (including 4 home offices). I shifted from Federal cloud sales ➝ fortune 500 sales ➝ BigData consulting ➝ product management (part time) ➝ data governance leadership ➝ global product management ➝ global Trust & Safety technical programs. The surface interpretation of that path is mobility, or career advancement. But here’s a more honest interpretation: learning how to see patterns, where my peers had a need, and finding how to be helpful at the right moment.
Each role revealed a different layer of how large-scale systems function, and where opportunity emerges to contribute. Sales exposed incentive structures. Account management exposed dependency chains between customers, requirements, and capacity limits. Technical consulting exposed the gap between what platforms promise and what enterprises can actually operationalize within their own culture. Product work exposed how signals and scales decide what gets built. Trust & Safety exposed how intelligence behaves, risk systems adapt, and politics come into sharp focus.
The feelings that emerge, from that kind of traversal, is akin to the satisfaction of completing a PhD… in some type of polymath curriculum.
The work I enjoyed most, always had an immense intersection at its core. For example: (1) Data Governance focused on data management + analytics + regulatory issues + data quality + lineage and information tracking; (2) Applied AI Systems focused on automation problems + workflow design + data curation + privacy + enterprise-scale architecture, etc. Best of all, I had a purview across regions and systems. This included launching and scaling Google’s Data & AI Governance platform (Dataplex), working with Fortune 500 organizations and securing AI-driven infrastructure against adversaries.
The peak of my career was probably 2021-2024 when I was provided Google Cloud universal admin access, which was an incredible privilege.
Across all my engagements, the deeper insight I gained… was systems are downstream of human behavior. Incentives, fear, ownership, desire, and ambiguity all shape how data is created, labeled, and governed. These dynamics become most visible during times of environmental compression, when one signal is louder than others, or, when counter-reactions constrain expression to offset excess noise. And AI systems do not currently correct for these variables. What appears as technical output is often a reflection of upstream human decisions and data streams, escalated beyond direct visibility.
Blameless Postmortem
I struggle to imagine a company more curious and insightful than Google.
The most effective execution environments were built around highly ambitious, self-starters, with strong character. About 90% of Googlers are expected to operate with a high degree of autonomy and navigate structural ambiguity; those who could not adapt to that model tended to phase out quickly (average tenure of 2 years). This created a culture where momentum was driven from the bottom-up, or horizontal alignment, rather than enforced from top-down.
At Google, Many management postures existed, by necessity or design, but all types held-in common a deep avoidance to micromanagement. In practice, this reduced traditional oversight and created space for execution and high-risk personal growth opportunities. For those comfortable operating without direction (high levels of accountability and visible deliverables) this was an advantage, and it felt refreshing compared to traditional corporate culture.
In professional services and product management, cross-team collaboration was a consistent strength. Bottom-up programs required different teams independantly forming synergies to get things done. Working across engineering organizations and engaging peers was generally frictionless (always expected), especially when tied to customer outcomes. Cross-team knowledge-share was never penalized, it was also expected.
At its best, Google Cloud’s culture leaned toward opportunity rather than constraint. There was a sense of fast-paced playfulness in how problems were uncovered, handled, and solved; the majority of Googlers prioritized problem solving and experimentation, over internal politics. Multiple orders of redundancy and blameless postmortums allowed Googlers to fail fast and learn quickly without fear of losing real momentum.
At the same time, some structural challenges existed.
In non-customer-facing roles, duplicate work was common. Multiple teams would often exist to compete, pursue similar initiatives in parallel, without clear coordination, leading to inefficiencies or fear that was very visible. This is how in 2021 the running joke became “but we have 6 versions of that”. Some roduct management and engineering orgs had a strange relationship; roadmap ownership was not always held by just one party.
In professional services, leadership churn introduced constant instability. Over these seven years, I had six Sr. Vice Presidents of Cloud, each lasted about a year, somtimes less. They always brought shifts in direction, priorities, and operating models. This created discontinuity in product optics. While the argument was “new leadership allows acceleration, adaptability, and momentum building”, it resulted re-org anxiety and decreased morale.
