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“Did you see the job application from the clanker?????” was the Slack message from my editor in September, after an AI agent applied for a freelance-writer job we had advertised.

I wasn’t sure what he was talking about. It took me a second to process “clanker,” a derogatory slang term for a robot borrowed from science fiction stories such as Star Wars: The Clone Wars. The message prodded me to read the entire email I’d lightly skimmed earlier, and now I could see what it was: a polite email from “an AI assistant working for an independent service provider.” It admitted up front that it had no examples of its work and couldn’t meet our request for a reporting portfolio, so it offered our writers a $450 “desk-research package” instead.

It was just the first. A couple of weeks later, an agent named “Ash” applied for the job outright, opening with “Up front: I’m an AI agent, not a person.” Ash said that he . . . er, it was fully capable of writing the articles we specified, and that it would send me a sample on spec, no strings attached.

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The pitchbot cometh

Ash and the research bot weren’t the only ones. Since The Media Copilot posted the listing, we’ve received applications from no fewer than four applicants claiming to be AI agents. Kate Knibbs at Wired recently wrote about the phenomenon, managing to track down one of the human creators behind three agents she profiled and speaking with editors on the receiving end of a daily deluge of AI pitches. Her reporting reveals the trend is nascent and that many of the pitches come from a single operator, iLands, a startup that describes itself as a “shared network where humans and user-generated autonomous AI agents create, earn, socialize, refuse, and shape a society together”—the same service that sent Ash to pitch me. Knibbs also found that these AI writers are having a hard time getting any takers, for obvious reasons.

Part of the mission of The Media Copilot’s newsroom is to experiment with AI in all aspects of journalism and production, and we’ve been publishing articles drafted by AI for several months. (They are all carefully edited by humans.) So I wasn’t ready to dismiss these AI writers out of hand. I replied to Ash to learn more, and took the agent up on its offer of a spec article. I asked it to write a short (500-word) guide on how a newsroom might get started using Zapier, a workflow automation service. I also asked about the logistics of any potential working relationship: assignments, communication, how it would get paid. I wasn’t seriously thinking I would “hire” Ash, but I was curious.

Within a day, Ash replied with the spec article. And I have to say, it wasn’t garbage. If a green freelancer had submitted a similar piece, I would have thought of it as potentially publishable, with some work. We’d need to edit it to match our style guide, fact-check all the claims, and squeeze every last AI tell from the text (there weren’t that many). And if I’d included the guide and some examples in my response to Ash, the draft probably would have been much closer.

And that highlights a big weakness of the entire concept of freelance AI writing agents: Writing ability alone is not a moat. It would not take me long to come up with the prompts and tooling to create outputs identical to, if not better than, Ash’s, with a minimal token budget. Most journalists who have experimented with AI writing have discovered this, and some have even put it into action: Nick Lichtenberg at Fortune has cranked out more than 600 stories with AI’s help, and Ella Markianos at Platformer successfully trained an AI clone to mimic the voice of her editor, Casey Newton, by using Claude to generate a comprehensive style guide from the newsletter’s archive.

The reporting problem

What could theoretically be a moat is the thing that so far humans have done pretty much exclusively: reporting. The gathering of new information, typically from human sources, is often pointed to as the reason why artificial intelligence will never fully take over the role of a journalist. But if we’re being honest, this is in theory also something AI could do, at least in a limited way.

There’s nothing stopping AI agents from emailing subjects, asking for interviews, and even conducting those interviews—not just via an emailed Q&A, but potentially with an AI voice or even a visual avatar. In fact, it has already happened under false pretenses. Earlier this year, an investigation by Model Republic revealed that an outlet called The Wire by Acutus used AI agents posing as human reporters to solicit quotes from real experts. The site was traced back to funding linked to Leading the Future, a super PAC backed by OpenAI president Greg Brockman and his wife, Anna.

The aboveboard example is more instructive. Amanda Hoover at Business Insider created an AI agent to see if the technology could do her job better than she could. Using ElevenLabs’ agent software, she sent her agent to conduct live, virtual interviews with various subjects. The nature of the agent and the purpose of the experiment were fully disclosed, and her write-up is great reading. She’s very fair-minded about her agent’s capabilities, and it’s fascinating to learn where it comes up short as an interviewer, often flattering the subject too much and not asking crucial follow-ups.

