ChatGPT Ads hit a $1B annualized run rate. Here’s the marketer’s guide to testing them.

TL;DR: ChatGPT is becoming a serious advertising channel. OpenAI reported a $1 billion annualized ad-revenue run rate and 1.2 billion weekly users. Advertisers can buy sponsored placements through Ads Manager, match offers to conversational context, and measure purchases and leads. Ads can appear on Free and paid Go plans; higher paid tiers remain ad-free.

Early B2B and B2C results justify a controlled test, but success means profitable customers and qualified pipeline. Start with one offer, a defined budget, working conversion tracking, and a clear rule for whether to continue.

Someone opens ChatGPT and describes the problem your company solves. They explain their budget, compare approaches, question tradeoffs, and work toward a decision.

That is a compelling place to introduce a useful product.

Marketers should learn this channel now. A small, disciplined experiment can teach you which customer problems deserve an ad, a better landing page, or a better offer.

Here’s the practical guide.

On August 31, OpenAI said ChatGPT Ads reached a $1 billion annualized revenue run rate in fewer than 200 days, with tens of thousands of advertisers.

OpenAI said on October 5 that ChatGPT reaches 1.2 billion people each week. That is the overall product audience, not guaranteed ad reach.

Who actually sees ChatGPT ads?

Plan or experience	Ad eligibility
Free	Ads may appear in eligible experiences and markets
Go — a paid subscription	Ads may appear
Plus and Pro	Ad-free
Business, Enterprise and Edu	Ad-free
Accounts identified as under 18	No ads
Temporary Chat	No ads
Free’s Ads-Free option, where offered	No ads, with lower usage and feature limits

So “ads only reach free users” is incorrect. Go is paid and ad-supported.

For B2B, ad-free business workspaces are a real reach limitation. It does not follow that every decision-maker uses an ad-free plan or that Free users never buy business software. Validate the audience through lead quality.

How many accounts are paid versus free?

Publicly disclosed metric	Figure	Disclosure date
Weekly ChatGPT audience	1.2 billion	October 5, 2026
Consumer subscribers	More than 50 million	February 27, 2026
Paying business users	More than 9 million	February 27, 2026
Subscribers, reiterated by OpenAI	More than 50 million	March 31, 2026
Current Free + Go that see ads	About 1.1 Billion users	October 5, 2026

How the advertising works

The core unit appears below a ChatGPT response, with an advertiser identity, headline, description, image, and destination link. It is labeled as advertising.

Delivery considers the conversation, ad content, landing page, advertiser settings, and context hints. Where personalization is enabled, permitted signals from a user’s broader ChatGPT experience can also contribute. Eligible ads compete in a relevance-weighted auction.

OpenAI says advertisers cannot pay to shape the organic answer and do not receive users’ private chat transcripts. A sponsored placement is separate from an organic recommendation.

For marketers, the useful shift is in the brief. Start with the situation in which your offer becomes useful.

Example: “We help a growing SaaS company manage usage-based billing” is more informative than “innovative software for everyone.”

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Formats: what you can use and what is still being tested

Format	What it does	Status as of October 5
Standard sponsored ad	Headline, copy, identity, image and link below a response	Core Ads Manager format
Product-feed ads	Build retail ads from catalog information such as product imagery, titles and prices	Documented campaign type
Sponsored Agents	User chooses to open a labeled conversation with a business’s AI representative	Limited alpha with selected advertisers; no open early-access requests
New visual ads during image generation	Visual product or service inspiration, separate from the generated image	Announced October 5; US test starts later in October with selected advertisers

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Product feeds help retailers manage broad or changing catalogs. Keep price, availability, and destinations current. Uploading an ads feed does not purchase inclusion in organic answers.

Sponsored Agents could help with considered purchases: fit, compatibility, configurations, and service questions. Treat that as a testable use case, not a feature every account can turn on today.

The new visual format is also an upcoming test. An announcement is not universal availability.

Pricing: understand the bill before the bid

Objective	What delivery seeks	What you pay for
Views / CPM	Exposure	1,000 impressions
Clicks / CPC	Website traffic	Valid clicks
Conversions / oCPC	A selected conversion event	Valid clicks
Conversions / oCPM	A selected conversion event	1,000 impressions

Conversion optimization does not mean pay-per-conversion. Its bid controls do not guarantee your achieved acquisition cost.

OpenAI’s general starting guidance for a Clicks campaign is a $3–$5 maximum CPC bid. That is bid guidance, not an average CPC, fixed price, or recommendation for every business. Conversion campaigns have different bid controls.

The current USD minimum daily budget is $25 per campaign. Daily budgets are averages: spending may reach twice the daily amount on a given day, subject to the applicable weekly limit. For a fixed experiment, consider a campaign-total budget where offered.

Set your own economic ceiling. Hypothetically, if your allowable cost per sale is $60 and 3% of clicks buy, your affordable average CPC is $1.80. A $4 click would need a higher conversion rate or more value per customer to work.

6. How to set up your first campaign

1. Open [ads.openai.com](https://ads.openai.com/). Check that your legal entity is in a supported country. Create one advertiser account and invite colleagues into it.

2. Complete account setup. Check legal name, public brand name, logo, verification and billing. Choose country, currency and time zone carefully; these settings cannot be changed after creation.

3. Prepare one destination. Use a relevant product, solution, comparison or offer page. Make the promise, price or buying path clear.

4. Install measurement. Create an Ads Manager data source; connect Pixel and/or Conversions API; verify the event you intend to optimize.

5. Choose an objective and budget. Set locations and available platform controls. Use Conversions when tracking and the selected standard event are ready; otherwise a Clicks campaign can diagnose initial traffic quality.

6. Create focused ad groups. Each should represent a product or customer need with relevant context hints.

7. Add distinct creative variations. Change the benefit, proof or buying situation. Review generated suggestions against what your product actually does.

8. Review, submit and monitor. Check approval, billing, delivery and tracking before making performance judgments.

You can also use supported Shopify or HubSpot integrations, a buying partner, or the Ads Manager plugin to work through natural-language instructions. Verify the integration’s supported objectives before assuming it matches every feature in Ads Manager.

Context hints are worth learning properly

Context hints describe a product, who it helps, and the circumstances in which it is useful. OpenAI recommends specific, accurate, natural-language ideas.

They are relevance guidance. They do not enforce exact-match keywords, job titles, geographic restrictions, schedules, or audience exclusions. Use the platform’s actual controls for restrictions.

Here are hypothetical examples to adapt only when true:

Offer	Useful context hint	Matching destination
B2B billing software	“Software for SaaS finance teams introducing usage-based pricing alongside subscriptions.”	Usage-billing overview and demo
Agency service	“AI workflow implementation for small marketing teams with repetitive content operations.”	Scope, example workflow and consultation
Travel luggage	“Lightweight cabin luggage for travelers comparing storage capacity and airline size limits.”	Dimensions, weight and airline-fit information
Furniture	“Compact dining tables for apartments where floor space and seating flexibility matter.”	Dimensions, layouts and product options

A useful planning formula: customer situation + real product advantage + relevant next step.

Two example ad drafts, to use only if the claims fit your product:

B2B headline: “Usage-based billing for growing SaaS teams.” Description: “Explore a workflow for subscriptions and metered charges. See the product and request a demo.”

B2C headline: “A dining table that fits your apartment.” Description: “Compare dimensions and seating options before you choose. Explore compact styles for smaller rooms.”

The best use cases to test

These are campaign hypotheses, not promised results.

B2B software evaluation: a buyer compares workflows or requirements. Offer a product tour, calculator, or implementation guide.

B2B category education: someone understands the problem but has not chosen an approach. Offer a useful checklist, webinar, or benchmark with a clear follow-up path.

Switching and alternatives: explain migration, compatibility, support, and pricing honestly on a comparison page.

Professional services: turn a broad problem into a clearly scoped assessment or consultation.

B2C considered purchases: furniture, luggage, hobby equipment, or electronics with meaningful tradeoffs. Answer fit and compatibility questions.

Travel and experiences: match a trip-planning situation with a specific itinerary, package, or booking page.

Retail catalogs: use well-maintained feeds to connect product choices with relevant needs.

Courses and learning products: match a concrete learning goal with the right level, curriculum, time requirement, and outcome.

Less compelling first tests: tiny margins, a weak destination, no conversion tracking, or an audience that must be restricted to specific executives at named companies. Sensitive categories also need policy eligibility; a brand’s eligibility does not make sensitive personal conversations available for targeting.

ChatGPT versus Google, Meta, Reddit and LinkedIn

This is a practical planning comparison. Each platform can serve multiple stages of a buying journey.

Platform	Distinctive signal or environment	Typical formats	Where I would prioritize it
ChatGPT	Conversational needs and context	Sponsored cards, product ads; selected new-format tests	Buyers researching a problem or comparing approaches
Google	Search demand plus broad inventory across Google properties	Search text, Shopping, video, display	Capturing explicit demand; established acquisition programs
Meta	Discovery feeds, engagement and conversion signals	Image, video, carousel; Feed, Stories and Reels placements	Visual discovery, creative testing and consumer demand generation
Reddit	Communities, interests and discussion context	Free-form, image, video, carousel	Niche interests, peer research and detailed education
LinkedIn	Professional profile and company data	Sponsored content, documents, video, lead forms and messaging	Job-function targeting, named-account strategies and B2B pipeline

If you need “finance leaders at these 200 companies,” LinkedIn’s professional targeting is a better starting point than describing that audience in a ChatGPT context hint. If you already capture profitable high-intent searches, keep those campaigns working while you test an additional source of demand.

Google and Meta also use sophisticated AI. The meaningful distinction is where the ad appears, which signals guide it, and whether the resulting customers meet your economics.

There is no reliable universal cost ranking across these platforms. Compare the same outcome, geography, attribution window, and customer quality. A low CPC can hide a poor customer-acquisition cost.

The early case studies: promising, with important differences

B2B: lead generation has worked for some advertisers.

InterTeam’s three B2B SaaS clients: the agency reports more than 150 qualified leads, approximately $5 CPC, and a headline result of 60% lower CPL than Google. The clients are unnamed, and these are agency-reported results, not audited industry benchmarks. They establish a useful hypothesis to test, not a universal discount.

StubGroup’s B2B compliance client: in the same 30-day comparison, reported ChatGPT CPC was $0.76 versus $10.46 on Google; CPL was $92.21 versus $157.57. But ChatGPT’s lead conversion rate was lower, and Google leads had converted to customers more efficiently at the time of reporting. The client’s long sales cycle was still unfolding. This is why cheap leads cannot be your final score.

