Tomasz Tunguz Profile picture
Dec 22 2 tweets 3 min read Read on X
11 Predictions for 2026

Every year I make a list of predictions & score last year’s predictions. 2025 was a good year : I scored 7.85 out of 10.

Here are my predictions for 2026 :

1. Businesses pay more for AI agents than people for the first time.
This has already happened with consumers. Waymo rides cost 31% more than Uber on average, yet demand keeps growing. 1 Riders prefer the safety & reliability of autonomous vehicles. For rote business tasks, agents will command a similar premium as companies factor in onboarding, recruiting, training, & management costs.

2. 2026 becomes a record year for liquidity.
SpaceX, OpenAI, Anthropic, Stripe, & Databricks IPO, with SpaceX & OpenAI ranking among the ten largest offerings ever. The pent-up demand from 4+ years of drought finally breaks. Fear of disruption by fast-growing AI systems drives defensive acquisitions exceeding $25b as incumbents buy rather than build.

3. Vector databases resurge as essential infrastructure in the AI stack.
Multimodal models & world/state-space models demand new data architectures. Vector databases grow revenue explosively as they become the connective tissue between foundation models & enterprise data.

4. AI models execute tasks autonomously for longer than a workday.
According to METR, AI task duration doubles every 7 months. 2 Current frontier models reliably complete tasks taking people about an hour. Extrapolating this trend, by late 2026, AI agents will autonomously execute 8+ hour workstreams, fundamentally changing how companies staff projects.

5. AI budgets receive scrutiny for the first time.
Buying committees & boards push back on AI spend. Small language models & open-source alternatives rise in popularity as research labs determine how to specialize them for particular tasks, achieving state-of-the-art performance at a fraction of the cost. Developers prefer them for 10x cost reductions.

6. Google distances itself from competitors via breadth in AI.
No other company achieves breakthroughs across as many domains : frontier models, on-device inference, video generation, open-source weights, & search integration. Google sets the pace, forcing OpenAI, Anthropic, & xAI to specialize in response. The era of every lab competing on every frontier ends.

7. Agent observability becomes the most competitive layer of the inference stack.
Engineering observability, security observability, & data observability fuse into a single discipline. Agents require unified visibility across code execution, threat detection, & data lineage. This marks the beginning of the confluence I predicted in 2025 : the three observability spaces finally converge.

8. 30% of international payments are issued via stablecoin by December.
The efficiency gains in cross-border settlement are too large to ignore. As regulatory clarity improves in major markets, stablecoins move from the periphery of crypto to the core of global trade finance, displacing traditional SWIFT rails for a significant portion of B2B volume.

9. Agent data access patterns stress & break existing databases.
Agents issue at least an order of magnitude more queries to databases & data lakes than people ever did. This surge in concurrency & throughput requirements forces a redesign of the overall architecture for both transactional & analytical databases to handle the relentless demand of autonomous systems.

10. The data center buildout reaches 3.5% of US GDP in 2026.
The scale of investment mirrors the historical expansion of the railroads. The only factor that slows overall building is perceived risk within the credit market, particularly in the private credit market. The massive growth in that asset class suddenly shows strains of increasing default rates, creating a potential bottleneck for the most capital-intensive infrastructure projects.

11. The web flips to agent-first design.
Most developer documentation & many websites become agent-first rather than people-first. This shift occurs because many purchasing decisions are now informed first through agentic research. Consequently, the front door needs to be designed for robots, while the side door caters to people.
I forgot #12 :

Cloudflare becomes the gatekeeper for agentic payments.
The x402 protocol revives HTTP’s long-dormant 402 “Payment Required” status code, enabling AI agents to pay for API access in real-time. Cloudflare’s position as the web’s infrastructure layer makes it the natural chokepoint for this new commerce. This concentration becomes a flashpoint as a few giants push Cloudflare to be more open.

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More from @ttunguz

Aug 24, 2023
If we needed another exclamation point on the tremendous growth opportunity, NVidia’s earnings punctuated the euphoria with gusto. Image
Revenue grew 88% in a quarter, nearly doubling. The company had projected $11b & exceeded projections by $2.5b or 26%.
The Data Center business, which sells to clouds & consumer companies drove nearly all of the growth, surging 141% quarter-over-quarter to $10.3b & nearly 3x annually.

image

NVDA’s profitability is remarkable. Image
Read 9 tweets
Jul 31, 2023
Earlier today, New Relic announced its sale to Francisco Partners & TPG for $6.5b.

The acquisition is notable for two reasons.

First, it accelerates the momentum within the technology buyout space. Image
At its current pace, technology buyout volumes of venture-backed technology companies will tie or exceed the ten year high, charted in 2022 of about $20b.
PE buyouts provide 2023’s slower M&A market liquidity & activity, perhaps will begin to spur strategic/corporate acquirers into action.

Second, New Relic’s sale price is close to the recent highs measured on January 1st of each year Image
Read 7 tweets
Jul 10, 2023
The $1m ARR figure used to be the benchmark in 2018 & early 2019 for raising a Series A. But the data shows how much the market differs from a few years ago.

Series A round size standard deviation has grown by between 4-5x in 4 years.
A Series A used to mean a single flavor. Today, like a Neapolitan ice cream, Series As can mean a $1m round, a $23m round or a $110m round.
The term Series A is an arbitrary moniker for a new share class used for convenience. I once met a startup founder who called his first round of financing Series Awesome. A $10m round could be called Series Starfruit.
Read 8 tweets
Jun 21, 2023
I think of marketing teams as hedge funds. Marketing teams develop a portfolio of different strategies to acquire leads.

Some days, content marketing works. A post challenging like this one () hits HackerNews or a journalist covers the company.motherduck.com/blog/big-data-…
Other days when content strategies struggle, a witty paid ad campaign entices some clicks & form fills. On some rainy Tuesday, the weekly webinar attracts an unusually promising audience.

Like an investment portfolio, any individual strategy may thrive one day & suffer the next.
But as the effort broadens,, leads compound at a steadier & steadier rate.

When I first started to write this blog, I was thrilled with 100 page views. One day, a post hit the front page of Hacker News.
Read 6 tweets
May 17, 2023
Yesterday, the Enterprise Tech 30 List was revealed. Congratulations to all the winners (especially MotherDuck, Hex, & Omni)!

I analyzed the headcount patterns within these companies to shed light on three questions :
1. How are these top companies changing their headcount through the downturn?
2. What percent of headcount is in product & engineering?
3. What percent of headcount is in sales & marketing? Image
The typical company grew headcount by 57%. The mid-stage businesses grew fastest at 88%, next early companies at 62%, & the later companies by 43%. The Giga companies held headcount flat over the past year.
Read 10 tweets
May 16, 2023
A product manager today faces a key architectural question with AI : to use a small language model or a large language model?

This is my current mental model of when to choose a large or small model : Image
When to choose a large model :

- time to ship is critical : many of these models are available via API, requiring formatted data as an index or vector database - which an engineer can achieve within a few hours for a working beta.
- the company would prefer to rely on external experts to drive innovation within the models.
- the company has no plan/interest to staff a team to manage AI infrastructure or develop deep machine learning experience / expertise in-house.
Read 11 tweets

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