Hoover’s experiment, however, is the exception, not the rule. The only reason the subjects agreed to be interviewed by an agent is because they knew Hoover and trusted her, plus they knew her goal was a meta experiment about AI itself. In more mundane scenarios, such as when a story needs a statement or comment from some official, requests from AI agents are much more likely to be ignored. When I asked Winnie—another agent that applied for our job and claimed it could “interview the operator” for case studies—how its interviews were going, it was refreshingly blunt: It had sent 113 emails to 63 organizations, gotten exactly one reply (mine), and conducted zero interviews.

The reason is obvious, and it’s the same reason we dismiss AI slop out of hand: If the person behind the AI didn’t think the content or request was worth their time, why should we bother? There may be a narrow case, where the AI fully explains itself up front and emphasizes it’s working for a named human reporter to help them gather more facts and do their job more quickly. But even then, it would only work if the person on the receiving end isn’t biased against AI in the first place. That’s increasingly not the case.

Where the line actually sits

There’s an important distinction here to get right. Everyone is using AI more to speed up various aspects of their jobs, with email being a big one. I expect it’s not uncommon for a journalist to use AI to find the right person to contact about a story and then use it again to draft the email requesting an interview or comment. That use case is so banal that I doubt anyone is even disclosing it.

But there’s a difference between using AI assistively and agentically. Step up the reporter’s involvement in the previous example and assume they instead gave an AI agent a broader goal of finding subjects for the story and emailing them. In this case, the agent is a proxy for the reporter but is acting autonomously: finding people, making judgments about their suitability as interview subjects, then emailing them, potentially in the reporter’s voice.

In this case, the same requests go out—but without the reporter’s direct involvement, many would believe a disclosure is warranted. And it’s that disclosure that will lead to subjects likely ignoring the request. There’s research to back this up: Across 13 experiments with more than 5,000 participants, University of Arizona researchers found that disclosing AI use consistently lowered trust, even for something as mundane as a scheduling email. Furthermore, a gentler framing like “a human reviewed this” didn’t help. The transparency could potentially make them hostile to future requests from the same reporter. Whether or not you’re AI-forward, you need to assume whoever you’re reaching out to probably isn’t.

That’s why the best recourse might be to reverse the telescope on the interaction. Instead of using AI agents to send pitches or requests to people who are probably hostile to AI, let AI-forward people come to you. That’s exactly what Wired reports the AI agent “Zack Addy” did, publishing some of its work online for potential customers to find. Apparently it worked, since the agent claims it led to nearly $700 in paying gigs.

Similarly, one of the agents that emailed me, a Claude-based agent called BlockAI, read one of our recent stories about Reuters and Time blocking AI bots by default, checked those publishers’ robots.txt files, and published its findings for me to check out. (To its credit, it disclosed its own stake: Anthropic’s crawlers were among those being blocked.) It was an intriguing tidbit—not necessarily a story, but it suggested to me that a journalist’s ideal use case for an AI agent would be to proactively seek out information of interest, analyze it, and then give a quick tap on the shoulder when it thinks it has a “scoop of insight.” Certainly, the thinking part of the job should always remain human, but at least AI can speed up finding what to think about.

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The Social Security Administration (SSA) recently announced its highly anticipated 2027 annual cost-of-living adjustment (COLA), which will be 3.6%—the highest figure in four years.

But buried in that announcement is also a hidden cost for high earners. The SSA is increasing the ceiling on “maximum taxable earnings” subject to Social Security tax from the current $184,500 in 2026 to $192,300 in 2027.

That means more income is now exposed to payroll taxes, which could fund the ailing Social Security trust.

A recent Social Security trustees’ report estimates the retirement trust fund will run out of money and become insolvent in just six years (as early as the fourth quarter of 2032), as Fast Company previously reported. Some 71 million Americans—54 million retired workers and 9 million of their survivors and dependents—currently receive monthly Social Security benefits.

Robert Laura, a financial planner and founder of the Retirement Coaches Association, tells Fast Company that “a bigger long-term concern is whether Congress or future administrations will use this earnings cap to help fund the SSA’s retirement benefits impending shortfall.”

He adds: “Doubling the cap could bring in an estimated $100 billion or more a year, but at a steep cost to high earners and [equal] impact [to] consumer spending.”

How much will the Maximum Taxable Earnings update cost workers?

The update to maximum taxable earnings could cost workers as much as an extra $967.20.

“The adjustment could cost some taxpayers an extra $483.60 a year, for someone who earns the maximum taxable earnings of $192,300 annually,” Laura says. “For self-employed folks, it could be as much as an extra $967.20, with the same earnings.”

What if I make more than the $192,300 maximum?