Windmill Strategy, advertising its own B2B agency: its June 22–July 21 pilot spent $1,470, generated 259 clicks, and produced zero tracked conversions. The agency saw new visitors and learning value, but this was not a demonstrated acquisition win. Firsthand pilot

The strongest B2B lead-success examples above disclose the agency, not the client brand. Do not turn anonymous cases into named-brand success claims.

B2C: named brands now have measurable early signals.

WeightWatchers: DV Rockerbox reported an attributed CPA 15.3% below its blended paid-search benchmark.

Dose: WorkMagic reported statistically significant lift, with 67% of incremental purchases from net-new customers. That is not a claim of “67% lift.”

Portland Leather: Triple Whale reported 93% of visitors from ChatGPT Ads were new. New visitors are not the same as new customers or profitable sales.

Those findings were published in OpenAI’s October 5 update and attributed to its measurement partners. They use different outcomes and methods, so do not combine them into an average platform ROAS.

What changed since launch — and what people are arguing about

February 9: US testing began for eligible Free and Go users. Launch

May 5: OpenAI announced self-service Ads Manager beta, CPC buying, and expanded measurement. The early CPM pilot no longer described the whole product. Update

August 31: the $1 billion annualized milestone and wider self-service access made it a material new channel.

September: Amazon announced a US pilot bringing ChatGPT placements to selected Amazon Ads advertisers. This is another buying route, not evidence that every Amazon advertiser has access.

September 16: Sponsored Agents testing, AI-assisted creative and campaign management, and Shopify/HubSpot integrations broadened the product.

October 5: OpenAI announced visual ads for a later-October test and wider measurement partnerships, including attribution and early incrementality work. Negative Phrases became available to qualifying advertisers; brand-suitability evaluation pilots with DoubleVerify and IAS were also announced.

Three debates matter to marketers:

Trust. Anthropic publicly argued that ads do not belong in Claude and committed to keeping it ad-free. OpenAI’s position is that labeled placements can fund access while answers stay independent. Your creative should be transparent about who is speaking. Anthropic’s position

Measurement. In a LinkedIn post, Cleverly CEO Nicholas Verity reported 57 platform clicks, fewer than 20 analytics visits, and zero conversions. Commenters pointed to tracking differences and the small sample. That is a useful warning to validate measurement; it does not establish fraud or a platform-wide failure. Clicks and analytics sessions are different events.

Speed versus maturity. September reporting from The Wall Street Journal described rapid expansion alongside advertiser reports of administrative and operational problems. Use current documentation; launch-era guides can miss capabilities now available.

Social posts are individual experiences. OpenAI’s success stories are selected examples. Neither is a representative benchmark for your account.

A $1,500 test a lean marketing team can actually run

This is a suggested learning budget, not an OpenAI requirement or a promise of statistical significance.

Before launch

Pick one offer and one primary market.

Define the outcome: purchase, activated trial, qualified lead, or sales opportunity.

Write your allowable CPA or cost per qualified opportunity using your own margins and close rates.

Choose the attribution window and give conversions time to mature.

Save a comparable Google, Meta, Reddit or LinkedIn baseline.

Campaign structure

1 campaign.

3 ad groups around distinct customer situations.

3 genuinely different ads per group.

A relevant destination for each situation; reuse a page when it genuinely fits.

Up to $1,500 over roughly 30 days, using the budget controls available in your account.

If actual CPC happened to average $4, $1,500 would buy roughly 375 clicks. At a hypothetical 3% conversion rate, that implies about 11 conversions. Those are planning assumptions, not forecasts. Eleven conversions can reveal obvious problems, but usually cannot settle a close comparison between channels.

Days 1–3: validate tracking, approval, destinations, spend and lead routing. Fix operational failures immediately.

Days 4–14: inspect which situations attract useful traffic. Check actual leads or orders, not only the platform dashboard. Change one major variable at a time where practical.

Days 15–30: put more of the remaining budget behind the strongest supported hypothesis. Keep a change log and preserve a comparable baseline.

After the attribution window: decide whether to expand, revise, or pause. For B2B, keep following the cohort through your sales cycle before declaring a customer-acquisition winner.

Decision	What must be true
Expand cautiously	Mature outcomes meet your allowable acquisition cost and quality threshold; tracking is reliable
Revise the offer or page	Relevant visitors arrive, but the next step or qualification is weak
Pause	Tracking fails, or the agreed spending cap is reached without enough acceptable outcomes to justify more spend

An inconclusive test is a valid result. Never spend past the cap simply to rescue the story.

For a smaller budget, $500 over about 20 days at a $25 average daily budget can check delivery and measurement. Treat it as a feasibility test, not a decisive channel verdict.

Measurement that keeps the experiment honest

Use three views of performance:

1. Ads Manager: spend, delivery, clicks, attributed conversions.

2. Your analytics and records: landing-page activity, leads, orders, CRM stages and revenue.

3. Incrementality where feasible: did advertising cause additional outcomes, using a suitable holdout or experiment?

Add a consistent URL convention, for example:

?utm_source=chatgpt_ads&utm_medium=cpc&utm_campaign=first_test&utm_content=usecase_a

Preserve OpenAI’s oppref click reference through redirects. If Pixel and Conversions API send the same conversion, use the same event ID for deduplication. Obtain required consent and send permitted data only.

Check attribution windows, view-through reporting where available, modeled conversions, time zones, refunds, and duplicate events before comparing totals.

For B2B, track qualified-lead rate, opportunity creation, pipeline and eventual customer acquisition. For B2C, track new-customer acquisition cost, contribution after returns, and repeat purchase value you can substantiate.

Pro tips people miss

1. Write for a buying situation. “A finance team adding usage pricing” gives you a sharper offer than “business decision-makers.”

2. Make the landing page answer the next question. Specifications, fit, integrations, migration and price often matter more than another slogan.

3. Make ad variations meaningfully different. Test distinct value propositions before making minor wording changes.

4. Use real targeting controls. Context hints cannot enforce “exclude students” or “only show this in Virginia.”

5. Keep paid and organic ChatGPT traffic separate. Otherwise organic referrals can make your paid test look better than it is.

6. Review AI-created copy. Especially pricing, guarantees, translations, eligibility, and claims your team cannot support.

7. Check crawler access. OpenAI’s guidance says landing pages must be reachable by its relevant crawlers; a blocked page can undermine setup.

8. Check budget pacing before increasing spend. A daily average is not a hard daily ceiling, and reporting may lag.

9. Optimize for useful signal. A frequent but meaningless event can train toward the wrong behavior; a rare sale may provide too little early feedback.

10. Keep the experiment concentrated. A small budget spread across many countries, offers and objectives can leave every result inconclusive.

My recommendation

Test ChatGPT Ads if customers research your category conversationally, you have a useful offer, and you can measure downstream quality. The opportunity is access to people explaining a need while they consider what to do next.

Use Google when capturing explicit demand is the immediate priority. Use Meta or Reddit when discovery, visual storytelling, or community context is the stronger route. Use LinkedIn when professional identity and named-account targeting are essential.

Start today by choosing one buyer problem, one landing page, and one allowable acquisition cost. Those three decisions will make your first campaign more useful than another week of reading launch hype.

If you’ve tested ChatGPT Ads, what did you sell, what did you spend, and what happened after the click? Share the country, outcome and measurement window so the comparison is useful.

Founders give up board seats, protective provisions, and veto rights to institutional investors each round, and these stack, so control erodes faster than equity dilution alone suggests. Retail and community capital rounds typically come with none of these by default, since checks are distributed across thousands of investors rather than concentrated in a few, giving founders a way to raise without automatically giving up control.

Every round, founders talk about dilution, what percentage of the company they're giving up. Fewer talk about the second thing every round takes: control. Board seats, protective provisions, and veto rights get negotiated into nearly every institutional round, and they compound. By a Series C, a founder can hold a minority of the board while still holding a majority of the equity. This is what founders are actually agreeing to each round, beyond the cap table math, and there's a way to raise that doesn't require giving any of it up.

How board seats get allocated round by round

A typical venture-backed board starts small and shifts fast. At the seed stage, it's often just the founders, maybe with one investor observer. By Series A, a lead investor typically takes a seat as a condition of the round. By Series B and C, it's common for the board to include two or three investor-appointed seats alongside the founders, plus one or more independent directors both sides have to agree on.‍

The mechanism is straightforward: each round's term sheet specifies board composition as a condition of closing, and once a seat is granted, it usually stays until a specific trigger, an IPO, an acquisition, or a later round, removes it. Founders rarely negotiate this line item as hard as they negotiate valuation, because it feels less urgent in the moment. It compounds the most.

Protective provisions and veto rights, explained

Protective provisions are a list of company actions that require investor approval, regardless of what the board or common shareholders want. Standard ones include raising future capital, selling the company, changing the size of the board, amending the charter, and issuing new stock. Veto rights are the enforcement mechanism, a single investor class, sometimes a single investor, can block any of these actions even if every other shareholder disagrees.

These provisions exist to protect investor downside, and in isolation, many are reasonable. The problem is they stack. Each new round can add its own set of protective provisions, and by the time a company has raised three or four rounds, the list of actions a founder cannot take unilaterally has grown far beyond what any single round's term sheet suggested it would.

Dual-class share structures

Dual-class structures are one of the few tools founders have to keep voting power disproportionate to their equity stake. Founders hold a class of stock with outsized voting rights, often 10-to-1 or higher, while investors and later employees hold standard shares. This is how founders at companies with household names have kept effective control of the board and major votes well after their own equity stake dropped below 50%.

It's not available to everyone. Dual-class structures require setting them up early, before later investors have leverage to reject the idea, and they carry their own tradeoffs, some institutional investors price dual-class stock at a discount, or avoid it entirely, because it limits their influence.

Case study: Pacaso

Pacaso took a different approach to the same problem. The company raised $200M in venture capital in its early rounds, standard institutional financing with standard board and governance terms attached. But instead of continuing to concentrate control with each subsequent round, Pacaso opened its cap table to 17,500 retail investors, raising $72.5M through Regulation A+. None of those 17,500 investors received a board seat, a veto right, or a protective provision. The round diversified the cap table without concentrating any new control in a single party, retail or institutional.

Pacaso's CEO has described the decision as deliberate, not a fallback after struggling to raise institutional capital. The company still had access to VC. It chose to add a capital source that came with capital and no governance strings attached.

How retail and community capital differs

This is the structural difference that makes Pacaso's approach repeatable. Institutional rounds are built around concentrated checks, a handful of investors writing large amounts, which is exactly why each one negotiates hard for board seats and veto rights, their exposure per check is high. Retail and community capital rounds are built around distributed checks, thousands of investors writing smaller amounts, and no single one of them has enough at stake to negotiate for control. Most retail and community offerings come with no voting rights, no board seat, and no veto rights by default, not because founders negotiated them away, but because the structure never puts them on the table.

