Tomasz TunguzModify

2026-01-07 08:00:00

The PC era began with a black screen & a blinking cursor. This stark prompt was the only gateway, beckoning a user to test the computer’s power.

Today, the interface to AI looks identical : an empty text box & a blinking cursor.

1980s Command Line	2020s AI Interface
Image credit: Newton Freehostia

We’ve spent decades moving away from the command line, first to Windows & then to websites. Now we’re arriving right back where we started, albeit with a much smarter partner behind the box.

In the 1980s, critics of the graphical user interface & the mouse argued that the command line was all anyone needed. Windows & icons were distractions from the raw power of the machine. But that power came at a cost : arcane syntax. mkdir, ls, pbcopy, the incantations that separated technical users from everyone else.

“Help me develop a cracked CRM” is much more powerful than npm init. Colloquial English is all we need. The barrier isn’t memorizing commands; it’s knowing what to ask.

Is this empty text box controlled by a robotic genius enough? Do we need a user interface today?

There are lots of problems with working with AI, even with all the promise of agents that anticipate our every need. Crafting the right prompt isn’t obvious, small wording changes produce different outputs, & the prompts need to be updated for each new model release. AI is expensive, & many of us have hit limits. Most strikingly, we’re back to single-threaded computing : one conversation, one task, one at a time.

Also, sometimes we prefer to see things. Projects like OpenAI’s Canvas & Anthropic’s Artifacts let users create bespoke interfaces at runtime : a snake game, physics experiments, a CRM for a startup. But that initial wave of excitement has passed. We don’t talk about canvases or real-time UIs much anymore, though. Why not?

Standard UIs persist for good reasons. They enable the training of thousands of people & manage parallel tasks. They guide users toward the next action, bridging the divide between the prompt-fluent & everyone else. They manage costs by balancing deterministic & non-deterministic processes. AI & standard code both have a role to play.

So would Apple have succeeded if DOS had spoken English? Yes. The need for user interfaces on top of powerful platforms doesn’t disappear when the underlying system gets smarter; it intensifies.

GUIs allowed millions of people to extract productivity from computers who never would have memorized command-line syntax. The same dynamic will play out with AI : interfaces that surface capabilities, manage parallel tasks, & guide users toward what’s possible will unlock productivity gains for the many, not just the prompt-fluent few.

The text box is powerful. The desktop built on top of it will be more powerful still.

2026-01-05 08:00:00

For decades, the to-do list has been a catalog of debt, a deceptively thin list of items to do, with icebergs of work hidden beneath the surface.

AI transforms tasks to work that has already been done.

Vibe Kanban, Gastown, & Conductor are the first instantiations of this for software developers. They have jargon-laden descriptions like “multi-agent orchestrator” or “visualizer,” but they are, at heart, simple & beautiful Kanban boards of done & dusted work.

Karri Saarinen of Linear describes this shift as the disappearing middle :

When the middle disappears or blends in, what becomes more in focus is the work of forming the right intent & making sure the outcome actually meets it.

As long as I’ve worked with computers, the middle of computer work has been the most important & time-consuming part.

An idea came quickly. The finished product - code, presentation, email, or spreadsheet - shipped in moments. Most of the effort was pushing pixels, writing code, or massaging formulas.

As AI improves, & it has very meaningfully in the last few months, AI completes more of the middle. In fact, planning & evaluation become the two most important parts of MacBook manipulation.

Gergely Orosz captures this perfectly :

When I just started out developing I remember being so so so full of ideas that I was coding in my head and wished I could have done programming while commuting / on the bus. With eg a phone. But it was impossible, ofc. Now it’s possible!!

Sometimes I lie awake wondering how to capture more of my thoughts for AI to work on while I sleep.

How do I describe the piece of software I want written, or the design of a particular image? How do I specify the layout & structure of a PowerPoint presentation or a blog post?

Design becomes more essential ; the clarity of intent becomes the limiting factor for our productivity.

The to-do list has become the done list, & it is waiting, patiently, for your approval.

2025-12-30 08:00:00

Meta is acquiring Manus for $2.5 billion.¹² Alongside the announcement, Manus disclosed $100 million in ARR achieved in eight months, 147 trillion tokens processed since launch.³

Can we use those figures to explain the acquisition price?