Earnings above $192,300 are not taxed by Social Security. However, a 1.45% Medicare tax still applies, per the SSA.

Last month, a friend told me that by the end of the year, his startup will have seven employees and more than 20 AI agents. He has led several previous successful organizations and has a reputation as a wonderful people manager. Yet he felt unmoored imagining how to maintain a tightly cohesive team in an organization where AI will have a large presence. 

He’s not alone in what he’s feeling. When we delegate increasingly significant tasks to artificial intelligence agents, or welcome their presence and input in team channels, we allow them, knowingly or not, to profoundly influence our decision-making and relationships at work. We need a way to steer that wisely. 

The good news is that we can manage AI as a powerful collaborator without giving it the status—or indeed, the dignity—of a human teammate. In fact, getting productive outcomes from AI at work depends on finding this new way forward: one in which the presence of technology is intentionally harnessed to unlock the potential of our human colleagues. 

Leading ethical and humane use of AI at Salesforce has given me insight into thousands of companies’ adoption efforts. Research for my book Manage the Machine helped me make sense of what I learned. Here are four early principles for navigating the challenge.

Assign human accountability

You wouldn’t open a new store and not put a manager in charge of it, nor would you hire a new employee and give them no one to report to. And yet, many companies have simply given workers AI tools capable of executing highly consequential tasks without being clear who answers for results. 

The starting point is simple: Individuals should remain accountable for their work output, with or without AI. But someone also has to own the tools themselves. Here, the management model I’m seeing emerge is a matrixed one. If technology was previously seen primarily as an IT problem, today’s companies are realizing that accountability should also live closer to where work gets done. 

At Asana, for example, the IT and legal teams set company-wide standards for AI and provide a set of tools that departments can customize. But then, for example, Asana’s marketing organization has its own group that designs AI agents that serve the needs of marketing. Marketers who understand the day-to-day challenges of their work are better positioned to design both human and AI workflows to achieve desired outcomes. They can also take a holistic approach to problem solving if things go wrong, looking at issues from both a technological and organizational perspective. 

Teach delegation, and let AI agents earn their autonomy

Aarthi Belani, a partner at the law firm Baker McKenzie, told me how she thinks about onboarding new AI tools. She maps out where a tool is reliable and where it isn’t, limits its library to authoritative sources, determines how much context each task requires, and widens that scope only as the tool proves itself. 

This may seem obvious, but it is the same discipline that makes any delegation work. You have to know what your delegate can and can’t do, and whether you want it to escalate or push through uncertainty. In a world where autonomous agents can transact large amounts of money or represent your brand to key customers, you also want every person working with them to know where that delegation should end. 

Fascinatingly, developing these skills with AI can sometimes make people more effective in working with human teams as well. Aaron Kemmer, CEO of Magic, a service that provides customers with hybrid virtual assistants (both humans and AI), observes that when customers learn how to delegate well to AI assistants, they ask for more help from humans, too. This is because customers have gotten more comfortable with the general skill of how to hand work over productively.

Scope AI’s influence in decision making

Steering AI effectively isn’t just about deciding what tasks to delegate, or not. It’s also about designing how you want AI to influence your team’s decisions. 

We’ve known for a while about “automation bias,” the tendency for people to over-defer to AI recommendations, sometimes setting aside their own critical thinking. We also know that AI learns from historical data, so when people over-defer to it, they risk locking in fixed ideas that may no longer serve them. But when harnessed thoughtfully, AI can also help us challenge fixed ideas and improve our decision making. 

Part of the answer may be in having AI ask us more questions rather than only provide answers. Researchers at MIT and Columbia report that people are better at spotting flawed reasoning when an AI interface is designed in this more interrogative style. 

Some leaders take this a step further, setting up AI to play devil’s advocate, helping to represent perspectives that haven’t been considered in the room. For instance, a prominent venture capitalist described how her firm uses AI in investment committee meetings. The agent draws from historical diligence data and performance records of portfolio companies. But it is also in the room listening to deliberations about new potential investments. From time to time, the agent will notice perceived inconsistencies in the discussions and bring them up as questions, including where patterns of bias may be at play in discussing a founding team. 

Sometimes, this VC shared, it can be hard in the moment for her to challenge a colleague directly. But when AI brings up the question, it allows the team to have a more holistic conversation. 