At the director level, operating styles were often polarized. Some functioned as reactive responders, only existing to handle moments of escalation. Others willingly and frequently fired-shots across their domain, sometimes at the expense of alignment with adjacent teams. Both modes had escalation utility, but neither consistently produced stable, long-term impact. However, I think that’s more true for Google Cloud, not Ads, or YouTube, etc.
Within Trust & Safety, to no surprise, the operating posture leaned heavily toward risk mitigation. It was jokingly refered to as “The Bunker”. It held a level of internal sensitivity and caution that, while understandable given external hostilities, could at times resemble covert-style super-compartmentalization or paranoia. The environment siloed ALL information flow as NTK. By design, curiosity did not exist, and cross-team collaboration was nascent.
The Future of AI
1. Scale, Capability, and Control
The dominant trajectory remains concentrated, but this might change.
Labs such as OpenAI, Anthropic, xAI, DeepMindand Meta Superintelligence Labs continue to push the frontier of model capability through scale. Larger datasets, more compute, and tighter integration between research and infrastructure remains in-focus. These systems act as general-purpose intelligence layers, capable of reasoning across domains and serving as the backbone ongoing advancement. However provenance remains a problem.
OpenAI remains the benchmark for consumer-facing AI, defining interaction paradigms and distribution. Anthropic has differentiated through controllability and safety, with models that are increasingly favored in enterprise environments for their predictability. DeepMind is focusing on creative generation, long-horizon research, multimodal reasoning, and the integration of AI into real-world problem domains. xAI is aggressively pursuing real-time integration and AGI-oriented systems, while Meta continues to advance an open ecosystem strategy, distributing high-quality models broadly to shape the market.
These labs define what AI can do.
2. The Quiet Backbone of the Ecosystem
Beneath these foundation is a layer that is arguably more important long-term: the infrastructure that makes AI usable, reproducible, and extensible.
Hugging Face operates as the AI community layer for the entire ecosystem. It represents the repository, tooling, and interface through which models, datasets, and experimentation flow. Together AI and EleutherAI extend this by pushing open datasets, decentralized training, and transparency into model development. Answer.ai represents a different approach, focusing on efficiency, which builds upon frontier-level breakthroughs in smaller environments.
These organizations are shaping the conditions under which intelligence can be built and shared.
In many ways, they define how developers participate.
3. Context Over Generality
A separate class of companies is emerging around the constraint of real-world usability.
Contextual AI, Adept, Imbue, and Perplexityare focused less on raw model capability and more on grounding intelligence in real systems. Retrieval-Augmented Generation (RAG), agentic workflows, and domain-specific reasoning are all attempts to solve the same problem: making AI reliable within resource constrained or politically fragmented environments.
These systems prioritize accuracy, traceability, and actionability over open-ended generation. Adept and Imbue push toward agents that can operate software and complete multi-step tasks. Contextual AI focuses on keeping models anchored to enterprise data. Perplexity is redefining how models interact with live information, integrating retrieval and citation.
This layer represents a shift from intelligence as conversation to intelligence as execution.
4. Exploring Evolutionary and Decentralized Intelligence
Another branch is exploring alternatives to brute-force scaling, I call these “the small but mighty”.
Sakana AI represents one of the most interesting directions. They’re using evolutionary techniques to merge and adapt models rather than retraining them from scratch. Nous Research and similar collectives operate in a decentralized fashion, refining and improving base models through distributed contributions.
Together, these efforts point toward a future where intelligence is not trained once, but continuously recombined, adapted, and optimized. This particular paradigm comes with some of the most challenging provenance and governance problems in the AI industry. While I do not expect these platforms to solve AI provenance, their experiance and insight may contribute toward a growing ecosystem of standards development.
This is a move from static models to living systems of intelligence.
5. Identity, Access, and Control
As AI systems approach human-level interaction, a new problem emerges: distinguishing humans from machines.