Publicly traded software companies have gross margins of 71-72%. AI companies run lower. Gross profit per token may be a better indicator of earnings potential. Some AI companies already use gross profit as a quota metric rather than revenue.

Here’s how six AI inference companies compare.⁴

DeepSeek & Together AI trade at the lowest multiples, they resell inference. Perplexity commands the highest at 222x, it’s an application. Different layers, different monetization, different multiples. Manus sits at 50x.

On a log-log scale, gross profit per token correlates 0.70 with valuation (R² = 48.5%). Raw token volume? Just 0.47. Investors value token monetization more than overall volume.

A caveat : these figures are estimates from press reports, funding announcements, & industry analyses.

The sample is small. But the correlation suggests investors & acquirers already believe what the industry hasn’t yet named.

2025-12-29 08:00:00

This year I traveled through systems, human & machine, from the mathematics of complexity to industrial espionage.

1. The Complex World: An Introduction to the Foundations of Complexity Science: Donella Meadows’ Thinking in Systems introduced me to feedback loops a decade ago. This book goes deeper, surveying where complexity science stands today.
2. Math Without Numbers by Milo Beckman: A vivid & accessible tour of abstract mathematics. Beckman covers topology, infinities larger than infinity, & other mind-bending concepts, all without a single digit & with proofs to explain it all.
3. On Democracy by E. B. White: White, New Yorker editor, Charlotte’s Web author, Elements of Style co-creator, is among my favorite writers. These essays provide a time capsule to help us understand where we are today. Written as fascism spread across Europe & America debated isolationism, White’s defense of America provided a window into another era of rapid political change.
4. Breakneck: China’s Quest to Engineer the Future by Dan Wang: Wang argues that the US & China each have a dominant form of government problem-solving : through laws in the US, or through engineering in China.
5. God Save Texas by Lawrence Wright: Wright’s Pulitzer Prize-winning exploration of the Lone Star State. Both California & Texas value independence & innovation. As a Californian, it was fascinating to see Texas through Wright’s eyes.
6. The NVIDIA Way: Jensen Huang believes Nvidia’s worst enemy isn’t competition but complacency. Kim’s portrait reveals a CEO who spends late nights alongside his team, torturing them into greatness.
7. The Unaccountability Machine by Dan Davies: Davies argues that modern organizations function like runaway AIs, making decisions no human intends. A hotel executive cuts staff to improve the balance sheet. Later, you can’t check into your room & the clerk can only offer a voucher. There’s no one to call, no way to communicate back. That’s an unaccountability machine.
8. Karla’s Choice by Nick Harkaway: I’ve read every le Carré. His son Harkaway picks up where his father left off, adding a bracing entry to the canon.
9. Titanium Noir by Nick Harkaway: Before spy novels, Harkaway spent fifteen years writing science fiction. Curious about his earlier work, I was not disappointed. Titanium Noir explores a world in which the wealthy have access to drugs that double their lifespan & double their size. The novel examines what happens when health becomes a function of wealth.
10. Boom: Bubbles & the End of Stagnation by Byrne Hobart & Tobias Huber: Why does transformative progress require financial bubbles? This builds on Carlota Perez’s work on technology innovation cycles. Hobart & Huber argue that bubbles’ poor accountability shelters the world’s most important breakthroughs.

What should I read in 2026?

2025-12-28 08:00:00

Motive, the AI-powered fleet management company formerly known as KeepTruckin, filed their S-1.

Founded in 2013 by Shoaib Makani, Ryan Johns, & Obaid Khan, the company has grown from an electronic logging device (ELD)¹ compliance tool into a comprehensive physical operations platform serving nearly 100,000 customers across trucking, construction, oil & gas, & manufacturing.

Motive’s platform has since expanded beyond compliance to combine AI-powered dashcams for driver safety, GPS tracking for real-time visibility, & spend management cards to control costs. This suite acts as a central operating system for physical economy businesses, unifying data from vehicles, drivers, & equipment into a single interface.

Both companies achieved roughly $500m in ARR at the time of IPO.

Samsara grew 76% annually at IPO compared to Motive’s 27%. This growth was fueled by higher sales efficiency, likely driven by deal size; Samsara’s average contract value (ACV) of $17k was more than three times Motive’s $5k.