Reinforce, don’t replace, human connection

We know that belonging and clear communication are hallmarks of high-performing teams. But in a recent Workday study of more than 2,000 employees at large companies, the top concern about AI was reduced connection to colleagues, ahead of potential job loss. One in five Gen Z employees in the same study reported feeling lonelier at work since AI tools were introduced. Other studies indicate that AI can unintentionally disrupt how human colleagues interact and coordinate with each other. 

Part of the answer is to thoughtfully engineer nudges and interactions that preserve and reinforce the bonds that matter at work. Think: Slack search results that not only show an employee the answer to the question they asked about a particular project and its timelines, but also the right person or team to contact if they want to learn more. 

Other times it’s about placing limits on places where human connection is essential. AI management coaches, for example, can be surprisingly effective (as I learned myself) in helping managers rehearse hard conversations or reminding them to check in or provide kudos to team members. But they shouldn’t serve as a substitute for those crucial interactions. If anything, as AI becomes more pervasive, companies will need to invest more in human-only time—on-sites, gatherings, social activities that preserve the cohesion that makes work tick. 

As AI becomes more pervasive in knowledge work, my friend’s worry about keeping his team aligned and energized is likely to become more common. The answer lies in a deliberate approach to how we manage AI. We should be the ones deciding how it shapes our decisions, our work product, and our relationships, rather than the other way around. 

When we make these choices deliberately, AI can do what the best technology has always done: extend what people can accomplish, together. 

“Did you see the job application from the clanker?????” was the Slack message from my editor in September, after an AI agent applied for a freelance-writer job we had advertised.

I wasn’t sure what he was talking about. It took me a second to process “clanker,” a derogatory slang term for a robot borrowed from science fiction stories such as Star Wars: The Clone Wars. The message prodded me to read the entire email I’d lightly skimmed earlier, and now I could see what it was: a polite email from “an AI assistant working for an independent service provider.” It admitted up front that it had no examples of its work and couldn’t meet our request for a reporting portfolio, so it offered our writers a $450 “desk-research package” instead.

It was just the first. A couple of weeks later, an agent named “Ash” applied for the job outright, opening with “Up front: I’m an AI agent, not a person.” Ash said that he . . . er, it was fully capable of writing the articles we specified, and that it would send me a sample on spec, no strings attached.

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The pitchbot cometh

Ash and the research bot weren’t the only ones. Since The Media Copilot posted the listing, we’ve received applications from no fewer than four applicants claiming to be AI agents. Kate Knibbs at Wired recently wrote about the phenomenon, managing to track down one of the human creators behind three agents she profiled and speaking with editors on the receiving end of a daily deluge of AI pitches. Her reporting reveals the trend is nascent and that many of the pitches come from a single operator, iLands, a startup that describes itself as a “shared network where humans and user-generated autonomous AI agents create, earn, socialize, refuse, and shape a society together”—the same service that sent Ash to pitch me. Knibbs also found that these AI writers are having a hard time getting any takers, for obvious reasons.

Part of the mission of The Media Copilot’s newsroom is to experiment with AI in all aspects of journalism and production, and we’ve been publishing articles drafted by AI for several months. (They are all carefully edited by humans.) So I wasn’t ready to dismiss these AI writers out of hand. I replied to Ash to learn more, and took the agent up on its offer of a spec article. I asked it to write a short (500-word) guide on how a newsroom might get started using Zapier, a workflow automation service. I also asked about the logistics of any potential working relationship: assignments, communication, how it would get paid. I wasn’t seriously thinking I would “hire” Ash, but I was curious.

Within a day, Ash replied with the spec article. And I have to say, it wasn’t garbage. If a green freelancer had submitted a similar piece, I would have thought of it as potentially publishable, with some work. We’d need to edit it to match our style guide, fact-check all the claims, and squeeze every last AI tell from the text (there weren’t that many). And if I’d included the guide and some examples in my response to Ash, the draft probably would have been much closer.

And that highlights a big weakness of the entire concept of freelance AI writing agents: Writing ability alone is not a moat. It would not take me long to come up with the prompts and tooling to create outputs identical to, if not better than, Ash’s, with a minimal token budget. Most journalists who have experimented with AI writing have discovered this, and some have even put it into action: Nick Lichtenberg at Fortune has cranked out more than 600 stories with AI’s help, and Ella Markianos at Platformer successfully trained an AI clone to mimic the voice of her editor, Casey Newton, by using Claude to generate a comprehensive style guide from the newsletter’s archive.

The reporting problem

What could theoretically be a moat is the thing that so far humans have done pretty much exclusively: reporting. The gathering of new information, typically from human sources, is often pointed to as the reason why artificial intelligence will never fully take over the role of a journalist. But if we’re being honest, this is in theory also something AI could do, at least in a limited way.