This gives founders a choice they don't have with an all-institutional cap table: what to give up, if anything, and to whom. Smaller retail investors are consistently more flexible on terms than institutional investors are, which is why founders across categories are increasingly running retail and institutional rounds side by side rather than choosing one exclusively. The mechanics behind structuring one of these raises are covered in DealMaker's guide to Reg CF and the broader equity crowdfunding landscape for founders sizing up which regulation fits their raise.

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Control as a founder decision, not a fundraising afterthought

Board seats, protective provisions, and dual-class structures aren't things that happen to a founder, they're negotiated, round by round, and most founders spend more energy on valuation than on any of them. Retail capital doesn't have to be the option founders reach for only after VC terms get uncomfortable, it's a way to raise a portion of every round on terms that were never going to include a board seat in the first place.

If you're weighing how much of your next round to raise institutionally versus from your own community, DealMaker's overview of Reg CF, Reg A, and Reg D is the fastest way to see which mechanism fits your stage, and the full guide to raising capital online covers what building one of these raises looks like end to end.

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Frequently Asked Questions

What does "board control" mean for a founder raising capital?

Board control refers to how much say a founder retains over company decisions after investors take board seats, veto rights, and protective provisions in exchange for capital. A founder can hold a majority of the equity and still be outvoted on the board, since board seats are negotiated separately from ownership percentage.

How do board seats typically get allocated in a VC round?

Board seats are usually granted as a condition of a round's term sheet. A lead investor typically takes a seat starting at Series A, and by Series B or C it's common for two or three investor-appointed seats to sit alongside the founders. Once granted, a seat usually stays until a specific trigger, like an IPO or acquisition, removes it.

What are protective provisions and veto rights?

Protective provisions are a list of company actions, like raising future capital or selling the company, that require investor approval regardless of what the board or common shareholders want. Veto rights are the enforcement mechanism, letting an investor class block those actions even if every other shareholder disagrees. They stack across rounds.

Do dual-class share structures protect founder control?

Yes, when set up early. Dual-class structures give founders a class of stock with outsized voting rights, often 10-to-1 or higher, so they can retain effective control of major votes even after their own equity stake drops below 50%. They need to be established before later investors have leverage to reject the idea.

Does retail or community capital come with a board seat?

Typically no. Retail and community rounds are built on distributed checks from many investors rather than a few large ones, so no single investor has enough at stake to negotiate for a board seat, veto right, or protective provision. Most retail offerings simply don't put governance rights on the table by default.

How did Pacaso maintain control while raising from thousands of investors?

After raising $200M in venture capital, Pacaso raised an additional $72.5M from 17,500 retail investors through Regulation A+. None of those investors received a board seat, veto right, or protective provision, which diversified the company's cap table without concentrating any new control in a single party.

Your next investors may already be your customers

TLDR: If your company has a community that buys, subscribes, shows up, and tells friends, equity crowdfunding deserves a serious place in your 2026 funding strategy. DealMaker reports more than $2 billion raised across over 900 offerings. Public examples include Pacaso's $72.5 million retail raise and EnergyX's $75 million round. The opportunity is bigger than collecting checks: customers can become owners and advocates.

A CEO can have an excellent product, a committed team, and customers who love the business and still run out of money before the next chapter gets built.

Securing the resources to reach that chapter is part of the job.

Yet many founders still treat fundraising as a narrow ritual: build a deck, find introductions, pitch funds, wait, repeat.

Meanwhile, thousands of people already understand the product because they use it. They read the newsletter. They fill the stadium. They buy the coffee. They recommend the software.

Those relationships deserve a place in the capital strategy.

That is why community fundraising through DealMaker deserves attention in 2026.

Capital has become concentrated

The headline numbers can make fundraising look easy. The experience of an individual founder can be very different.

The PitchBook–NVCA Venture Monitor describes the first half of 2026 as a record period for U.S. venture investment, with capital heavily concentrated in AI companies and very large rounds. An expanding market can still leave plenty of capable businesses competing for a smaller share of investor attention.

For a consumer brand, media business, sports organization, or specialized technology company, the practical question is whether your funding plan depends too heavily on investors pursuing a different opportunity.

CEOs should develop more than one credible route to capital. Your customers may understand your opportunity before a fund puts your category on its agenda.

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What crowdfunding means here

This guide concerns investment crowdfunding: offering securities to investors, potentially including customers and fans. It is different from donations or taking advance orders for a product.

The legal route matters:

Regulation Crowdfunding, or Reg CF: eligible companies can raise up to $5 million in a 12-month period through a registered intermediary. Non-accredited investors face investment limits.

Regulation A Tier 2: eligible issuers can raise up to $75 million in a 12-month period. It requires an SEC-qualified offering statement, audited financial statements, and ongoing reporting. Investor limits can apply.

Regulation D Rule 506(c): permits broad solicitation and has no offering cap, but purchasers must be accredited and their status verified. It cannot open ownership to every ordinary fan.

For a company considering a $70 million public community raise, Regulation A Tier 2 is the relevant starting point.

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Why customers becoming owners matters

Ownership adds a new dimension to an existing relationship. Someone who likes your product can also participate in the company's potential economic success.

That creates several useful possibilities:

A broader investor base. You can reach eligible individuals beyond a small group of institutional decision makers.

More influence over the proposed terms. Management can design the offering, valuation, and share rights, subject to approvals and investor demand.

Capital without scheduled loan repayments. An ordinary equity raise does not create the same monthly principal and interest burden as debt.

A stronger advocacy program. Shareholders may introduce customers, share company news, and contribute useful feedback.

Brand visibility alongside fundraising. Explaining the investment case also introduces people to the business.

A relationship that can outlast one round. An informed investor community may participate in future offerings.

These benefits need deliberate execution. Equity still dilutes existing ownership. Voting rights depend on the securities. Investors can reject an unrealistic valuation. Loyalty grows when management communicates honestly and delivers a worthwhile customer experience.

Track repeat purchases, referrals, retention, and participation. Compare investor customers with similar non-investor customers before claiming that ownership caused an improvement.

How the funding options compare

Funding route	Especially useful when	Main tradeoff
Community equity	An engaged audience understands the business and growth plan	Dilution, campaign costs, disclosure, and investor servicing
Venture capital	Rapid growth benefits from substantial checks, specialist advice, and networks	Negotiated ownership, governance rights, and exit expectations
Private equity	An established business needs expansion capital, a buyout, or ownership transition	Significant ownership and often control negotiations
Bank or other debt	Cash flow can reliably support repayments	Interest, repayment obligations, and potentially security or guarantees
Angels or strategic investors	Specific expertise, relationships, or distribution access matter	Smaller pools of prospects or strategic restrictions
Retained earnings or grants	Internal cash or an eligible program can fund the next milestone	Growth pace, program restrictions, or uncertain availability

This is a decision framework, not a ranking. Compare the actual terms available to your company. A profitable business funding equipment may find debt attractive. A company funding uncertain product development may prefer equity.

Community capital can also sit beside institutional funding. A strong financing plan can combine sources that serve different purposes.

Why consider DealMaker specifically

DealMaker's public materials report more than $2 billion raised across over 900 offerings. That is platform scale.

Its proposition is to bring the fundraising process into an experience built around your company: branded offering pages, digital investment processing, investor management, and communications. DealMaker also offers campaign marketing services, including creative, paid acquisition, and investor engagement.

Dealmaker emphasizes ownership of your investor list and will not market competing offerings to that community.

DealMaker's integrated services include compliance support, broker-dealer and transfer-agent services. Confirm which entities, services, and fees apply to your engagement. The issuer still owns the accuracy of its disclosures and its ongoing obligations; securities counsel and accountants remain essential.

Start with the quality of your audience

If you have 30,000 or more customers, subscribers, or fans, community funding deserves a feasibility review. Treat that figure as a planning starting point, not a legal threshold or a guarantee of DealMaker acceptance.

Count people you can actually reach. Remove duplicate records, inactive subscribers, and inflated follower numbers. Separate repeat buyers from casual followers. Understand geography, engagement, and whether people have expressed interest in the company's future.

Then model the outcome.

For illustration, 30,000 reachable supporters multiplied by a 2% investor conversion rate produces 600 investors. At an assumed $2,500 average investment, that is $1.5 million gross.

Those are assumptions, not benchmarks. Replace them with evidence from your campaign.

A $70 million target at an assumed $3,500 average investment requires 20,000 funded investments. Large campaigns usually need much broader distribution, larger checks, or both. A follower count is an input to the model; it is not the result.

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Six public examples worth studying

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Pacaso shows the scale of retail capital. In October 2025, Pacaso announced a Regulation A growth round exceeding $72.5 million from more than 17,500 individual investors. This was a company that already had institutional backing. DealMaker's case study describes dedicated investor relations and outreach to existing customers. The lesson: give everyday investors a business case they can evaluate, and give their questions serious attention.

EnergyX shows how a technical story can reach a broad audience. Its October 2024 announcement confirmed a $75 million Regulation A round powered by DealMaker. Community capital joined funding from strategic institutional investors. DealMaker later reported that webinars generated more than $4.3 million in investments. The lesson: explain the commercial opportunity clearly, then provide deeper technical and financial answers for people who want them.

The Oakland Ballers show what local identity can do. DealMaker reports $3.2 million raised across two community rounds from more than 3,800 fan investors. The team's initial $1.235 million cap was reached in under nine days. Fan governance rights helped make ownership tangible. The lesson: a specific community purpose can create a compelling proposition. Each team's league rules and proposed ownership structure still matter.

The Flyover shows the value of reader trust. DealMaker's March 2026 case study reports a completed $5 million Reg CF raise from 3,708 investors in about six months. Almost all were already readers. The lesson: a publication with a direct, recurring relationship can introduce an investment opportunity through a channel its audience already uses. Six months also makes clear that a strong community campaign still requires sustained work.

Green Coffee Company shows the customer connection. DealMaker's May 2026 case study reports a $5 million Reg CF raise from more than 2,000 retail investors. It also describes thousands of ecommerce orders following shareholder discount codes. The lesson: plan the customer relationship after the raise. This is a reported example of investor engagement, not proof of a universal loyalty increase.

Death & Co shows that the model extends into hospitality. DealMaker reports that parent company Gin & Luck raised more than $9.3 million through community raises involving 2,764 investors. That is a cumulative result, not one Reg CF offering above the legal ceiling. The lesson: a distinctive experience and a trusted brand can give customers a reason to consider becoming owners.

These are fundraising examples, not evidence of investor returns or typical campaign results. Their common thread is understandable value, a credible story, and a community that has a reason to listen.

A practical playbook for a serious raise

1 Fund a defined milestone

Explain what the money changes. More production capacity? A new market? A product launch? A measurable path toward profitability?

