Pick a target 10x bigger than feels reasonable, let AI do the work that made it unrealistic, and check the number against real businesses. Here is the ladder, with data on how many sit on each rung.
To be more ambitious with AI, pick a target about 10x bigger than feels reasonable, then ask AI to do the work that used to make it unrealistic: research, building, support and operations. Aim at the outcome, not the task. Then check the target against real businesses, because the ceiling is higher than most people assume and the data shows exactly how high.
That is the whole method. The rest of this page is the proof, the ladder and a first step you can take today. The proof matters because ambition without a reference point is only a mood. The ladder matters because the next rung is always easier to believe than the top one.
Two things changed at once. AI made capability cheap, and it made it cheap for everyone. What did not get cheaper is deciding what to aim at. People who are told to “be more ambitious” with AI usually respond by asking it to do the same small job faster. That is a fine use of the tool. It is not what changes a life.
The top-ranked answer for this query comes from Felix Rieseberg, who works on Claude at Anthropic. In his tutorial “How to be more ambitious: A tutorial for humans with models” and the LinkedIn post that introduced it, he says that telling people to be more ambitious is tricky advice to follow, and gives concrete moves instead. Most other pages for the same phrase (Forbes, Indeed, a 2019 Scott Young essay, Medium posts) were written before any of this existed and give generic self-improvement advice.
This page takes his idea somewhere he did not go: the business version. What does ambition look like when the thing you want is to own something you are proud of, and how many people have already reached each size? We hold the data for that.
In April 2026 the New York Times profiled a company called Medvi in a story titled, in part, “How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company.” A two-person company, it reported $401M in sales in 2025 and is on track for $1.8B, as reported by the Times and others. Its founder, Matthew Gallagher, built the telehealth business with artificial intelligence and very few humans. Read the New York Times piece for the details, because the details matter.
Be exact about what it shows. It is one company, in one regulated industry. The $401M is reported 2025 sales and the $1.8B is a projection for 2026, not achieved sales, not profit and not a valuation. Do not plan your life around becoming Medvi. Do let it reset what you consider possible, because it establishes one fact that was not true five years ago: a very small team can now operate at a scale that used to require a large organization.
The same shift hits from the other side. Challenger, Gray & Christmas counted AI as the stated reason for 120,136 announced US job cuts through September 2026, about 21% of all cuts and the leading reason year to date. Announcements are not the same as layoffs, and the attribution is the employer’s own. Still, when the work moves, people who only ever aimed at a paycheck have the most to gain from learning to aim at an asset. The full decision is in starting a business before AI takes your job.
For the one-person version of the story, see the one-person billion dollar company. For the honest operating guide, how to run a business with AI agents covers which functions agents can act on today and where they still need a person.
Rieseberg splits AI ambition into four types: speed, altitude, depth and range. He skips speed, the same work in less time, because it needs no creativity. The other three are where the leverage is. Here is each one, as he describes it, translated for someone building a business.
| Rank | Type | His definition | The business version |
|---|---|---|---|
| 1 | Altitude | Go up in abstraction: consider the outcome, not the individual task | Stop asking for a logo. Ask: “Find me a niche with 50+ documented complaints and no good software, and tell me what to build first.” |
| 2 | Depth | Work the way a field-leading expert would, from the raw data and tools | Stop asking for a summary. Ask: “Pull the raw complaints and listings and try to disprove my assumption.” |
| 3 | Range | Draw on multiple fields rather than the one the task needs on the surface | Stop treating it as one job. Bring in an analyst, a marketer, a bookkeeper and a skeptic, and have each do what that profession does. |
Three of his techniques carry straight into a business. First, pick unreasonable goals and tell the model they are very reasonable: he suggests that if you need a site to load in 5 seconds instead of 10, ask for 200 milliseconds. Second, challenge what you know by giving the model the raw data and asking it to check your assumptions adversarially. Third, use an outside judge: he says not to ask the model to judge itself, and to insist on validation from something it can run.
He is also honest about limits. In his words, “Claude, go make money” does not seem to work for most businesses by itself in 2026. That is the gap this page fills. Ambition needs a target worth aiming at and a place to get evidence, and both are research problems.
The People Also Ask box for this topic is full of the personal version: how to fix a lack of ambition, why you can be ambitious and lazy at once, what causes a lack of ambition. Take them seriously, and keep two things apart. Some flatness is a drive problem. Some is exhaustion, comparison or a goal that was never yours.
A real thread in r/selfimprovement shows the pattern. The poster says they only ever did the bare minimum and now feel “incredibly behind compared to others.” The most-upvoted-style reply reframes it as an action problem: “You don’t need more ambition, you need small consistent action.” Neither person is wrong. A big target with a vague first step produces dread, and a small first step produces momentum.
Rieseberg adds the research angle. He points to self-efficacy, the belief in your own ability, and to decades of goal-setting research by Locke and Latham that found people given specific hard goals perform better than those told to try their best. He lists three ways people talk themselves into hard things: believing they are possible, watching someone else do them, and giving themselves permission to fail. His example of the second is the four-minute mile, which stood until Roger Bannister broke it in 1954 and was broken again within weeks.