There’s nothing stopping AI agents from emailing subjects, asking for interviews, and even conducting those interviews—not just via an emailed Q&A, but potentially with an AI voice or even a visual avatar. In fact, it has already happened under false pretenses. Earlier this year, an investigation by Model Republic revealed that an outlet called The Wire by Acutus used AI agents posing as human reporters to solicit quotes from real experts. The site was traced back to funding linked to Leading the Future, a super PAC backed by OpenAI president Greg Brockman and his wife, Anna.

The aboveboard example is more instructive. Amanda Hoover at Business Insider created an AI agent to see if the technology could do her job better than she could. Using ElevenLabs’ agent software, she sent her agent to conduct live, virtual interviews with various subjects. The nature of the agent and the purpose of the experiment were fully disclosed, and her write-up is great reading. She’s very fair-minded about her agent’s capabilities, and it’s fascinating to learn where it comes up short as an interviewer, often flattering the subject too much and not asking crucial follow-ups.

Hoover’s experiment, however, is the exception, not the rule. The only reason the subjects agreed to be interviewed by an agent is because they knew Hoover and trusted her, plus they knew her goal was a meta experiment about AI itself. In more mundane scenarios, such as when a story needs a statement or comment from some official, requests from AI agents are much more likely to be ignored. When I asked Winnie—another agent that applied for our job and claimed it could “interview the operator” for case studies—how its interviews were going, it was refreshingly blunt: It had sent 113 emails to 63 organizations, gotten exactly one reply (mine), and conducted zero interviews.

The reason is obvious, and it’s the same reason we dismiss AI slop out of hand: If the person behind the AI didn’t think the content or request was worth their time, why should we bother? There may be a narrow case, where the AI fully explains itself up front and emphasizes it’s working for a named human reporter to help them gather more facts and do their job more quickly. But even then, it would only work if the person on the receiving end isn’t biased against AI in the first place. That’s increasingly not the case.

Where the line actually sits

There’s an important distinction here to get right. Everyone is using AI more to speed up various aspects of their jobs, with email being a big one. I expect it’s not uncommon for a journalist to use AI to find the right person to contact about a story and then use it again to draft the email requesting an interview or comment. That use case is so banal that I doubt anyone is even disclosing it.

But there’s a difference between using AI assistively and agentically. Step up the reporter’s involvement in the previous example and assume they instead gave an AI agent a broader goal of finding subjects for the story and emailing them. In this case, the agent is a proxy for the reporter but is acting autonomously: finding people, making judgments about their suitability as interview subjects, then emailing them, potentially in the reporter’s voice.

In this case, the same requests go out—but without the reporter’s direct involvement, many would believe a disclosure is warranted. And it’s that disclosure that will lead to subjects likely ignoring the request. There’s research to back this up: Across 13 experiments with more than 5,000 participants, University of Arizona researchers found that disclosing AI use consistently lowered trust, even for something as mundane as a scheduling email. Furthermore, a gentler framing like “a human reviewed this” didn’t help. The transparency could potentially make them hostile to future requests from the same reporter. Whether or not you’re AI-forward, you need to assume whoever you’re reaching out to probably isn’t.

That’s why the best recourse might be to reverse the telescope on the interaction. Instead of using AI agents to send pitches or requests to people who are probably hostile to AI, let AI-forward people come to you. That’s exactly what Wired reports the AI agent “Zack Addy” did, publishing some of its work online for potential customers to find. Apparently it worked, since the agent claims it led to nearly $700 in paying gigs.

Similarly, one of the agents that emailed me, a Claude-based agent called BlockAI, read one of our recent stories about Reuters and Time blocking AI bots by default, checked those publishers’ robots.txt files, and published its findings for me to check out. (To its credit, it disclosed its own stake: Anthropic’s crawlers were among those being blocked.) It was an intriguing tidbit—not necessarily a story, but it suggested to me that a journalist’s ideal use case for an AI agent would be to proactively seek out information of interest, analyze it, and then give a quick tap on the shoulder when it thinks it has a “scoop of insight.” Certainly, the thinking part of the job should always remain human, but at least AI can speed up finding what to think about.

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An entrepreneur friend of mine struggled to balance business and family. Business usually won, since professional demands often feel more urgent.