Prepare a minimum, target, and stretch operating plan. Show what happens if you raise less than hoped. Investors should understand how the business moves forward at each level.

2 Work backward from net proceeds

Your operating budget needs the money left after fundraising expenses. Include legal work, accounting, platform and intermediary charges, payment costs, marketing, investor support, and ongoing reporting.

Ask how fees change with volume and which costs arrive before any money is raised. Reserve enough runway to prepare and run the campaign without forcing a rushed launch.

3 Get the company ready for scrutiny

Clean up the capitalization table, financial records, material contracts, intellectual-property ownership, and outstanding obligations. Confirm approvals and the securities you can issue.

The SEC's readiness guidance emphasizes preparing the company and its records before pitching investors. A campaign will expose gaps that friendly customer conversations never did. Resolve them early.

4 Test interest through the correct process

Where permitted, use a compliant testing-the-waters campaign to learn what people understand, what concerns them, and whether they might invest. Required legends and filing rules apply. Indications of interest are nonbinding and are not cash raised.

Segment the results. Existing customers, industry enthusiasts, and new prospects may need different explanations.

5 Build a clear investment story

Answer five questions in plain English: What does the business do? What evidence supports demand? How does it make money? What will the capital accomplish? What could go wrong?

Distinguish current results from forecasts. Explain the security, valuation, dilution, voting rights, and potential liquidity limitations. People can believe in a mission and still need to understand the economics.

6 Make participation straightforward

Test the entire journey on a phone. Check readability, page speed, document access, identity checks, payments, and support. Use accessible contrast and type sizes.

Track where qualified prospects stop. Improve instructions and resolve technical failures. A polished homepage cannot compensate for a confusing investment process.

7 Put the CEO in the conversation

Record a concise founder explanation. Schedule live question sessions, product demonstrations, and operating updates using formats appropriate to the offering rules.

Create an approved answer bank for recurring questions. Give difficult questions the same attention as enthusiastic ones. EnergyX's webinar experience is a reason to test substantive education as part of the campaign.

8 Measure funded capital by source

Follow the journey from outreach to completed, accepted, funded investments. Separate nonbinding interest, pending payments, and settled funds.

Review conversion, average funded investment, acquisition costs, cancellations, and net proceeds by channel. Email opens and social impressions can suggest interest; they cannot pay for the next hire.

9 Scale advertising when the economics support it

Begin with an authorized audience and a defined test budget. Expand channels that produce acceptable funded results. Refresh creative when performance declines.

Give each campaign a specific job: explain the business, answer an objection, invite a question session, or support a permitted follow-up. Confirm offering-specific advertising and promoter rules with the team responsible for compliance.

10 Use AI to improve the work you can verify

In 2026, AI can help turn approved material into draft FAQs, summarize recurring questions, suggest creative variations, and organize campaign feedback.

Keep humans responsible for financial claims, legal language, testimonials, and public replies. Maintain one approved source for facts and numbers. Fast content production is useful only if the resulting message stays accurate.

11 Treat the close as the start of investor relations

Welcome investors. Explain how updates, support, reporting, and any voting work. Set a dependable communication rhythm and keep required filings current.

Share progress against the funded milestones, including delays. Offer appropriate product experiences and opportunities for feedback. The next round will be influenced by how you treated people after the previous one.

What a seventy million dollar plan requires

Large targets need a detailed distribution plan. Identify how much demand might come from existing customers, prior investors, partnerships, earned media, and paid acquisition. Give every estimate an owner and an evidence level.

Build conservative, base, and ambitious scenarios. Change conversion rates, average investment, acquisition costs, and the time required for money to arrive. A plan that only works under the ambitious scenario needs more preparation.

For example, if you assume 20,000 investments averaging $3,500, reducing the average to $2,500 lowers gross proceeds from $70 million to $50 million. That $20 million difference should change the operating plan before it becomes a surprise.

Budget investor service for the volume you hope to attract. Thousands of investors generate questions, payment issues, document requests, and address changes. Decide who handles them and how quickly. Confirm that marketing capacity, finance operations, and support can expand together.

Also distinguish money raised for the business from any permitted sales by existing holders. The headline offering size and cash available for company growth can differ. Have counsel and finance map the structure before using the headline as a spending budget.

Keep the valuation connected to the business

An attractive headline valuation can make a financing look successful while creating difficult expectations for the next round. Explain the reasoning, compare plausible outcomes, and model dilution across future funding needs.

Show investors what they actually receive. A common share, preferred share, convertible instrument, and debt security can provide different economics and rights. A shareholder perk does not replace that explanation.

Use a simple, clearly labeled hypothetical ownership example with your advisers. Keep the assumptions consistent with the documents. The objective is a community that understands the deal and can follow the company's progress with realistic expectations.

Build your first month around evidence

In week one, audit the reachable audience, agree on the milestone, and inventory the financial and legal work required.

In week two, meet DealMaker, counsel, and your finance lead. Establish the likely offering route, responsibilities, preparation budget, and realistic schedule.

In week three, develop the investment narrative, collect supporting evidence, and prepare the investor journey and measurement plan.

In week four, review readiness and any permitted audience research. Decide whether to proceed, strengthen the business case, or pursue another funding source.

This is a preparation framework. A Regulation A launch depends on qualification and other requirements; a four-week calendar cannot promise regulatory readiness.

Give your community a place in the funding plan

The best reason to explore crowdfunding is that you have built something people value and have a credible plan to make it more valuable.

CEOs owe their companies a serious search for the resources that make future progress possible. If your customers already supply attention, purchases, feedback, and referrals, explore whether some also want an opportunity to invest.

Start with an audience audit and a funding model. Bring both to a conversation with DealMaker.

The main alternatives to venture capital are bootstrapping, revenue-based financing, venture debt, grants, and retail or community capital raised under Reg A+, Reg CF, or Reg D. Each trades off differently on cost, speed, and how much control a founder keeps. The right one depends on your revenue, your stage, and how much of your cap table you want to open to your own customers instead of institutions.

Wall Street just decided that access, not just returns, is the pitch worth making. In July 2026, Goldman Sachs consolidated its alternatives business into a single private markets platform, giving wealthy clients direct pre-IPO stakes in companies like SpaceX, Stripe, and Canva before they go public. Kristin Olson, Goldman's global head of alternatives for wealth, put it plainly: there's "a lot of focus on the big growth tech names and getting clients access to those before they debut in the public markets." If even Goldman Sachs is now selling access to private markets as the product, the question every founder should be asking isn't just "how do I raise money without VC," it's "why is access to growth-stage companies suddenly the thing everyone wants, and who gets to have it."‍

Right now, the answer is: whoever qualifies as an accredited investor. Goldman's platform, like most private markets access, is restricted to accredited investors, qualified purchasers, and family offices. That threshold was set in 1982 under Regulation D, at $1 million in net worth or $200,000 in individual income, and it has never been adjusted for inflation. The SEC's own 2023 staff report found that just 1.81% of households qualified in 1982, versus 18.5% by 2022, a 16-fold increase driven almost entirely by inflation eroding the original number rather than by any change in the rule itself. Adjusted for inflation, that $1 million threshold would be closer to $3 million today. Investors including Brad Gerstner have made this critique publicly on shows like All-In: the rule gatekeeping access to private markets is a decades-old net worth test that was never designed for today's dollars, and it decides who gets to invest in the next SpaceX and who doesn't.

Founders should care about this for a different reason than investors do. If access to capital increasingly depends on who counts as "accredited," then non-dilutive and less-dilutive alternatives to venture capital aren't just a fallback for companies that couldn't raise VC, they're one of the only paths that lets founders raise from people who aren't wealthy enough to qualify as accredited investors in the first place. That's the gap this guide is built to close.

Comparing every alternative to venture capital

Founders usually hear about these options one at a time, from different sources, with no way to compare them side by side. Here's all five in one place.

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Option	How it works	Cost or dilution	Speed	Best for
Bootstrapping	Fund growth from revenue and personal capital	None, no equity or debt given up	Immediate, but capped by cash flow	Capital-efficient businesses that are cash-flow positive early, bootstrapped companies have outperformed VC-backed peers on several efficiency measures
Revenue-based financing	A lender advances capital against a share of future revenue until repaid, typically 1.3 to 3x the amount advanced	Non-dilutive, but repayment cost is higher than a traditional loan	Weeks	Companies with predictable, recurring revenue that want to avoid giving up equity
Venture debt	A loan, often paired with warrants, layered on top of an existing equity round	Minimal dilution, just the warrant coverage	Weeks to a couple months	VC-backed companies extending runway between equity rounds without a full new raise
Grants	Non-repayable funding from government programs or foundations, e.g. SBIR/STTR	Non-dilutive, but highly competitive and restricted to eligible uses	Months, application and review cycles are slow	R&D-heavy, deep-tech, or mission-aligned startups that fit a specific program's criteria
Retail capital (Reg A+/CF/D)	Raise directly from your own customers and community under an SEC exemption	Dilutive like any equity round, but terms and control differ, most retail rounds come with no board seat or veto rights by default	Weeks to a few months for a structured campaign	Founders with an engaged customer base who want capital and marketing in the same raise

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The retail capital row covers three separate regulations, and the differences matter. Reg CF caps a raise at $5 million in any 12-month period and is open to non-accredited investors. Reg A+ allows up to $20 million a year under Tier 1 or $75 million under Tier 2, also open to non-accredited investors, which is what makes it the closest thing to Goldman's platform that doesn't require being wealthy enough to qualify as accredited. Reg D has no offering cap at all, but 506(c) restricts the raise to accredited investors only, the same gate Goldman's platform sits behind. DealMaker's overview of all three is the fastest way to see which one fits a given raise size and investor base.

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Why this matters now

Private markets access is opening up, but not evenly. Venture capital itself has kept flowing, just not to every category of company, and even companies that do raise VC successfully are finding follow-on capital harder to secure in categories VCs have rotated away from. At the same time, the pool of capital sitting in retail investors' hands dwarfs the entire venture market, and institutional gatekeeping that used to wall off growth-stage access is cracking at the top of the market and starting to crack in the middle of it too. Goldman built infrastructure to give its wealthiest clients a way in. Equity crowdfunding under Reg A+, Reg CF, and Reg D is the infrastructure that already gives everyone else a way in, non-accredited investors included, and DealMaker has been running that infrastructure long enough to know its mechanics cold.

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How to choose the right alternative for your stage and category

None of these five options are mutually exclusive, and treating them that way is usually the mistake. Diversifying the capital stack, bootstrapping to extend runway, layering in venture debt between equity rounds, and opening part of a raise to retail capital, gives a company benefits none of those sources provide on their own: cost efficiency, speed, and a customer base that's also a cap table.

