
The Ecosystem Bringing AI Solutions Onchain | Seed, Ship, Scale | $VERTAI
Joined March 2024
- Tweets2.3K
- Following41
- Followers15.1K
- Likes1.6K
Pinned Tweet
Vertical is moving towards one clear goal
To become the place where founders and builders receive the support they need to launch and grow
This is the vision behind Vertical Labs ⚡️
Watch the video as @MarkTbuilds explains what the future of Vertical looks like
Speaking with a16z, OpenAI’s James Sun outlined four barriers to AI adoption: understanding the tools, connecting them, deployment and ongoing support
Ari Weinstein explained that computer-use agents operate applications through their interfaces, reducing the need for dedicated connectors
Sun identified an opportunity for startups to package these capabilities into complete workflows customers can use
For founders, the commercial offer should define:
◻️ The result the customer will pay for and how often it is needed
◻️ Where human approval or intervention is required
◻️ Who handles failures and changes in the underlying software
◻️ Delivery costs, including supervision and support
An incubator should help founders use these requirements to establish the buyer, scope and price
A paid pilot should test the complete service under normal working conditions
Renewal reviews should cover continued usage, measurable customer benefits and margins after delivery costs
OpenAI's James Sun and Ari Weinstein on the AI adoption gap between San Francisco and the average American business, and why agentic computer use is the way to bridge it:
"Living in San Francisco, we see a lot of thinking, 'Wow, the labs can do everything,' and, 'Why doesn't everybody just install Codex and just have it do stuff?'"
"But for the average American and the average small to medium business owner or the average enterprise, even understanding what the tools are capable is [hurdle] one, connecting tools in a way that makes sense is two, deploying it is three, servicing is four."
"The really cool thing about computer use as a technology is the universality of it. It works with every application. It's unlike any other thing that we've built where the developer has to do some work to adopt it, someone has to build a connector or MCP server."
"If there are complex workflows that require a lot of disparate software, computer use is a great way to join them, complete the entire end-to-end workflow, and then building it in a format and UI that feels good to hold for those customers feels still like a really, really big opportunity that I think is still open."
@JamesZmSun @AriX
Running AI agents means managing both model access and the compute they consume
With Router and Staking, @vAPI_Network connects the two:
150+ models through one endpoint, with a daily compute allocation linked to staking that builders can share across their agents
For us at Vertical, this gives the token a clear purpose within the product
Participation in the network provides access to resources builders can use to develop, test and run their applications
Watch EP02 to see how it works
EP02: Router & Staking.
Agents need intelligence to get work done. Network participants need a way to put their stake to use.
Router brings access to AI models. Staking connects $vAPI participation to compute credits, putting that intelligence within reach.
Two more pieces fall into place: api.vapinetwork.ai
For an AI startup selling to businesses, a paid pilot should have defined criteria for moving into regular use
Before it begins, the team and customer should agree on:
◻️ The task being tested and the expected result
◻️ The time and cost of the existing process
◻️ Acceptable error rates and required human review
◻️ Who controls the budget and makes the renewal decision
During the pilot, the startup should record the full cost of delivery: implementation hours, model usage, retries, support and manual corrections
Onboarding and custom development fees should be reported separately from recurring product revenue
This helps establish how much income depends on additional work for each customer
At renewal, review whether the customer uses the product regularly, achieves the agreed results and accepts pricing that covers delivery costs
Any discount or additional support needed to secure the renewal should remain visible in the assessment
For an incubator, this evidence should inform the next funding decision
If each deployment requires extensive custom work, the budget should account for that delivery model
If deployments become more consistent, the team can test whether acquiring additional customers produces sustainable margins
The next review should compare those expectations with actual renewals, implementation hours and margins across subsequent customers
AI Is Lowering the Cost of Cyberattacks
Artificial intelligence helps attackers research targets and develop tools with less technical knowledge and manual work
In its 2026 Global Threat Report, CrowdStrike reported an 89% year-on-year increase in the activity it observed from attackers using AI
Two documented cases 👇
◻️ On 19 October, NorForge documented more than 1,100 attack projects involving AI agents across 14 exposed dashboards linked to one activity cluster
The figure counts projects, not confirmed breaches
◻️ In January and February, Amazon documented an AI-assisted campaign that compromised over 600 FortiGate devices across more than 55 countries
An attacker with limited technical skills gained entry through weak credentials and exposed management interfaces
AI helps attackers research targets, build tools and adapt their methods. Smaller teams can attempt more attacks with less manual work
Agents deployed inside companies can also be targeted
Malicious instructions hidden in a document or webpage can cause an agent to expose data or misuse its access to other systems
As companies give agents access to customer records, code and payments, they need to fund the security work that comes with it
Permissions must be enforced outside the model
Sensitive actions need separate authorisation. Teams need logs, monitoring and a way to revoke access quickly
Security testing must continue as software changes. AI can help defenders find vulnerabilities and check whether fixes work
For founders and incubators, due diligence should establish who owns security, which systems agents can access, how incidents are handled and whether the operating budget covers that work
Public token fundraising has recovered unevenly.