This disparity likely stems from different initial go-to-market strategies. Motive initially focused on the SMB segment (specifically owner-operators & small fleets needing a cost-effective compliance solution for the ELD mandate), building a massive base of smaller customers.

Motive’s ACV of $5k closely mirrors Fleetmatics’³ ACV of $6.8k at its IPO, confirming Motive joined Fleetmatics as a high-volume SMB player, but with modern AI capabilities. In contrast, Samsara targeted mid-market industrial operations from the start.

While legacy players like Geotab & Verizon Connect (Fleetmatics) maintain large installed bases, Samsara & Motive are capturing share with modern, AI-first platforms.

Motive’s customer metrics reveal an enterprise-focused growth strategy. Large customers (>$100k ARR) grew 58% year-over-year, from 312 to 494. This 58% growth in large accounts compares favorably to Samsara, which grew its $100k+ customer count 48% year-over-year at the time of its IPO.

Motive’s enterprise accounts grow at 126% net dollar retention, meaning the average large customer spends 26% more each year. The company is successfully landing & expanding within enterprise accounts.

Core customers grew at 17%, from 7,875 to 9,201.

Both companies maintain similar 70% gross margins, impressive for businesses that ship hardware with their software. This positions them at the median for public SaaS companies, despite the hardware component.

The profitability picture differs. Motive’s net loss margin expanded from -35% in 2023 to -42% in the most recent period, while Samsara improved from -100% to -34% in the nine months before its IPO. Samsara’s improvement was driven by operating leverage : revenue grew 108% while sales & marketing expenses increased 11%.

Despite similar gross margins, Motive’s bottom line is weighed down by significantly higher “Other Expense” ($57M in the last nine months). This figure includes $22M in interest expense on approximately $300M of term debt, with the remainder driven by non-cash charges related to convertible securities.

Samsara generated $328k in ARR per employee at IPO. Motive generates $111k, roughly one-third. With 4,508 employees versus Samsara’s 1,500 at IPO, Motive has built a much larger organization to achieve similar scale, with about 3.2k employees in Pakistan.

Samsara trades at approximately 14x forward revenue, roughly in line with other vertical SaaS companies.

Motive has raised $600 million from Kleiner Perkins, GV, BlackRock, & others at a $2.85 billion valuation. With $500M in ARR, that implies a roughly 6x ARR multiple in the private markets.

Despite Motive having raised approximately $600 million in equity capital, Samsara’s path to IPO was significantly more capital-intensive, with over $930 million raised pre-IPO. However, Motive’s reliance on debt, holding roughly $300 million in term loans, partially offsets this difference in total capitalization, highlighting contrasting financing strategies between the two leaders.

Given these factors, what is Motive worth? Using an interaction model⁴, which is a refinement on the initial linear model, the analysis implies a valuation of approximately $3.7 billion.

Congratulations to the Motive team on reaching this milestone. Building a $500M ARR business in physical operations is no small feat, especially in a competitive market.

2025-12-23 08:00:00

Every year I make a list of predictions & score the previous year’s. You can find my 10 Predictions for 2026 here. 2025 was a good year : I scored 7.85 out of 10.

1. The IPO market rips.

Score : 0.6.

46 software IPOs raised $12.3b in 2025, up from 21 IPOs raising $3.8b in 2024. The 2021 peak saw 126 tech IPOs raise over $150b. ¹ CoreWeave & Circle successfully debuted with significant market caps & strong post-IPO performance. ² However, others like Figma & Chime are trading below their last private valuations, reflecting a more discerning public market. We also didn’t see some of the high-flying IPOs like SpaceX, Stripe, & Databricks go out, although 2026 is a new year.

2. Google continues their surge in AI.

Score : 1.

Google has reclaimed its position at the apex of the AI landscape, ranking in the top tier of nearly every major category. Gemini 3 represents a fundamental leap in pre-training efficiency & multimodal integration, a thesis explored in The Scaling Wall Was A Mirage ³.

Gemini 3 Flash ⁴ has redefined the frontier for performance & latency, becoming the default engine for high-frequency agentic workflows.