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A few starting points. If you're cash-flow positive or close to it, bootstrap as long as you can before giving up anything. If you have predictable recurring revenue and want to avoid dilution entirely, revenue-based financing or venture debt are worth pricing out before an equity round. If your product fits a specific government or foundation program, grants are free money, but budget months for the process, not weeks. If you have an engaged customer base and want capital that also builds distribution, retail capital is worth structuring in as part of the round, not a fallback if VC falls through. Pacaso is the clearest example on the record: the company raised $200M from VCs, then deliberately raised another $72.5M from 17,500 retail investors rather than going back to VCs for more.

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The founders who avoid a down round later, and who keep the board seats and control decisions covered elsewhere in this series, are usually the ones who built more than one of these five into their capital stack before they needed to. DealMaker's full guide to raising capital online covers what building that stack looks like end to end.

President Trump convened an unprecedented gathering of top artificial intelligence executives—including Elon Musk, Mark Zuckerberg, Jensen Huang, Sundar Pichai, Satya Nadella, Dario Amodei, and OpenAI co-founder Greg Brockman (representing OpenAI while Sam Altman managed product launches) - culminating in the signing of the White House Accord on Super Intelligence.

Signed by six leading frontier model developers, the accord fundamentally shifts governance away from voluntary self-policing and slow multinational treaties toward enforceable corporate mechanics: Corporate Responsibility, Internal Controls, External Audits (powered by third-party audit infrastructure), Board Oversight, and Federal Enforcement.

Crucially, the agreement links external audit reports directly to Board Fiduciary Duty and Directors & Officers (D&O) Insurance Cancellation Risk, giving public commitments structural legal teeth. Beyond corporate governance, the summit established that open-weight models across 197 sovereign nations render centralized software bans impossible, triggering a mandatory AI Cyber Defense Arms Race, an industrial-scale Data Center Power Infrastructure Buildout, and potential top-down federal GPU Compute Allocation for critical national sectors.

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The White House Accord: Core Governance and Legal Mechanics

Voluntary corporate pledges in tech governance have historically served as PR gestures lacking regulatory teeth. The White House Accord on Super Intelligence structurally alters this dynamic by translating voluntary executive promises directly into actionable corporate governance law. Rather than attempting to pass multi-year statutory legislation or establish a bloated administrative agency, the framework weaponizes existing corporate law, fiduciary obligations, and board liability to enforce immediate operational compliance across frontier model developers.

Core Governance Tiers and Enforcement Mechanisms

Governance Tier	Core Provision & Functional Focus	Primary Enforcement Mechanism
Corporate Responsibility	Frontier developers explicitly accept direct corporate accountability for model safety, rejecting deflection to international bodies (UN) or anthropomorphic evasions.	Signed commitment under the White House Accord on Super Intelligence.
Internal Controls	Developers must build internal validation teams and mandate internal safety controls mapped to operational procedures.	Verification teams conducting real-time operational risk validation.
External Audits	Mandatory third-party technical verification of internal controls, risk mapping, and deployment safety artifacts.	Independent professional audit/accounting firms (e.g., Ernst & Young using 8090 infrastructure).
Board Oversight	Mandatory establishment of an independent board committee dedicated strictly to receiving and evaluating third-party AI safety audit reports.	Board Fiduciary Duty and Directors & Officers (D&O) Insurance Risk (ignoring audit findings exposes directors to personal liability).
Federal Enforcement	Federal regulatory oversight monitoring public safety commitments and corporate disclosures made by frontier developers.	Existing statutory authority of the FTC and SEC to prosecute fraudulent or unfulfilled public commitments.

Technical Execution: The 3-Pillar Audit Framework

To operationalize external audits without stalling development, infrastructure providers like 8090 and Ernst & Young (EY) are deploying a specialized three-pillar audit framework designed for super intelligence deployments:

1. End-to-End Traceability: Implementing real-time operational monitoring across model deployments so enterprise boards and auditors maintain complete visibility into model behavior and system interactions.
2. Policy-to-Risk SOP Mapping: Directly mapping frontier model deployments onto a corporation's existing Standard Operating Procedures (SOPs) and institutional risk management frameworks.
3. Auditable Evidence Generation: Generating immutable, verifiable compliance artifacts that can be presented immediately to downstream enterprise customers, insurers, regulators, or courts during legal discovery.

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Statutory Bypass and National Competitiveness

By establishing governance through corporate board mechanics, the administration intentionally bypassed slow legislative channels and multinational organizations like the United Nations. Federal legislation faces multi-year gridlock, while international treaties are functionally useless against non-compliant foreign adversaries like China, who would exploit unilateral Western pauses.

Creating a restrictive administrative regulatory body—what industry leaders term a "DMV for models"—would hobble domestic innovation and yield technological supremacy to foreign rivals. The White House Accord allows American developers to push model capabilities at maximum speed while locking them into immediate, board-level accountability.

The "So What?" Layer: Fiduciary Duty and D&O Insurance Teeth

The true enforcement power of the White House Accord lies in its legal linkage to corporate insurance and governance law. By mandating that independent external audit reports be delivered directly to a dedicated board committee, the accord triggers an inescapable sequence of corporate liability:

1. Formal Notice: Once an external auditor (such as EY) delivers a safety audit report to the independent board committee, board members are legally on notice regarding identified risks.
2. Fiduciary Obligation: Board members cannot ignore or override external audit findings without breaching their fundamental fiduciary duty to the corporation and its shareholders.
3. D&O Insurance Policy Cancellation: Corporate insurance underwriters for Directors & Officers (D&O) liability mandate compliance with independent audit recommendations. If a board disregards an external audit report on super intelligence safety, underwriters will cancel the company's D&O coverage—leaving individual board members personally liable for catastrophic down-funnel harms.

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This structural mechanism transforms a voluntary presidential accord into an enforceable corporate governance regime overnight, completely bypassing Congressional action.

Having established the operational mechanics of the accord, the breakdown moves to the closed-door diplomacy and room dynamics between industry rivals at the White House.

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Palace Intrigue: Rival CEOs, Room Dynamics, and Moderation

Gathering Silicon Valley’s fiercest corporate competitors in a single room created an environment charged with intense personal rivalries, active commercial litigation, and talent poaching wars. Navigating these personality conflicts required strategic political moderation to extract a unified commitment to national AI leadership.

Behind-the-Scenes Exchanges and Key Personalities

• Anthropic CEO Dario Amodei & President Trump: Anthropic CEO Dario Amodei—frequently viewed as an industry "conscientious objector" heading a left-leaning organization—had previously skipped White House events. Recognizing his past absence, President Trump personally telephoned Amodei to invite him to a private, one-on-one dinner two nights before the summit. Amodei accepted, attended the dinner, and took a central seat at the East Room summit. During the post-summit press conference, President Trump reached into the back of the executive crowd, pulled Amodei to the front to speak on behalf of the industry, and offered an affectionate pat on the back. When meeting tech analyst David Sacks in person for the first time, Amodei lightheartedly shook hands and joked, "Oh, my nemesis." Sacks later colorfully characterized the internal dynamics of Anthropic's leadership team by describing Amodei as the cerebral "Dustin Hoffman" character in the room, with Anthropic co-founder Tom Brown acting as the hyper-pragmatic "Tom Cruise" character translating his ideas to the world.
• OpenAI Representation (Greg Brockman): While OpenAI CEO Sam Altman was absent from the summit due to major product launches, OpenAI was officially represented at the table by co-founder and President Greg Brockman, ensuring the market leader was fully aligned with the governance accord.
• Nvidia CEO Jensen Huang: Addressing abstract existential fear during discussions on super intelligence risks, Nvidia CEO Jensen Huang re-centered the room by stating: "Alarmism without solutions is unproductive, but alarmism with solutions is helpful." This framing successfully pivoted executive consensus away from passive "doomerism" and toward actionable engineering controls and audit structures.
• Drafting Session in the Roosevelt Room: Core principles were initially outlined during the East Room luncheon by Meta CEO Mark Zuckerberg and Nvidia CEO Jensen Huang, who advocated for internal controls, external audits, and board-level oversight. The executives then adjourned to a breakout session in the Roosevelt Room, where Speaker of the House Mike Johnson read the draft accord text aloud. Following brief word-smithing, all six frontier model developers signed the accord unanimously.
• Presidential Moderation & "Brainiacs": President Trump moderated the summit, opening the session with a stark geopolitical assertion: "Whoever wins super intelligence wins." Prior to formal discussions, he led executives on a tour of the new White House ballroom and East Wing expansion—a five-story-deep, two-story-high underground construction project moving ahead of schedule and under budget. Expressing his view of the gathered technology titans, Trump summarized: "These are brainiacs... getting shit done for America."

​

[East Room Luncheon: Strategic Framing] 
                  │
                  ▼
[Roosevelt Room Breakout: Draft Read by Speaker Johnson]
                  │
                  ▼
[Unanimous Accord Signing by 6 Frontier Developers]

With corporate governance locked in and executive consensus established, the analysis moves to the macro trajectories driving energy, defense, and economic policy.

Macro Trajectory: Cyber Arms Race, Data Centers, and Economic Outlook

The race for super intelligence shifts technological policy into a high-stakes arena of heavy industrial capacity, baseload electron generation, and defense strategy. Competing in frontier AI requires treating digital infrastructure as a vital component of national power.

Open-Source Proliferation vs. Centralized Regulation

Attempts to restrict AI development purely through software bans or centralized licensing regimes run directly into irreversible technical and geopolitical realities:

• Ubiquity of Open-Weight Models: Highly performant open-weight models are released globally on an ~11-day iteration cycle. These models can be downloaded, fine-tuned, and run locally on consumer edge hardware without requiring monitored cloud data center clusters.
• Sovereign Arbitrage: With 197 sovereign nations offering localized power grids and internet connectivity, centralized software bans are fundamentally unenforceable.

Because software distribution cannot be choked off, national security strategy must pivot from software restriction to aggressive AI Cyber Defense. The proliferation of open weights triggers an inescapable cyber arms race: enterprises and federal agencies must exponentially expand cyber defense budgets—utilizing AI-native security platforms like Palo Alto Networks—to defend critical infrastructure against AI-driven threats.

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The Data Center Power Crisis and GPU Compute Allocation

The ultimate bottleneck for AI supremacy is no longer algorithm design, but electron generation and physical data center construction.

• Historical Grid Trajectory: Throughout the 20th century, the US power grid doubled its electrical capacity every decade to feed expanding domestic manufacturing. Following the offshore de-industrialization of the 1970s and early 2000s, US grid capacity flatlined for roughly 25 years.
• The National Security Imperative: Restoring aggressive electrical power generation is an urgent national security imperative comparable to WWII industrial mobilization. In a modern technological conflict, blocking data center construction is the operational equivalent of blocking tank manufacturing facilities during wartime.
• Top-Down GPU Compute Allocation: As data centers evolve into critical utility infrastructure, federal regulators will inevitably oversee compute distribution. Top-down allocation frameworks may soon mandate that explicit percentages of domestic GPU compute be prioritized for critical national assets, such as the financial services network (e.g., JP Morgan) and military defense grids.