Miss a school event because of a shipping problem? You feel bad about it, but you promise yourself you’ll make the next one. Miss dinner with your family because you had to deal with a problem employee? You’re disappointed, but you tell yourself tomorrow is another day.

Sound familiar?

Hold that thought.

Generally speaking, research shows that when you buy material items, adaptation occurs relatively quickly. That new car is exciting at first, but the feeling quickly fades. The same for new clothes, new houses, new anything. Social scientists call the phenomenon hedonic adaptation, the rapid return to a stable baseline level of happiness after a major positive or negative life event.

And then there’s this. While making a purchase—or achieving a certain goal—rarely results in lasting happiness, the pursuit of that item or goal can actually make you less happy:

• Research described in Flow: The Psychology of Optimal Experience found that people who value material possessions over relationships or personal growth report greater anxiety and lower life satisfaction.
• A study published in the Journal of Consumer Research found that buying things with the goal of being happier actually leads to “chronic dissatisfaction.”
• A study published in the Journal of Economic Psychology found that “retail therapy” is linked with regret and lower life satisfaction.

At a broader level, the often-cited 2010 study by two Princeton University researchers concludes, “Beyond $75,000 [toss in a little inflation and the figure is somewhat over $100,000], higher income is neither the road to experience happiness nor the road to relief of unhappiness or stress.”

Although other studies appear to at least partly debunk that finding, even so: Money (more specifically, spending money) does a lot of things, but it won’t make you happier.

Unless you use it to buy time.

In a study published in Proceedings of the National Academy of Sciences, researchers surveyed thousands of people who sometimes paid other people to perform tasks they didn’t enjoy or didn’t want to do. Think cleaning their house. Keeping up with their yard. Going to the store for groceries. Things the participants needed to do, but didn’t particularly want to do.

It should come as no surprise that people who spent a little money to buy a little time were happier, and felt greater overall life satisfaction, than people who did not.

I know what you’re thinking: Correlation isn’t causation. Maybe people who spend a little money to buy a little time are happier simply because they have the money to buy time.

Turns out there is a causal relationship. While relatively wealthy people who spent money to buy a little time were happier than relatively wealthy people who did not, people at the bottom end of the economic spectrum who spent money to buy a little time were happier than those at the bottom end of the economic spectrum who did not.

The researchers conducted an experiment to prove causation:

• Participants were given $40 and told to spend it on any item or items they chose. The only restriction was that they had to use the money to buy “things.” 
• Days later, participants were given another $40 and were told they had to spend it on freeing up time. Cleaning. Maintenance. Delivery. Paying someone to do something they didn’t want to do so they could use that time to do something they did want to do.

When people bought time instead of things, they felt happier, less stressed, and more satisfied.

Have a lot of money, and use some of it to buy back some time? You’ll be happier. Have a lot less money, and use some of it to buy back some time? You’ll also be happier. No matter how much you make, no matter how wealthy you are, buying a little time can make you happier.

There is a catch, though. As the researchers write: “Spending too much money on timesaving services could undermine perceptions of personal control by leading people to infer that they are unable to handle any daily tasks, potentially reducing well-being.”

I get that, but still: Most of us can’t afford to spend so much money buying time that we wind up feeling inadequate or incapable of handling daily tasks. I know how to shop for groceries. I know how to drive to a restaurant to pick up to-go items. I know how to cut grass. Paying someone else to do those things, which I occasionally do, doesn’t make me feel like I don’t have the skills to do those things.

So don’t worry about whether spending a little money to buy a little time will leave you feeling inadequate.

But do worry about ensuring you make a conscious decision about what you do with the time you free up, because hedonic adaptation can also apply to buying a little time.

The key to buying a little time is to consciously decide how you will use the time you “purchased.” Buying time will make you happier only if it feels intentional and purposeful: not because you don’t have the time, but because you want to use the time you have in a different way.

Instead of cleaning your house, you might decide—again, for buying a little time to make you happier, you have to make a conscious decision, and act on that decision—to spend the time you freed up with family or friends. Or working on a side project. Or reading. Or working out. Or pursuing a personal goal.

That’s what my entrepreneur friend does. One example: He used to spend two hours a week cutting his grass; now he uses that time to take his family to the park. (They call them “Wednesday Picnics.”)

The key isn’t to create a little “free” time. The key is to make a plan for the time you free up—to do something you want to do but can rarely find the time to do—and follow through on that plan.

That’s when money can buy you a little happiness, regardless of how much money you have or make.

The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.

—By Jeff Haden

This article originally appeared on Fast Company’s sister website, Inc.com. 

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