The Great Rewiring
How Big Tech and startups are racing to adopt AI and rewire work as we know it
In my last post “Rearranging the Factory Floor,” I highlighted the opportunity for companies to change how they operate with AI, just as factories did a hundred years ago with electricity. How they transform and the time it takes for that transformation will determine the scale of job displacement and economic upheaval.
To better understand these changes, I spoke to over 20 current and former employees of Amazon, Atlassian, Block, Google, Meta, Oracle, and several startups.
The consensus is that this is not a solved problem. From a leader at a large technology company: “I think your expectation on how ‘AI-first’ we are is not yet aligned with reality. This is all evolving.”
It may be evolving but it is evolving rapidly. Extreme urgency is driving adoption of AI, experimentation in how to accelerate the work of individuals, how to make existing processes more efficient, and how to rewire work itself.
Being first to figure it out is existential - the difference between pulling ahead and being left behind.
Accelerating Individuals
Standard Rigs. Many companies are still at the bolt-on stage and have just given people lots of tools to play with. Block gives employees “literally every tool you could want” and is building out agents across many of those tools creating “overwhelming choice.” Another startup I spoke with is leaning into standardization, with the CTO aiming to get every single engineer to operate at the same level, executing the same way by creating a standard Claude Code “rig” that includes markdown files containing repository overviews, style guidelines, skills for repetitive tasks.
Slop Fatigue. One side effect of leaning into AI is the slop fatigue of senior staff who have to review the code and docs being fired at them by the cannon. A frustrated startup leader shared that most of the docs he’s getting from his team are AI generated (fine) but haven’t been iterated on to refine the content (not fine). The review experience is like reading a paragraph of text where letters have been transposed: when you skim it’s readable but under closer inspection it falls apart.
Making processes more efficient
Bottleneck shift aka The Slop Cannon Corollary. “The Theory of Constraints” focuses on identifying and removing bottlenecks in order to increase overall system output.
The bottleneck in product development used to be creating code: There were never enough engineers on a team to build everything the company wanted. Now with AI, everyone is a builder and the bottleneck has shifted from creating code to reviewing code before it goes into production. Companies are attacking this new bottleneck with very different approaches:
YOLO. If a human sanity checks an AI code review and it looks good, it goes into production.
Using a different AI to review the other AI. For example, if Claude was used to generate the code, then GPT is used to review the code. Rationale is that hopefully have different blind spots. A fine idea but feels a bit like hiring a horse to watch your dog.
Risk stratified. Atlassian has two different processes: one for “infra” and one for “product.” On the infra side, the expectation is that every change is manually reviewed because the blast radius is large whereas on the product side, it’s closer to YOLO.
Automation of expertise. When I worked at Google, researchers with Computer Science PhD’s were responsible for tracking the state-of-the-art in machine learning, make high judgement decisions around what new techniques might improve model performance, and work with teams to run experiments.
Today at Meta, there is an agent pipeline that scrapes arXiv (website where academic papers are shared rapidly before peer review), identifies relevant papers, ranks them, writes new code in a sandbox, and tests new methods to see which have the most impact. Fully automated, scalable, and several times faster.
This automation of business processes will become more common outside of tech with companies hiring for “AI Transformation” roles and bringing in consultancies to help them retool (Financial Times).
Rewiring work with extreme urgency
There is a gnawing sense from executives to individual contributors that their orgs are too big and not organized correctly, driving extreme urgency for leaders to figure out how to rewire work. As one CRO put it, “Any company started more than two years ago is already behind.”
5x too many people. When speaking to people at Google and Amazon, they think orgs have roughly 5x more people than they need to keep the lights on. Obviously a company has to do more than keep the lights on to grow but that is an enormous amount of extra cost and complexity to carry vs. an AI-first competitor. Individuals are making alternative career plans, and proactively leaving. Leaders are trying to redesign the org.
New orgs - Smaller pods of builders (Google, Meta). One Google org is breaking itself up into full stack pods of about 30 people. Meta is on the same path, breaking down its 1,000 person Reality Labs group into “AI Pods” (Business Insider). The pendulum is swinging from functional orgs to much smaller product / business focused teams.
Same org - Far fewer people (Block). Block had grown considerably before it cut 40% of staff in February. It added headcount organically with the Cash App run as its own BU, inorganically through the acquisition of Afterpay, and across the board during COVID with management projecting a new normal rather than a one-time boost. The result was an organization that felt bloated. Too many people held too narrow scopes leading to “20 people being involved to get one thing done.” Notably there was and still is no real plan for how to organize: “I don’t know how we are structuring teams. I don’t know if anyone knows.” It appears Jack believed two things: AI would enable running the company more efficiently and that the cuts would create the conditions for that rewiring to occur. (More on that in the next section).
Observations
Very little structured thinking. Individuals and companies are doing a lot but I haven’t seen a clear articulation of how they are approaching AI adoption, rewiring the business, and measuring success. I plan to spend more time on this in a followup post.
Opportunities for a complete new ways of working. Early in March I shared an AI Chief of Staff that I’d developed (link). The core ideas were a) AI should not be a reactive tool the same as a spreadsheet or a word processor, but a proactive partner in work and b) AI should not be siloed in one app but review emails, chats, meetings, and docs to help identify and prep the most important work. This should change how work is done, how teams communicate, and how information is shared across an enterprise. Last week Google announced Workspace Intelligence which addresses (b) but not (a). A step in the right direction and hopefully more to come.
Process power will be a source of differentiation. Because AI adoption is not yet a solved problem, companies that figure it out will pull ahead of competitors. In the late 1990s NVIDIA figured out how to design chips using hardware emulation (Acquired, Sequoia). This allowed them to skip fabrication steps and ship twice as fast as their competitors. Those same opportunities exist today. Take the highly contested market for customer support AI agents. The underlying models and frameworks are the same so expect to see greater focus on process innovation around customer onboarding and continuous improvement and less transparency on the specific methods.
Creative destruction and renewed culture. The experience for employees still at Block is worth highlighting. Because there are fewer people, there is both the need and opportunity to change how work is done. There are fewer places to hide and poor performance is easier to spot and address. The reduction has also helped break down some of the old Block/CashApp/Afterpay cliques. That coupled with retention bonuses, salary bumps, and stock refreshes adding 30-50% to people’s total grant have helped renew the culture.
Be sure to reach out and share how AI is changing how your company works.
Really interesting observations, Carter, thanks for sharing them. I especially appreciate the point about how the org structure pendulum is swinging from functional designs to smaller pods focused on products & businesses.