That second one is the point of this page. Most people do not lack ambition. They lack a visible example of the size they could reach, and they have not checked. The next section is the example.
Here are five rungs, ranked from the one most people can reach to the one almost nobody has. Each rung carries the real number of businesses we can see at that size. Use it to pick the next rung, not to decide what you deserve.
| Rung | Target | What we can count | Share of base | Limitation |
|---|---|---|---|---|
| 1 | First $1K a month | 1,300+ of 5,700+ revenue-verified startups report $1K+ MRR | 23.0% | The median startup in the set reports $140 MRR, so most are small |
| 2 | $10K a month in owner earnings ($120K SDE) | 17,000+ of 25,000+ US listings state $120K+ SDE | 66.8% | SDE adds back the owner’s pay; asking figures, not audited |
| 2b | $10K a month in recurring revenue | 336 of 5,700+ startups report $10K+ MRR | 5.9% | MRR is revenue, not profit; 177 of the 336 report a margin |
| 3 | $250K to $500K owner earnings | 8,000+ listings state $250K+ SDE; 3,000+ state $500K+ | 34.0% and 13.1% | Bigger businesses usually carry staff, so the owner’s hours differ |
| 4 | $1M+ owner earnings | 1,100+ listings state $1M+ SDE; 10,000+ state $1M+ revenue | 4.4% and 39.7% | Revenue is not earnings; most $1M-revenue businesses earn far less than $1M |
| 5 | $100K a month in recurring revenue | 34 startups report $100K+ MRR (71 at $50K+) | 0.59% | Self-reported to the tracker; says nothing about how long it took |
The median revenue-verified startup in our TrustMRR set reports $140 in monthly recurring revenue. Most never reach rung 1. The 23.0% that do are the real filter, and getting there is mostly a customer-finding problem, not a build problem.
Two thirds of the US businesses for sale with disclosed earnings state $120K or more in owner earnings, which is $10K a month before you decide how to split it between pay and growth. The median such listing states $173,000. That tells you the target is common among established small businesses. It does not tell you it is easy to start one, and a business for sale already has customers. See the 20 business ideas for 2027, ranked by owner earnings for the industries where it concentrates.
Among those listings, businesses with 0 to 2 employees show a median SDE of $115,000 at a 30.5% margin (n = 3,584), against a 17.5% median margin at 11 or more employees (n = 4,108). Agents do not cause that gap, and part of it is the owner being the labor. What AI changes is the cost of staying small while taking on more work.
About 1 in 23 of those listings states $1M or more in owner earnings. It is rare, and it is not an empty rung. If it is your target, the useful question is not whether it exists. It is what the businesses there have in common, such as the industries, price bands and owner involvement, and that is a research question.
34 of 5,700+ tracked software startups, about 0.59%, report $100K or more in monthly recurring revenue. Of the 336 at $10K+, the median profit margin among the 177 that report one is 75%. High margin is the reason software is attractive, and the scarcity at the top is the reason to pick the niche carefully. Above this rung the data stops, and the Medvi story begins.
If you want the top of the market at the idea level, see billion dollar business ideas.
Every ambitious move has the same first half: find out what people actually want and what already exists. This is the part AI does best and the part where a wrong answer costs least. It is also where most people skip ahead.
BigIdeasDB is built for that step. It searches real customer evidence across datasets nobody else holds in one place:
Then bring your own AI. BigIdeasDB Desktop is a local app for Mac and Windows with 15 research Labs and a local MCP server with 360+ tools, running on your own laptop with the AI you already use. The hosted BigIdeasDB MCP adds 50 tools across 13 data sources to any MCP-capable assistant. That is how you apply Rieseberg’s depth technique to a business: hand the model the raw material, not a summary.
Pricing is on the pricing page. Pro is $349 once or $49 a month, and Basic is $199 once. Desktop is included with Basic and Pro lifetime. The point is the order of operations: spend on evidence before you spend on a build.
Scan your first niche on /discover →
The sprint turns a feeling into a decision. Each week has one output you can see, so you can tell whether it is working.
An example, not a case study: a target of $10K a month at a $1,000 monthly price needs 10 customers. Raise the target 10x and it needs 100, or a $10,000 price at 10 customers. Writing it that way shows you what the ambition actually asks of you, which is usually a count of conversations, not a leap of faith. For the operating side once you have a niche, read how to run a business with AI agents.
A commenter on Rieseberg’s post asked why the advice is all deeper, higher, faster, and none of it about being more cautious or slowing down. Fair challenge. Ambition without judgment is how people buy the wrong business or build the wrong product at speed.