Key Takeaway

The White House Super Intelligence Summit marks a decisive turning point in American industrial and technology policy: bypassing slow statutory routes in favor of enforceable corporate board fiduciary duty, accepting the irreversible open-source proliferation of software, and treating power generation and data center construction as vital national security imperatives to win the global AI arms race.

TL;DR: Upload a clear adult portrait to ChatGPT and use the prompt below to request an apparent-age report: an approximate age guess, a broad range, six short observations, and an explanation of what the photo makes difficult to judge.

You can do something as simple as upload a selfie and qsk ChatGPT: “How old do I look?”

Then ask the more interesting question:

“Which visible details led you to that impression and which details are you uncertain about?”

But you can also take it further and get a one page report that explains why you look younger or older than your actual age.

It asks ChatGPT to turn the answer into a one-page portrait report, with your original photo, numbered callouts, and six short assessment cards.

The report should cover:

• Forehead: visible lines and how clearly they can be seen.
• Eyes: lids, under-eyes, expression, and shadows.
• Skin: visible texture, lighting, and image quality.
• Cheeks: volume and folds beside the mouth.
• Jawline: the lower face and anything obscuring it.
• Hair: hair, brows, and facial hair, treated as variable cues.

Each observation should explain how much weight it deserves in that particular photo.

A blurry forehead doesn’t establish smooth skin. A smile can introduce creasing. A beard can hide the jawline. Those details belong in the explanation.

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Here’s how to try it.

Copy this prompt after uploading your photo:

Create a one-page APPARENT-AGE PORTRAIT REPORT using only the adult person's uploaded photograph.

ESTIMATE BEFORE DESIGNING
First make a subjective overall age impression from the original photo, then check it against visible facial details. Ignore any known identity, actual age, filenames, previous estimates, desired outcome or age printed in the reference. Do not look up the person. Do not infer age from clothing, occupation or background.

Give:
- One approximate apparent-age estimate, phrased "about N"
- A broad plausible range
- Qualitative confidence and the main image-quality limitations

Do not imply that this is a validated measurement or that the range is a statistical confidence interval. Avoid decimal ages, numerical confidence percentages, feature scores or invented biometric calculations. If the photo is too unclear for a useful impression, say so and request a better photo instead of inventing precision.

PRESERVE THE ORIGINAL PHOTOGRAPH
Place the actual uploaded photo into the layout. Cropping and background removal are allowed only if the face remains untouched. Preserve skin texture, hair, hairline, facial hair, proportions, expression and head angle. Do not redraw, regenerate, retouch, beautify, sharpen facial detail, age or de-age the face. Do not turn an angled view into a front-facing portrait. If exact preservation is not possible, keep the original photo as a rectangular crop. Do not substitute a generated lookalike.

ASSESS ONLY WHAT IS VISIBLE
Use six short assessment cards:

1. Forehead and visible lines
2. Eyes, lids and under-eyes
3. Skin texture and tone
4. Cheek volume and folds beside the mouth
5. Jawline and lower face
6. Hair, brows and facial hair

For each, describe the visible observation and how much weight it deserves in this photo. Use cautious labels such as younger-leaning impression, mature-leaning impression, mixed or limited visibility. Separate what is visible from your interpretation. Account for smiling, squinting, lighting, shadows, blur, filters, facial hair, makeup, camera angle and close-camera distortion. Do not treat missing detail as proof of smooth skin. Gray hair and hairline shape are variable cues, not age measurements.

Do not infer biological age, health, diagnoses, ethnicity, lifestyle, personality or cosmetic-treatment history. Do not recommend medical procedures or claim that a facial feature proves anything about health.

BOTTOM FINDINGS
Summarize the two strongest younger-leaning cues and the two strongest mature-leaning cues, only when supported. Add two or three optional, low-risk photo or grooming suggestions for a softer presentation. Prioritize lighting, camera distance and framing. Do not promise a specific number of years of change.

DESIGN AND OUTPUT
Use a refined black-on-white editorial design: elegant serif headline, clean sans-serif body text, thin gray rules, generous spacing and restrained monochrome details. Put the estimate, range and confidence across the top; a large original portrait with delicate numbered callouts on the left; six compact rounded cards on the right; and concise findings and suggestions across the bottom.

Keep everything on one portrait-oriented page. Shorten the writing rather than shrinking it into unreadable text. Use a document or layout tool that embeds the original image without regenerating it. Export a one-page PDF and a readable high-resolution PNG if supported. Also give a brief plain-text summary.

Footer: "Subjective apparent-age impression from one photograph. Not verified age or biological age. Lighting, expression and camera perspective can shift the result."

Before delivery, verify that every callout matches its card, the estimate is consistent throughout, no text is clipped and the original face is unchanged.

Try it before reading everyone else’s results: did it guess younger, older, or close—and which observation surprised you most?

TL;DR: ChatGPT Space gives your AI work a place to live: editable Pages, files and shared work you can return to and develop with other people. You can bring ChatGPT or your Dot into a Page, add interactive elements, and use Sites for a hosted website or app. The opportunity is to turn useful conversations into work you keep improving. Below: how it works, five practical starting points, five ambitious builds, and the details that make the difference.

Your best ChatGPT answer shouldn't disappear into a conversation you never open again.

You work through an idea. Get a useful plan. Generate a draft.

Then next week arrives, and you're hunting through chats trying to remember which version mattered.

ChatGPT Space gives that work a home.

OpenAI included Space in its September 29 DevDay announcements, alongside Dots. My take: the interesting shift is that the work can become something you organize, collaborate on and keep developing.

What ChatGPT Space actually is

Space brings together Pages, saved files, images, spreadsheets, presentations and Sites. A Page is a document you can edit directly or work on with ChatGPT. Individual spaces group related Pages around a topic or team.

That could mean a product launch, a course you're studying, a trip, or your next creative project.

Two naming details help: your workspace is the personal or organization environment you're signed into; a space organizes related work within it. OpenAI's admin documentation also calls Space the successor to Library.

How Space relates to Dots and Sites

Here's a practical way to divide the jobs:

Tool	Its job	Example
Space	Keep the material and shared work together	A launch brief and its supporting research
Dot	Take on ongoing responsibilities	Research changes and follow up on assigned work
Site	Deliver a hosted web experience	A demo, calculator, dashboard or browser game

You can mention your Dot directly in a Page. Dots can continue work between conversations, including cloud work while your computer is off. Sites gives you a separate workflow for building and hosting web experiences.

For example, imagine launching a workshop:

• Space: Keep the audience research, curriculum and decisions together.
• Dot: Give it responsibility for researching unanswered questions.
• Site: Build the workshop's website from the approved brief.

That's a workflow you can set up. Saving a Page doesn't automatically create a Dot assignment, a running schedule or a published Site.

How to get started

You need an account with Space enabled.

1. Open Space in ChatGPT.
2. For a group of related Pages, use All → New → Space and name it.
3. Choose New page. Add notes, files or source links.
4. Open ChatGPT alongside the Page and explain the outcome you want.
5. Review and edit the result, then use the sharing controls if you want collaborators.

You can also ask ChatGPT to turn an existing conversation into a Page. Connected sources such as Google Drive require the appropriate app connection and file access. Getting started

Try this as your first request:

Turn these notes into a project brief with a clear goal, current decisions, next actions, source links and unresolved questions. Mark missing information instead of guessing.

Then ask for one focused improvement:

Rewrite the opening for someone seeing this project for the first time. Keep the facts and decisions intact.

Five useful ways to try it

These are starter recipes you can adapt.

1. Build a learning headquarters

Pick something you actually want to understand. Add a few good articles, your notes and a specific learning goal.

Create a learning plan from these sources. Explain the essential concepts, make a glossary and give me practice questions. Separate what the sources establish from your own explanation. Start with a short exercise that exposes what I don't understand.

The useful part: you have a place to capture corrections and build on them.

2. Create a content studio

Put your audience, source material, writing samples and ideas in one place.

Develop this idea into a Reddit post, a LinkedIn post and a 60-second video script. Give each format its own opening. Preserve the evidence and distinguish opinion from fact. Put unsupported claims in a questions list.

Then save the angle you chose and why. Your next draft has a better starting point.

3. Plan a trip people can actually agree on

Bring dates, budget, must-haves and saved options.

Compare these options against our priorities. Draft an itinerary with travel time, a daily budget and a rainy-day alternative. Flag prices or opening hours that need checking. Show which decisions we still need to make.

Share the planning Page with your travel companions and use comments to resolve the tradeoffs.

4. Build a launch room

Collect customer insights, product notes and positioning ideas.

Use these materials to draft the launch story, audience, key objections and a checklist. Separate confirmed commitments from proposals. Suggest owners only where the source material supports them.

Add research and creative work as related Pages as the project grows.

5. Keep a decision notebook

Use it for software selection, hiring priorities or a business idea.

Compare these options using my constraints. Recommend one, explain the tradeoffs, record the assumptions and identify what evidence would change the recommendation.

Future you gets to see the reasoning behind a choice.

Five wild things to build

These are ambitious project ideas using Space with available Dots, Sites and visualizations. They require setup and iteration.

1. A one-person launch studio

Give a single idea a full development environment: customer evidence, an offer, a demo, a launch narrative and a website.

Help me develop this idea into a small launch. Build Pages for customer evidence, the offer, experiments and launch copy. Give my Dot a scoped research assignment. Use Sites to build a demo website from the approved brief. Show me the work at each stage.

The ambition: make the leap from “interesting idea” to something another person can experience.

2. A personal research observatory

Choose a question you want to follow for months: an emerging technology, a scientific field or a changing market.

Create an evidence notebook for this question. Track each claim, its source, date, contradictions and open questions. Help me set up a weekly review that adds material changes and explains whether they affect our conclusions. Confirm the saved schedule and its time zone.

You're designing a record of how your understanding changes.

For fixed recurring work, Dots requires a saved schedule. Supported event monitoring also needs an explicit assignment; connecting a service alone doesn't start it. Tasks and memory

3. A business wind tunnel

Create a place to test an idea before committing real resources. Model pricing, customer demand, costs and different operating choices.

Build an interactive model from these assumptions. Let me adjust price, customer volume and costs. Show what breaks first under pessimistic scenarios. Label simulated results clearly and create a Page explaining the assumptions we should test with real customers.

Use a Page visualization for a compact experiment, or Sites for a fuller tool. The value is seeing which assumptions matter.

