With less than $100, one can buy tens of thousands of votes for Twitter polls using bot-for-hire manipulation services, according to the nonprofit digital rights group Accountable Tech. The finding, first reported by Bloomberg, could spark new concerns about Musks reliance on the tool to chart the future of one of the worlds most influential social media companies.

Musk spent months before the acquisition claiming that Twitters user numbers were fraudulent due to the preponderance of bots on the platform; lately he has been claiming the problem is resolved. He has said overall user numbers have gone up under his leadership.

Theres no evidence the number of bots has decreased, though. Not only are bots flourishing under Musk, its now easier than ever to use bots to manipulate Twitter polls, said Nicole Gill, co-founder and executive director of Accountable Tech. As long as Musk continues to put major platform decisions in the hands of anyone with a few dollars and some spare time, Twitter is unsafe and open to manipulation by bad actors including foreign governments.

Polls can be used for trivial matters, like helping decide what to eat for dinner. Musk uses them to determine Twitters fate, which gives for-hire services a greater incentive to learn how to manipulate the feature. Typically, key business decisions such as whether or not a CEO should lead a company are decided by boards and stockholders not Twitter polls, said Lisa Kaplan, chief executive of Alethea, which helps corporations mitigate the threat of misinformation. If this form of decision-making continues, its likely that polls could be a target for actors seeking to manipulate the platform.

Twitter didnt respond to a request for comment.

Since he took the helm of Twitter in October, Musk has turned to polls to create brand new social media policy on the fly, in spite of protestations from critics that the polls were easily gamed and unrepresentative of Twitters user base. And while Musk has declared victory over bots, there is little evidence the platform has actually tamped down on the problem. According to a report from Platformer, Musks war on bots amounted to Twitter blocking traffic from around 30 mobile carriers around the world, effectively cutting off access to hundreds of thousands of accounts in the Asia-Pacific region. The block on the carriers has since been reversed, Platformer reported. 

On the evening of Dec. 18, Musk ran his most popular Twitter poll yet: asking users whether he should step down as Twitters CEO. Twelve hours later, nearly 58% of the polls 17.5 million respondents voted yes. Musk promised to abide by the results of the poll, but after the vote closed, Musks replies to users on the platform appeared to indicate he didnt agree with the outcome.

In one conversation, after his fans suggested that the votes could have been manipulated, he suddenly was less confident in the integrity of Twitters user base, and said maybe we might still have an itsy bitsy bot problem on Twitter. In response to a user who suggested only subscribers of Twitter Blue the social networks $8-a-month tier that grants users a blue check mark  should be able able to vote on the companys policies, Musk responded, Good point. Twitter will make that change.

Two former members of Twitters Trust and Safety team said the company has lacked proper safeguards to identify and eradicate inauthentic behavior and manipulation of Twitter polls. Twitter is good at detecting fake engagement on the site  such as likes and comments that arent from real people. There are some technical signals that automated bot accounts leave behind, which allows Twitter to catch the activity and eventually remove it, according to one of the people familiar with the tools, who declined to be named discussing non-public technology.

But since the ability to vote on Twitter polls expires after a set period of time, there appears to be no way for Twitter to undo manipulated votes on a poll after it closes, the people said.

In December, Accountable Tech set up a dummy account called @VoxPopuliVoxBot and ran several polls to analyze the scale of manipulation that was possible on the Twitter feature. On its most popular poll, the research found that up to 26,261 votes could be cast by bots for a mere $57, with the votes being delivered within 24 hours. The study used readily available for-hire manipulation services originating in Russia, India and Turkey to conduct the experiment.

Can polls on Twitter be trusted? the dummy account tweeted on Dec. 16. The researchers tried to determine whether fake votes could be bought at scale, targeting half a million to a million votes set to be delivered in 24 hours, but the manipulation services did not have that capacity. If the for-hire manipulation services were to ramp up their bandwidth, the Accountable Tech researchers estimated it would cost $2,600 to $3,600 to throw a popular poll at that scale.

The researchers also bought fake comments, likes and follows on their dummy account. Notably, the inflated follower count shrank after a few days; a person familiar with Twitters technology explained this was evidence of automated guardrails on Twitters systems against bots. The research found other safeguards in place on Twitters systems. Fake followers bought through for-hire manipulated systems could be viewed only if a user unchecked the services quality filter, a feature Twitter launched in 2016 to let users weed out problematic tweets from their notifications.