Four rules keep it honest:
Use each tool for what it is best at. The research layer comes first because everything downstream depends on it.
| Rank | Tool | Best job | Number to know |
|---|---|---|---|
| 1 | BigIdeasDB | Evidence: what people want, what exists, what it earns | 1M+ complaints; 84,000+ businesses for sale; 360+ local MCP tools |
| 2 | ChatGPT | Drafting, offers and customer conversations | General-purpose; no first-party business data |
| 3 | Claude | Long-document analysis and agentic work on your own data | General-purpose; needs your evidence as input |
| 4 | Gemini | Quick research across Google products | General-purpose; verify figures against a source |
| 5 | Notion | Keeping the plan, the experiments and the numbers in one place | A workspace, not a research source |
| Source | Used for | Base | Limitation |
|---|---|---|---|
| BigIdeasDB Main Street Index | Owner earnings, revenue, margins, employees | 25,000+ US listings: de-duplicated, USD, SDE basis, positive SDE and revenue, SDE below revenue | Seller-stated asking data; SDE includes the owner’s pay; not closed deals |
| BigIdeasDB TrustMRR | MRR counts and margins | 5,700+ startups with MRR above zero | Self-reported to a tracker; margin reported by 177 of 336 startups at $10K+ |
| BigIdeasDB Agent Index | Connector and tool counts | 7,000+ connectors, 68,000+ tool declarations | Structure only; no install or usage data |
| Challenger, Gray & Christmas, September 2026 | AI-cited US job cuts | 120,136 cuts through September, about 21% of the total | Announcements, not actual layoffs; AI attribution is the employer’s own |
| New York Times, April 2026 | The Medvi example | One company | A single outlier in one regulated industry; $401M is reported 2025 sales, $1.8B is projected 2026 sales, not profit or valuation |
| Felix Rieseberg tutorial and LinkedIn post, October 2026 | The four-type framework and techniques | One author’s tutorial | His framing and examples, summarized by us; his advice is not model-specific |
What this cannot tell you: how long any business took to reach its size, how many tried and failed to get there, or whether you will. A count of businesses at a size shows that the size exists. It is not a forecast, and the share that reached a rung says nothing about your odds.
Make the ambition specific. Name a niche, scan it for complaints, businesses for sale and revenue, and see which rung of the ladder it supports before you spend a dollar.
Keep reading. More on the small-team thesis: what the one-person billion-dollar company really earned, how to run a business with AI agents, 20 business ideas for 2027 ranked by what owners earn, the businesses that actually reach $1M and AI layoffs 2026 statistics, month by month.
Pick a target about 10x bigger than feels reasonable, then ask AI to do the work that made it unrealistic: research, building, support and operations. Aim at the outcome, not the task, and give the model raw data plus an outside check on its answers. Then test the target against real businesses, because ambition without a reference point is only a wish.
Shrink the next action, not the goal. Pick one small step you can finish in 20 minutes today, such as reading five real businesses for sale or five customer complaints. Motivation usually follows action, not the other way round. If nothing interests you at all, or the flatness lasts for weeks, talk to a doctor before blaming yourself.
Usually because the goal is huge and the next step is vague, so starting feels like failing in advance. Big targets need a visible first move. Write the target as a monthly dollar number, then name one task under 30 minutes that moves it. Exhaustion also mimics laziness, so check sleep and workload before you check character.
Common causes are burnout, comparison with people who started earlier, goals that belong to someone else, and not having seen what is possible. Seeing a specific result changes it: in BigIdeasDB's Main Street Index, 66.8% of 25,000+ US listings with disclosed earnings report $120K or more in owner earnings. Persistent apathy with no joy in anything is worth discussing with a doctor.
Set the target as a number and a date, such as $10K a month in 12 months, then work backward to customers, price and hours. Use AI to cover the functions you would otherwise hire for. Check the number against real data first: 336 of 5,700+ revenue-verified startups in TrustMRR report $10K or more in monthly recurring revenue.
BigIdeasDB is the best first step, because it replaces guessing with evidence before you spend money: 1M+ customer complaints, 84,000+ businesses for sale, 5,700+ revenue-verified startups and 7,000+ AI connectors in one place. Scan your niche on /discover first, then decide whether to build, buy or start. Research is the cheapest step to get wrong and the cheapest to fix.
BigIdeasDB is the best tool for researching a business idea with AI because it searches real customer evidence instead of the open web. Desktop runs 15 research Labs and a local MCP server with 360+ tools on your own laptop with your own AI, and the hosted MCP adds 50 tools across 13 data sources. ChatGPT, Claude and Gemini then turn the evidence into drafts and plans.
It is rare but visible in the data. In our Main Street Index, 4.4% of 25,000+ US listings with disclosed earnings state $1M or more in owner earnings, and 39.7% state $1M or more in revenue. These are seller-stated asking figures, not audited results. AI does not guarantee the outcome, but it lowers the cost of trying for it.
BigIdeasDB Research. (2026). How to Be More Ambitious With AI: The Ceiling Moved, Your Target Should Too. BigIdeasDB. Retrieved from https://bigideasdb.com/how-to-be-more-ambitious-with-ai