4. An entire fictional universe

Build the world bible, geography, factions, characters, timelines and visual style. Then make something playable.

Help me create an original fictional universe. Organize its canon into related Pages, generate character and location concepts, and flag continuity conflicts. Use Sites to prototype a branching browser adventure based on the approved canon.

A shared world you and collaborators can keep expanding could become a game, a story series or a creative playground.

5. A personal university built around your goals

Design a curriculum that ends with work you can show.

Use my goals and these sources to design a course with lessons, practice, assessments and a capstone project. Keep my feedback and revisions in Space. Prototype a learning portal in Sites and specify how progress should be saved.

Imagine a course on storytelling where each week improves your actual screenplay, or a marketing course built around your real launch.

Pro tips that make the work better

• Name the outcome. “Prepare a launch decision” gives better direction than “organize my stuff.”
• Start with a small source set. A few relevant documents beat a pile of unrelated uploads.
• Separate evidence, decisions and drafts. Make it easy to tell what is known, what is agreed and what is still being explored.
• Tell the agent where the answer belongs. Ask for a chat response, a Page edit or a separate deliverable.
• Protect settled details. Tell it to preserve approved names, dates, quotations and commitments.
• Ask for changes, not another full report. “What changed, why does it matter, and what should we reconsider?”
• Define success before delegating. Specify the output, sources, review points and when you need an update.
• Test the result. Click the calculator, open the source and inspect the saved output.

Things people can easily miss

Pages can do more than hold text. The slash menu includes image generation and interactive visualizations. Prompt blocks let readers reuse a request to start a chat. You can also reference existing files, Pages and chats.

You can bring the agent to a specific passage. Select text and use Ask for change, or address ChatGPT or dot inline or in a comment. Be explicit about the section and requested change.

“Keep Updated” isn't available at launch. Writing “update this every morning” in a Page doesn't establish a working schedule. Request recurring work in chat, inspect the saved task, and verify an actual run and resulting update.

Sharing can extend to nested work. Space access applies to its Pages; sharing a Page also includes its child Pages. Removing a direct invitation may leave access inherited from a parent. Choose view, comment or edit access from the options your account offers.

Private sources and shared summaries are different. A linked source keeps its own permissions. Information copied or summarized into the Page becomes visible to that Page's readers. Sharing a Page doesn't expose your entire private chat history or saved memory. Sharing details

A shared Page doesn't give everyone the same tools. Collaborators may have different agents, connections and instructions. Agree on who changes what.

A Site has its own publishing and access settings. You can save a Site version before deploying it. Decide its audience separately; a Page edit isn't automatic website publication.

A note on access

Check the features enabled in your account and workspace. Dots has a separate gradual rollout and eligibility rules; Sites has its own plan limits. Having Space doesn't establish access to every feature described here.

Try this today

Choose one project you've already been putting off.

Create one Page. Add three useful sources. Ask for one concrete result.

Then improve it once.

The result could be a better decision, a draft worth publishing or the first version of something people can use.

Save this for the prompts, or send it to the person you'd want to build with.

TL;DR: These 10 ChatGPT image codes give you a handy way to organize photo edits: realistic detail, new backgrounds, professional headshots, hairstyle previews, outfit changes, and more. Upload a reference photo and use the full prompts below. Treat the codes as custom shorthand you define in the conversation. The instructions attached to each label are what make the request clear.

Your next creative project might already be sitting in your camera roll.

A portrait can become a professional headshot. A haircut preview. A coastal travel scene. An original magazine cover.

I’ve included examples using Amanda on the Thinking Deeply AI team as the reference model throughout, plus prompts you can adapt to your own photos.

To try these: Upload your photo to ChatGPT and paste one prompt below. For portraits, add this instruction:

Create a vertical 4:5 image. Preserve the reference person’s recognizable facial features, age, skin tone, and natural proportions. Change only the elements I request. Do not print the command label in the image.

Here are the 10 recipes.

1. /hdreal — More believable photographic detail Useful when skin looks overly smooth or hair and clothing lack texture./hdreal — Refine this portrait with natural skin texture, individual hair strands, detailed fabric, and realistic light and shadow. Preserve the face, expression, pose, clothing, background, and original color or monochrome treatment. Avoid waxy skin and harsh sharpening. Pro tip: More detailed-looking doesn’t necessarily mean more pixels. Check the downloaded file’s dimensions if you need it for print.
2. /newbg — Change the setting Replace the setting with a sunlit glass conservatory and a cream upholstered bench. Preserve the person’s face, hair, expression, clothing, and seated pose. Match the original light direction, perspective, and contact shadows so the result feels like one photograph. Pro tip: Choose a background whose lighting fits the subject. A softly lit face needs a believable explanation in a scene with harsh sunlight.
3. /proshot — Create a professional headshot Useful for exploring profile photos, speaker portraits, and personal-brand imagery. /proshot - Create an editorial headshot using this person as the identity reference. Use a charcoal blazer, teal blouse, warm-gray studio background, and soft light from camera left. Frame from the chest up with a relaxed, confident expression. Preserve facial features and natural skin texture. Pro tip: Describe the impression you want: approachable founder, creative professional, or formal executive. Give the image a specific job.
4. /together — Bring people into one scene For two specific people, upload a separate reference photo for each. /together - Place the person from image 1 on the left and the person from image 2 on the right, seated together at a bright café table. Preserve both identities. Match their scale, perspective, lighting, and shadows. Keep faces distinct and hands naturally positioned. Pro tip: Specify who goes where. Also describe their interaction: looking at the camera, sharing a laugh, or discussing something on the table.
5. /hairstyles — Preview a different haircut Amanda’s example uses a brunette bob. /hairstyles - Change only the hairstyle to a chin-length brunette bob with a soft side part and realistic volume. Preserve the person’s face, expression, hair color, clothing, pose, and background. Keep the hairline natural and the face unobstructed. Pro tip: Compare one haircut at a time using the same reference, lighting, and framing. That makes the differences easier to judge.
6. /anatomy — Refine hands and body proportions For this guide, I’m defining this label as hands-and-proportions cleanup./anatomy — Refine the hands so they have natural finger anatomy, believable joints, and relaxed wrist alignment. Keep the intended pose and grip on the object. Preserve the face, clothing, background, and natural body proportions. Change only the anatomical errors. Pro tip: Point to the specific problem: “Fix the fingers gripping the cup handle.” Then zoom in and inspect the result.
7. /droneshot — Create an aerial perspective Amanda’s example places her on a coastal terrace, looking up toward the camera./droneshot — Create a low aerial portrait of this person on a pale stone terrace beside turquoise water. Look down from several meters above, with the person looking up and remaining large enough to recognize. Include the whole body, natural proportions, and warm afternoon shadows. Pro tip: Specify camera height and subject size. Otherwise, you may get a beautiful landscape with a tiny person somewhere inside it. A new viewpoint also requires imagined details outside the original photo.
8. /add_object — Insert something into the scene /add_object — Add a small clear-glass vase of blush-pink peonies on the surface beside the person, on the right side of the image. Match scale, light direction, reflections, and contact shadows. Preserve the person, pose, clothing, background, and framing. Pro tip: Describe where the object rests and how it interacts with the surface. Those details help it feel physically present.
9. /magazine_cover — Build an original editorial cover Useful for personal-brand concepts, feature announcements, and creative social posts./magazine_cover — Create an original magazine cover featuring this person in an emerald blazer against a muted terracotta background. Preserve their identity. Add only these words: “AI WITH AMANDA,” “10 IMAGE SHORTCUTS,” and “ONE PHOTO. TEN POSSIBILITIES.” Use clear editorial typography and keep the face unobstructed. Pro tip: Replace the wording with your own. Keep cover lines short and check every letter at thumbnail size.
10. /suit_up — Change the outfit /suit_up — Replace only the clothing with a tailored midnight-navy trouser suit and an ivory blouse. Preserve the person’s face, hair, expression, body proportions, pose, hands, background, and lighting. Make the fabric, seams, folds, and fit look natural. Pro tip: Describe the garment’s cut and material. “Single-breasted navy wool blazer” gives more direction than “make me look successful.”

A few things that make this workflow more useful:

• Define the shorthand. Tell ChatGPT: “In this conversation, use these labels as shorthand for their accompanying instructions.” Include the definitions again when starting a fresh chat.
• Keep your original reference handy. If the face drifts after several edits, start again from that reference.
• Make one focused revision at a time. “Reduce the background blur” is easier to assess than changing the background, outfit, pose, and lighting together.
• Give references separate jobs. One can define the face, another the outfit, and another the setting.
• Describe visible results. Light direction, fabric texture, framing, and expression are useful creative decisions.
• Inspect the small details. Hands, earrings, reflections, clothing seams, and text deserve a second look.

What are you going to shoot first?

A better headshot? A haircut preview? A different outfit?

TL;DR: Vercel’s Find Skills helps compatible AI agents discover and install reusable workflows from skills.sh, a directory listing more than 1.5 million skills. Find Skills itself currently shows approximately 3.7 million installs. Describe the task, compare relevant skills, install one, and put it to work. Claude and ChatGPT users can use this ecosystem through supported skill environments; the installation route depends on the app.

You know that workflow you explain to your AI every single week?

The audience. The process. The format. The checks. The things it keeps forgetting.

That’s a good candidate for a skill.

A skill packages instructions and supporting material into a reusable folder. Its core file, SKILL.md, explains what the skill does and when to use it. The folder can also include scripts, examples, templates, and reference documents.

Think of it as an onboarding playbook your AI can consult when the relevant job comes up.

Vercel’s Find Skills is itself a skill. Its job is to help your agent find other skills.

You describe what you need. It searches for relevant options, considers signals such as the publisher and installation count, and provides an installation path. Its instructions also tell it to check the leaderboard for established options.

Three pieces make this work:

• skills.sh: The directory you browse.
• Skills CLI: The command-line tool that searches, installs, and manages skills.
• Find Skills: The workflow that helps your agent discover suitable skills for your task.

Here’s how to start with Claude Code or local Codex.

Open a terminal in your project, with Node.js and npx available, and run:

npx skills add vercel-labs/skills --skill find-skills

Select your agent when prompted. The installer supports Claude Code, Codex, Cursor, and many other agents.

Then give your agent a specific job:

Use Find Skills to find three skills for auditing a B2B SaaS landing page. Compare their sources, instructions, required tools, and fit. Recommend one, explain why, and show me its installation command.

Specificity helps. Give it the task, context, and desired output.

Some searches worth trying:

• “Find a skill for auditing my website’s technical SEO.”
• “Find a skill for turning a webinar transcript into LinkedIn content.”
• “Find a skill for improving a landing page’s messaging and conversion flow.”
• “Find a skill for reviewing React performance.”
• “Find a skill for checking website accessibility.”