Tools for Humanity claims to address this through “World ID” and hardware-based verification. Their work sits outside traditional AI modeling and instead focuses on identity infrastructure (verified humans in a world of indistinguishable agents). Mostly driven from a global-design (globalism) frame.
In parallel, systems like WisdomAI are redefining how intelligence interacts with data. Rather than centralizing knowledge, they enable agents to operate across fragmented systems without moving the data itself. This introduces a federated chain-model of intelligence (absent of a network protocol). I expect their work may showcase the risk of some solutions related to modern AI provenance (if blockchain technology is made relevant again).
This layer is not building intelligence itself, but trying to decide what users can act, and where.
6. Edge, Local, and User-Owned Intelligence
A growing movement is pushing against centralization entirely.
Web AI, VESSL AI, and Hydrahost are exploring models and/or infrastructure that runs locally, trains dynamically, or operates in decentralized networks. These systems prioritize privacy, ownership, and distributed supply-chain resilience over centralized control.
Running models on-device or in-browser eliminates entire classes of risk associated with data movement. Decentralized agent networks challenge the idea that intelligence must be owned and controlled by a single entity.
This is the emergence of user-owned intelligence.
7. Beyond Neural Scaling
Finally, a subset of labs is questioning the dominant paradigm itself.
Symbolic AI reintroduces structured logic into systems that have historically relied on probabilistic reasoning. Archetype AI expands intelligence beyond text and images into the physical world, incorporating sensor data to build models that can interpret real-world environments. Imbuecontinues to push toward dedicated reasoning systems that behave more like full-stack engineers than assistants.
The Bigger Picture
Beneath the surface, the future of AI is being shaped by a set of unresolved questions, which the public-market has less room to answer.
“What type of activities can these systems do.”
“Who is allowed to participate in building them.”
“Who has the authority to act through them.”
“Where should that action can take place.”
The future of AI will be decided by the behavior of nations and their population’s value system, over the next few years.
Centralized network protocal standards might continue to prevail in China.
Western standards and new protocals will provide topography for AI Agency.
Orbital infrastructure will likley provide additional durability and future margins.
At its core, AI’s trajectory hinges on concentrated intelligence vs user-owned (decentralized) vs distributed system agency. It all maps to geopolitcs.
In a Western-led system, concentrated intelligence can persist, but only so long as it maintains a technological and infrastructural lead.
In a bipolar scenario, two powers enter high risk AI-gorilla warfare, requiring hardened and decentralized system edges (end users).
In a multipolar world, adversarial activity exceeds central capacity or decentralized risk, so distributed management is the only option.
In my opinion, the worse-case scenario is a hegemonic global-order, which abandons western values, such as freedom of expression, association, religion, etc. Such a system would require a high burden of maintenance and a highly compliant/passive-agressive authority. However, this scenario seems unlikley. In 2027 or 2028 the internet is an active theater of attrition. We’ve moved past the era of "dumb" bot attacks. The 500 billion pings a year that CloudFlare can currently configuration of geopolitics, and the 10 billion human-reviewed leads that FANG companies can currently manage, are both lack-lucster compared to a future where trillons of highly intelligent AI Agents can produce highly complex heist operations.
When an adversary deploys a lean, highly optimized model to probe or attack a network, their cost per attempt can be negligible. Defense, by contrast, often requires inference-driven monitoring across the full stack—an AI security agent that matches the attacker’s depth of reasoning and persistence. This symmetry holds only when both sides operate with comparable efficiency. As optimization diverges, the burden shifts: the less efficient defender must spend disproportionately more to maintain coverage. In a scenario where a concentrated providers operates multiple world-class data centers while an adversary relies on purpose-built and highly optimized facility, the balance of power tilts.
Under these conditions, traditional margin assumptions fall apart. So if anyone is wondering “why are layoffs happening” it’s because adversarial activity is a steep tax on margins. That burden compounds under polycentric governance, when concentrated systems follow multiple regulatory regimes; this combination introduces additional coordination, un-even standards and compliance overhead.
You can read more about my opinions on distributed Trust & Safety in this older post, or review how geopolitics are changing in this other post.
