2026-05-13 08:00:00
Nobody will open Gmail five times a day in five years.
The average knowledge worker receives 121 emails per day.1 That’s one every four minutes during working hours.
The inbox is a conveyor belt that keeps accelerating. You open Gmail. You read. You decide. You respond. One at a time. But the belt doesn’t wait. It just moves faster.
Today’s triage is generic : “This is from your boss. I need to work on that today. Next. Spam. Archive. Spam. Archive. Newsletter, read & archive.” Tomorrow’s is personal. User-defined skills & rules. Programming in English that encodes your priorities, your relationships, your workflow.
A receipt arrives & forwards itself to the expense platform before you see it. An inbound lead hits the CRM, gets scored, & a draft proposal waits in your outbox. The workflow starts the moment the email lands.
Then there’s the archive. Years of context about every relationship, commitment, & decision you’ve made. That history becomes a personal context layer that informs how your AI handles the next message. On-device models process sensitive messages privately.2
The inbox disappears. What remains are the 6 messages that actually matter.
2026-05-12 08:00:00
It’s time for the 2026 Annual Theory Go-to-Market Survey. This is a brief 25-question survey.
Our goal is to understand how startups have evolved their sales, marketing, customer success, and cash management over the last several years by comparing these results to our surveys from 2022 through 2025.
We will publish these results and answer questions about them at upcoming Office Hours.
This year, we’re focused on five key hypotheses. Each is designed to be rigorously testable with the survey data:
Augmented reps outperform both autonomous AI and unaugmented humans. As AI tools mature, companies face a choice: deploy AI alongside SDR teams, replace them with autonomous tools, or forego AI entirely. We expect augmented teams, humans plus AI, will show the best conversion rates and productivity gains.
AI is widening the performance gap between top and bottom quartile GTM teams. The companies investing most heavily in AI may be pulling ahead, widening the efficiency, growth, and conversion gap between quartile-one and quartile-four sellers.
Buyer-side AI adoption is the bigger GTM disruptor than seller-side AI. While most companies focus AI investment on their own GTM teams, an emerging dynamic is buyers using AI themselves: automated RFPs, AI-assisted evaluations, and even AI negotiation. We expect this shift will lengthen sales cycles and create new objections.
AI efficiency gains are being captured as headcount reduction, not revenue growth. When companies report “AI productivity” gains, the primary result may be flatter SDR hiring, not faster pipeline or higher revenue. The headcount flattening signal, not cost savings, is the real story.
Founder expectations on AI have reset downward as reality caught up. In 2024, the most optimistic respondents expected 500% efficiency gains from AI and recorded 0%. We expect this perceived-measured gap has narrowed as companies recalibrate what AI can realistically deliver.
With this data, we should be able to draw broader conclusions about the continued shift from growth to efficiency, measure the real impact of AI on GTM teams, and understand the emerging dynamics of AI-to-AI selling.
If you complete the survey, I will share with you the anonymized raw data so you can perform your own analyses. If you have questions, just message me on Twitter or send me an email.
2026-05-11 08:00:00
As demand for AI inference explodes, I’ll be asking a lot more of my little computer.
How much more?
Over the past five weeks, I’ve been using local models to see how much of my daily work I can accomplish without the trillion parameter models in the cloud. The answer is half.
| Category | Count | % of Total | Example |
|---|---|---|---|
| Other | 521 | 35.3% | Catch-all for unstructured requests |
| Scheduling | 254 | 17.2% | Check availability, propose meeting times |
| Market Research | 192 | 13.0% | Competitor analysis, fundraising data |
| Summarization | 184 | 12.4% | Transcript review, video summaries |
| Email & Inbound | 170 | 11.5% | Draft replies, follow-ups, forwards |
| Engineering | 147 | 9.9% | Debug scripts, API fixes, CLI tasks |
| Admin | 10 | 0.7% | Travel, expenses, reimbursements |
If you classify these 1.4k tasks by category, half can succeed on a local 35B model. Email & Inbound, Scheduling, Summarization, & Admin total 618 tasks (41.8%). Market Research & Engineering split roughly 50/50 between simple tasks (data lookups, script fixes) and complex ones (multi-source synthesis, architectural decisions). That gets us to 50%.
There are many reasons to use local models : privacy, cost, asset depreciation.1
But in reality, the only one that really matters is latency.
I ran a head-to-head benchmark this morning. Eight agentic tasks, same prompts, both models warmed. Qwen 3.6 35B-A3B-4bit on my MacBook Pro M5 vs Claude Opus 4.5 via API.
The local model isn’t smarter. Opus 4.5 scores ~20% higher on reasoning benchmarks. Local models lag frontier by 3-4 months, and for large-scale complex tasks, that gap matters. But for routine agent tasks, it rarely does.