In CryptoRank’s dataset, the average disclosed public token sale raised approximately:
◻️ 2021: $1.50 million
◻️ 2023: $460,000
◻️ 2025: $2.08 million
◻️ 2026 YTD, through 10 June: $3.15 million.
Activity remained volatile in 2026
CryptoRank reported just $8.4 million in total token-sale fundraising in August. The first week of October recorded the highest weekly total since February
Capital remains concentrated
Coinbase’s analysis of 962 projects with documented token-sale proceeds from 2021 to September 2026 found that 130 projects accounted for approximately 75% of proceeds
This broader sample includes public and private sales beyond launchpads
The sector mix also changed. Within that sample:
◻️ Infrastructure, L1/L2 networks and DeFi represented 64.5% of sales in 2025
◻️ AI’s share rose from 1.3% in 2021 to 13.2% in 2025
◻️ Gaming and metaverse sales fell from 75 in 2021 to 17 in 2025
The January–September 2026 subset contains only 20 documented sales, with recent disclosures incomplete. It does not support a reliable update to those sector shares
Selected CoinList offerings in early 2026 illustrate three fundraising themes:
◻️ AI infrastructure finance: USD.AI - lending against GPU collateral
◻️ Confidential infrastructure: Zama - computation on encrypted data
◻️ Integrated DeFi: Flying Tulip - trading and lending within one protocol
Mastercard and Visa are building payment infrastructure for AI agents, enabling them to transact on behalf of users within defined budgets and permissions
@vAPI_Network already puts this model to work for paid APIs
With Call, an agent can find a provider, pay per request in USDC on @base through x402, and use the returned data to complete a task
Users set spending limits, and every paid request generates a receipt
The walkthrough below shows the full process, from installation to an agent querying onchain data, analysing liquidations and comparing liquidity pools
From setup to your first paid API call. See vAPI Call in action.
Our protocol lead @MarkTbuilds walks through installation and demonstrates Call using @graphprotocol as a showcase provider, with USDC payments on @base powered by x402.
Launchpads need to prove their value before and after a token sale
Only 13 public token sales closed in May 2026, the lowest monthly figure since late 2020
Active institutional venture investors fell from 767 in Q3 2025 to 333 in Q3 2026
Returns have given buyers little reason to return: by December, 84.7% of 118 tracked launches from 2025 traded below their initial valuation
We see three directions for launch platforms:
◻️ Selection and support before the sale. Teams undergo formal review and receive help with tokenomics, market entry and fundraising preparation. Some programmes also provide initial funding
◻️ Regulated sales. In the EU, professionally placing tokens on an issuer’s behalf is a regulated service under MiCAR, requiring the relevant authorisation
◻️ Control over capital after the sale. Some platforms hold proceeds in treasuries that teams cannot move independently
Monthly budgets cover operations, while additional spending requires governance approval
A launchpad seeking to rebuild trust should explain why it selected a team, what support that team receives and who controls the money after the raise
Vertical AI retweeted
From setup to your first paid API call. See vAPI Call in action.
Our protocol lead @MarkTbuilds walks through installation and demonstrates Call using @graphprotocol as a showcase provider, with USDC payments on @base powered by x402.
Vertical AI retweeted
The first piece is live. The next two arrive together.
Another step toward one trusted network where agents and humans do business.
EP02: Router & Staking - October 21.
AI agents need a way to find and pay for the services their tasks require
Incubated by Vertical, @vAPI_Network brings both into one flow with Call, using per-request USDC payments on @base
Watch the walkthrough
EP01: Call.
Every paid API is a capability an agent can buy. Every provider gains a route to a new customer.
Call connects them: discover APIs and pay per request in USDC on @base, powered by x402.
The pieces are falling into place: api.vapinetwork.ai/call