In the open-source arena, the Gemma models ⁵ consistently hold the top spots for their weight classes, offering 70B-level reasoning in 27B packages. Even in creative media, Google’s video models ⁶ rank in the top three globally, prioritizing temporal consistency & character stability for enterprise use.

3. Voice becomes a dominant interface for people with AI as speech models are pushed on device & the accuracy/latency astounds.

Score : 1.

OpenAI reported that ChatGPT voice chat accounts for 19% of total user engagement as of October 2025. ⁷ Globally, there are now 8.4b voice assistants in use, with 153m users in the US alone. ⁸ 80% of businesses plan to integrate AI-driven voice into operations by 2026. ⁹ The prevalence of dictation with Whisper, WisprFlow, & conversations with agents like Gemini Live is now normal.

4. US VC investment remains roughly around $210-$230b, but VC fundraising increases by 20%

Score : 0.5.

US VC investment hit the mark, landing at approximately $220b for 2025, driven by massive AI rounds. ¹⁰ However, the fundraising prediction missed. While deal counts rose 11% in early 2025 ¹¹, actual US VC fundraising is on track for a ~20% decline, totaling roughly $65b for the year. ¹² A prolonged liquidity crunch & a slow exit environment kept LPs cautious, despite the enthusiasm for AI.

5. Consolidation is the theme for the Modern Data Stack.

Score : 1.

2025 was a record year for data infrastructure M&A, as the “Modern Data Stack” shifted from a collection of best-of-breed tools to a vertical race for integrated platforms.

The consolidation moved down the stack, proving that the race is now for power, compute, & integrated software. Most notably, CoreWeave’s acquisitions signal the rise of the “Full-Stack Hyperscaler,” owning everything from the GPU to the MLOps layer. ¹³¹⁴¹⁵

6. The first $100m ARR company with 30 or fewer employees is created.

For comparison, Slack had 650 employees at $100m ARR. Ramp had 275. Wiz had 400.

Score : 1.

AI-native teams have achieved unprecedented efficiency. Cursor hit $100m ARR in January 2025 with just 12 employees ¹⁶, proving that agentic software can scale with minimal headcount. Midjourney continues to defy gravity, reaching $500m ARR with a team of roughly 100. ¹⁷ These teams leverage the capital efficiency of agentic software to meet ravenous consumer & enterprise demand. ¹⁸

7. After years of declines, the US web3 engineering population grows by 25% as the government embraces crypto & web3.

Score : 1.

US Web3 jobs grew by 26% in 2025, reaching 21,600 positions. ¹⁹ The regulatory environment shifted significantly, leading to a surge in institutional adoption & a new wave of consumer applications built on decentralized stacks.

8. Data center spending by hyperscalers eclipses $125b for the year as the AI race fuels demand for GPUs. Broadcom is the hottest semiconductor stock of the year.

Score : 0.75.

Hyperscaler CapEx far exceeded expectations, reaching an estimated $315b to $350b in 2025. Amazon alone spent $100b ²⁰, followed by Microsoft ($80b) ²¹ & Google ($75b) ²². Broadcom’s stock surged as it became the primary beneficiary of the AI networking buildout, outperforming even NVIDIA in the latter half of the year, but it was third in the domain next to Google & Micron. ²³²⁴

9. Stablecoin supply increases 50% to $300b as more businesses adopt this payment mechanism for B2B payments. Stablecoin volume is greater than 3x Visa’s transaction volume.

Score : 1.

Stablecoin supply hit $310b in December 2025. ²⁵ Monthly adjusted stablecoin volume has now surpassed Visa’s network volume, with annual on-chain volume exceeding $46t—nearly 3x Visa’s transaction volume. ²⁶ B2B adoption has accelerated as businesses seek faster, cheaper cross-border settlement.

10. Observability, SIEM, & Business Intelligence begin to use the same data lake. Usage-based pricing for many software companies creates a need for a single data lake. The data lake becomes the dominant data architecture across all workloads.

Score : 0.

While there is some convergence in the use of OpenTelemetry (OTel) for both security & observability ²⁷, the broader vision of a single data lake for BI, SIEM, & observability has not materialized. Enterprises continue to maintain siloed architectures for performance & compliance reasons, & the cost savings from hybrid lakehouse models haven’t been enough to force a total consolidation. ²⁸²⁹