Opus wins on structure & polish : bullet points, headers, cleaner code. Qwen wins on brevity, often half the tokens. I read every output side by side, and both completed the tasks correctly. For agent tasks where output feeds into another system, terseness is a feature.
Localmaxxing, pushing more inference to local models, is an inevitable response to tokenmaxxing. As local models improve & close the gap with frontier, more users will shift workloads to their own hardware.
If half the work runs 2x faster on my laptop, I’ll take that trade every time. My little computer is about to earn its keep.
A MacBook Pro depreciates whether you use it or not. Running local inference extracts compute value from a sinking asset before resale. ↩︎
2026-05-08 08:00:00
Enterprises run on AI agents. So do the attackers.
What does it mean to build, secure, and operate AI systems when both sides - defenders and attackers - are automated?
Jonathan Jaffe, CISO at Lemonade, is one of the most forward-thinking security leaders in this age of AI, with more than 25 years of experience in technical security roles since 1997.
Join us for a conversation covering :
The format. 15 minutes online. One topic. Call-in questions live. No slides. No pre-written questions. Just a real conversation.
Register here for Office Hours : Securing the Agentic Enterprise with Jonathan Jaffe.
2026-05-07 08:00:00
Anthropic grew from $1B to $30B in 15 months. So why does it trade at a discount to public comparables?
High-growth companies trade on forward (NTM) revenue. Anthropic’s $30B run rate implies $20B in actual TTM revenue. If they exit 2026 at an $80B run rate1, we can estimate NTM revenue of around $50B. The EV/NTM multiple is 17x.
Anthropic commands a 65% discount to Palantir while growing nearly 3x faster. Four factors explain the gap.
| Company | Revenue Growth (NTM) | EV/NTM |
|---|---|---|
| Anthropic | 165% | 17x |
| Palantir | 62% | 49x |
| Cloudflare | 29% | 23x |
Capital intensity. Anthropic has raised $15B+ and will need more. The xAI Colossus GPU deal alone will cost $6.2B annually at current market rates2.
Profitability uncertainty. Revenue multiples assume future profitability. GPUs account for 60-65% of AI data center capex3. Anthropic could be growing into a high-margin software business or a capital-intensive utility. The market doesn’t know yet.
Growth volatility. In March & April, Anthropic’s revenue exploded. Will that growth continue? Public markets prefer predictable growth curves they can underwrite.
Exogenous political risk. AI regulation is in flux. Export controls, compute caps, safety requirements : any of these could reshape the competitive landscape overnight.
The discount isn’t irrational : it prices uncertainty in the fastest growing & quickest changing market.
The $80B run rate is an estimate used to derive the NTM multiple. ↩︎
Using Ornn’s Compute Price Index spot rates : (150k H200s × $2.64) + (50k × $4.13) + (20k × $5.29) = $708k/hr, or $6.2B annually. ↩︎
Goldman Sachs estimates GPUs and IT equipment account for 60-65% of hyperscaler AI data center capital expenditure. ↩︎
2026-05-05 08:00:00
What happens when a startup employee leaves on a Monday?
In a twenty-person engineering team, one resignation is a 5% headcount loss. The remaining nineteen absorb the work.
In an AI-pilled three-person team running twenty autonomous agents, one resignation is a 33% headcount loss.
The agents do not resign. They keep generating, reviewing, testing, and deploying. But one-third of the institutional memory that trains, prompts, validates, and debugs the agent fleet walks out the door.
The tradeoff at the heart of AI/labor ratio decisions is not throughput. It is resiliency.
At 10/90 (10% AI, 90% labor), a typical mid-stage startup engineering budget powers ~20 engineers and a layer of Copilot, Cursor, and inference spend. Traditional hierarchy. Human code review as the bottleneck. The org chart looks familiar.
At 50/50, the same budget powers ~12 engineers and a fleet of agents. Engineers become solution architects, problem decomposers, and prompt designers. Manager span of control widens because agents do not need standups.
At 90/10, three engineers sit at the center of a constellation of autonomous agents that generate, review, test, deploy, monitor, and optimize. No managers. No hierarchy. No redundancy.
If we are building software factories, maybe it’s time to study operations research.
In manufacturing, the rule of thumb is simple: run your factory at 70–90% utilization. At 100%, one breakdown cascades into missed deadlines, burned teams, and lost customers. The slack is not waste. It is the feature that keeps the system robust.
Engineering teams are not factories, but the same logic applies. When you concentrate orchestration knowledge in three heads, you are running at 100% utilization.
Most startups should not make that bet yet.