The directory includes marketing skills covering SEO, copywriting, content strategy, conversion optimization, email sequences, and launch planning. There’s plenty here for marketers and founders to explore.

You can also search directly:

npx skills find seo audit

Or browse skills.sh before installing anything.

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Using the Claude or ChatGPT app? Match the installation method to your environment.

Your environment	How to approach it
Claude Code / local Codex	Use the terminal installation above.
Claude app	Compatible custom skills can be uploaded as ZIP files through Customize → Skills → + → Create skill → Upload a skill. Code execution must be enabled.
ChatGPT	The desktop app supports standalone skills through its Skills experience. Skills bundled in plugins can also work across web and mobile. A local terminal installation is a separate setup path.

A skill that expects command-line software, network access, or a particular integration still needs those dependencies available. Check its requirements before assuming it will run unchanged in another app.

These are the details worth understanding before you build a collection.

1. The description helps determine whether the skill gets used.
Agents use a skill’s name and description to recognize relevant tasks. Full instructions load when needed. If a skill isn’t activating, explicitly name it and check whether its description matches your request.

2. The supporting files can matter as much as the instructions.
Copying only SKILL.md can leave behind scripts, templates, or reference files the workflow expects. Keep the complete skill folder when those resources are required.

3. Install counts are a discovery signal.
They tell you about adoption. Evaluate the actual instructions, bundled scripts, dependencies, and publisher before enabling a skill. A popular listing still deserves inspection.

4. Project scope and global scope serve different purposes.
Keep project-specific workflows with the project. Use global installation for workflows you want available across projects. The CLI supports both, and lets you select individual skills from a repository.

5. A skill still needs useful inputs.
Give it your audience, source material, constraints, examples, and definition of a good result. A content skill cannot infer your positioning from an empty brief.

6. Test the result before expanding the collection.
My recommendation: run the same real task with and without the skill. Compare accuracy, usefulness, editing time, and whether it followed your requirements. Keep what earns its place.

7. Build a small sequence around an actual job.
For a content workflow, try research → draft → edit. Review each output before passing it along. Start with one useful skill and add the next when a specific gap appears.

Here’s the prompt I’d save:

Find a skill for [task]. My context is [business/project]. The output should be [deliverable]. Compare three relevant options, including their sources and required tools. Recommend one, explain the tradeoffs, and give me one concrete task to test it on.

The opportunity here is reusable process knowledge: finding a useful workflow once, then making it available when the next job arrives.

Pick something you do every week. Search for a skill that could improve it. Test it on real work.

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Explore: Find Skills · Skills directory · CLI documentation

TL;DR: OpenAI launched Dots on September 29: personal agents with their own cloud computer, persistent context, connected apps and ongoing responsibilities. The bigger story is OpenAI making an agent the front door to its ecosystem. Muse, Grok Bot and Instinct already offer competing approaches. The winner will be the one that finishes useful work with the least supervision and earns enough trust to keep doing it. Below: the comparison, 10 jobs to try, setup and voice instructions, and the memory detail worth reading twice.

One of the coolest thing is you can actually call your Dot and talk to it to give it instructions.

I am not sure about the name Dots at all - OpenAI spent billions on compute and named their agent after the thing Pac-Man eats for free.

Outstanding. We have reached the dot-com era again.

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But underneath the naming jokes, this is a serious product move.

OpenAI didn’t invent the personal agent yesterday. It made the personal agent the front door to its whole ecosystem. THAT’s the story.

Think about the friction today: the context is in one place, the files are somewhere else, the work happens in another app, and you are the unpaid integration layer.

OpenAI’s bet is that you’ll increasingly hand that coordination to one agent that knows what you’re trying to accomplish.

What Dots actually does

A dot runs on GPT-6 Astra and has its own cloud computer and browser. It can work across ongoing projects, retain context, use connected services and bring you updates or decisions. OpenAI says its plugin ecosystem connects to more than 4,000 apps. That’s ecosystem reach not automatic permission to access every app you use.

You describe an outcome, provide the relevant context and access, and explain where you want approval. Your dot can use tools and delegate work to Codex or ChatGPT Work, then bring the result back for review.

The personal agent is proactive, has memory and can keep researching and working on things for you.

The useful shift is from “answer this” to “keep this moving.”

Dots vs. Muse vs. Grok Bot vs. Instinct

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My read of the four approaches:

Dots: For ongoing work across the ChatGPT ecosystem. One primary dot today; broader teams of dots are a future plan.

Meta Muse: For everyday delegation with a free entry point. Available through its app, web and WhatsApp, with Link payments already live. Meta also announced a small-business expansion on September 29.

Grok Bot: For several AI teammates handling different responsibilities. Bots can coordinate with each other and work in group chats, with desktop and iOS access.

Instinct: For handing off life admin through messages and calls. Its Trusted Person network lets your Instinct coordinate with another person’s Instinct.

So “Muse needs another app while Instinct doesn’t” is already an outdated comparison. Muse works in WhatsApp.

And agent coordination needs a precise definition. Grok Bot’s multiple teammates and Instinct’s coordination across people are different features. OpenAI describes teams of dots as a future direction. I found no comparable Muse cross-person network in the official material reviewed for this post.

Muse’s momentum is real: MarketWatch reported more than 3.4 million downloads in its September 26 coverage. That is adoption, not proof of reliability. Reuters reported reliability and data-exposure problems around its launch. A viral demo and a dependable assistant are different milestones.

The 10 jobs I’d give a dot first

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These are suggested briefs, not claims that I personally tested every workflow. Each depends on the tools, sources and permissions you connect.

1 — Morning chief-of-staff brief

“At 8 a.m. Eastern on weekdays, review my calendar and permitted inbox. Give me three priorities, looming deadlines and decisions only I can make. Confirm the schedule.”

2 — Meeting preparation

“Prepare a one-page brief for this client meeting using the linked notes and account history. Include unresolved questions and the decision we need.”

3 — Content repurposing

“Turn this transcript into a Reddit post, LinkedIn post and short-video script. Match these writing samples. Save drafts for review.”

4 — Launch maintenance

“Compare the updated product scope with our launch materials. Propose revised messaging and identify every claim that needs checking.”

5 — Competitor monitoring

“Every Monday, check these three competitors’ pricing and product pages. Show what changed with dated source links. Confirm the recurring task.”

6 — Sales proposal support

“Compare these requirements with our product documentation. Draft responses and flag anything we cannot substantiate.”

7 — Invoice follow-up

“Compare completed projects with my invoice records. Identify possible missing invoices and prepare drafts. Ask before sending.”

8 — Weekly performance reporting

“Use this campaign data to explain what changed, what likely drove it and the three actions you recommend. Separate evidence from hypotheses.”

9 — Website quality checks

“Review this site for broken links, confusing copy and mobile layout problems. Produce a prioritized issue list and prepare fixes in a branch if repository access is available.”

10 — Travel research

“Compare three options for these dates and this all-in budget. Include travel time and cancellation terms. Ask before booking.”

The biggest upgrade you can make to any of those prompts: define what “done” looks like.

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How to use Dots on web, desktop and mobile

Desktop web: Open Dots in ChatGPT and complete the introduction. Connect the services it needs and personalize it.

Desktop app: Set it up there, inspect its activity and optionally connect your computer. Local computer work requires your computer to stay online with ChatGPT open.

Mobile app: Create the dot on desktop first, then access the same dot in the native ChatGPT app once the supporting update is available. Mobile web is not supported.

Slack and Teams: Add a supported contact method through the dot’s profile. Messaging access and permission to use other apps are separate connections.

Texting: Coming soon at launch.

Access starts with Pro and Business Premium in eligible markets. Enterprise users, including eligible Edu and Healthcare workspaces, can try an admin-enabled beta. Dots is 18+ and has no free tier at launch.

Your first dot is included on the eligible Pro or Business Premium plan. Dot conversations do not count against ChatGPT usage limits; delegated Codex and Work tasks still use the usual allowances. Don’t read “included” as unlimited execution.

How to call your dot and get useful work out of voice

Open your dot’s conversation and select the phone button. In the desktop app, you can also choose Call from its profile. You can type while talking. Calls initiated by your dot are planned for after launch.

My voice briefing formula: outcome + context + boundaries + deadline.

“Prepare tomorrow’s client brief from the notes I’ve shared. Include three decisions and the evidence behind your recommendations. Draft only. Have it ready by 8 a.m. Eastern. First, tell me what you think I’m asking for.”

Five pro tips:

Lead with the deliverable. “Create a one-page brief” beats a five-minute ramble that never identifies the output.

Type exact names, URLs, email addresses and numbers while you talk. Speech is useful for nuance; text is useful for precision.

Ask for a short readback of the scope before it starts.

State your preferred updates: “Tell me when you’re blocked or need a decision.”

Use an explicit stop instruction when you want work cancelled. Hanging up ends the conversation; assigned work may continue.

The “works while you sleep” detail people will get wrong

Idle proactive research uses read-only tools. It can inspect permitted sources and keep private notes, but cannot directly send messages, change connected-app content or control a browser or computer.

Work you already assigned can continue in the background under the normal authorization and safety rules.

Those are different situations. “Always on” is not a blanket permission slip.

The memory detail worth reading twice

OpenAI’s FAQ says you currently cannot inspect, directly edit or delete individual dot memories. Deleting the dot deletes its own context. Disconnecting an app stops new access but does not erase what the dot has already retained.

ChatGPT Memory, files and conversations have separate controls. Turning off model improvement also does not eliminate limited human review, including safety-related review.

That’s a meaningful product tradeoff for an assistant you expect to know your work deeply.

Also: Private Safety Processing is an API offering, not a default Dots privacy guarantee.

Why call it Dots?

I couldn’t verify an official explanation. My interpretation: a small, approachable presence that “connects the dots” across your work. That is a branding read, not OpenAI’s stated origin story.

Still sounds like a product Pac-Man would acquire.

Why the ecosystem is the real story

DevDay also added computer use to the Agents API—which originally launched September 10. It introduced plugin extensions and expanded ways to use a ChatGPT account and eligible plan allowance with participating tools. The enterprise Marketplace adds another route for buying approved partner software.

My interpretation: OpenAI is assembling an agent operating layer - identity, context, tools, execution and distribution with Dots as a personal entry point.

The desktop operating system still exists. But more of the decisions about which software to open, what information to move and how to finish a job could happen inside an agent conversation.

Own computer. Persistent projects. Memory. Proactive updates.

Those capabilities are becoming the admission ticket.

The fight is who finishes work with the least supervision, and who you trust with the keys.

That is how I would judge Dots, Muse, Grok Bot and Instinct: give them the same repeatable job, count the corrections, and inspect the finished result.