Yoel Roth, the former head of Trust and Safety at Twitter, said in a recent interview at Rolling Stone that when Twitter launched polls in 2015, One of the big discussions was around the tradeoffs between integrity and privacy  keeping logs or not. We landed on the side of privacy, so that Twitter wouldnt know who voted for what. Polls are more prone to manipulation than almost anything else on Twitter, Roth added.

In order to test whether manipulated votes worked on a live poll, in December the Accountable Tech researchers bought votes on a poll being conducted by the white nationalist far-right media personality Tim Gionet, who runs an account called @bakedalaska on Twitter, and was recently reinstated on Twitter as part of Musks general amnesty for banned accounts another policy decision Musk made after running a poll.

On Dec. 14, Gionet asked his audience whether he should use a racist slur on Twitter. Accountable Tech researchers bought 2,000 votes on both the yes and no options, in order to neutralize the influence on the poll but prove that the manipulation was doable. It cost $13, and the poll closed with the votes on both options counted in the final results.

The people have spoken, Gionet tweeted on Dec. 15, after 52 percent of 11,764 votes counted pointed to the no option. He echoed the Latin pronouncement often used by Musk after running polls on Twitter, which roughly means that the voice of the people is the voice of God: Vox Populi, Vox Dei.

Musk, for his part, has expanded his use of polls beyond Twitter-related matters. On Dec. 21, he asked whether Congress should approve a $1.7 trillion federal spending bill. After 71% voted no, he shared the result with senate leaders. 

The public has spoken, he tweeted.

The percentage of companies adopting A.I. has been plateauing between 50% and 60% over the past few years. Meanwhile, some leaders have shared their frustration over not yet seeing the level of returns from A.I. investments that they expected. They simply havent been able to get it to scale.

Are some companies foregoing the pursuit of A.I.?

We hope not. Significant returns are being realized by leading companies. There is a contingent that is seeing substantial revenue boosts and 20% or more of their earnings before interest and taxes (EBIT) come from A.I. as they apply the technology to improve decisions, speed processes, and automate mundane, repetitive tasks, freeing workers to explore innovations that can vault an organization into entirely new business arenas.

These arent just tech firmstheyre companies in sectors from health care to mobility to financial services. And they plan to continue embedding A.I. in everything they do, widening their advantage over competitors that arent tapping A.I.s potential.

What are these companies doing differently? Theyre putting a significant chunk of their A.I. investments into capabilities that enable them to do more A.I., more rapidlywith less effort. Theyre putting their dollars into building a veritable A.I. factory.

From one-off productions to repeatable processes

At the start of A.I. journeys, companies typically execute a smattering of A.I. pilots and one-off use cases to tackle business problems. At some companies, with each success, they do more of what delivered those victories, for example, building more entirely bespoke models and encouraging more experimentation in silos of their organization.

While its wise to continue some investment in these areas, doing more of what yielded initial results isnt enough to make A.I. use more ubiquitous within your organization, which is the key to realizing maximum ROI.

As one-off productions multiply, so does the complexity and cost associated with teams starting every development effort from scratch, sifting through hundreds of raw data sets, writing bespoke code, and deploying their own development tools and technologies.

To scale A.I., investments must be channeled toward creating reusable assets, a platform, and repeatable processes (a factory, of sorts), which enable A.I. teams to build, deploy, and maintain models in less time and with less manual effort.

In our research, the respondents from organizations seeing the highest returns from A.I. are far more likely to employ this factory approach. And in our work, weve seen numerous organizations begin to get this right. Vistra Corp., the largest competitive power producer in the U.S., created an A.I. factory to standardize the deployment and maintenance of more than 400 A.I. models. It saw $60 million in savings in about a year and is on its way to delivering against a roadmap of $250 million to $300 million in identified EBITDA and a reduction in greenhouse gas emissions.

Similarly, an Asian financial services company was able to reduce the time to develop new A.I. applications by more than 50%, in part by creating high-quality, ready-to-use data products on top of data source systems that could be used in numerous A.I. applications. The company also standardized supporting data management tooling and processes to create a sustainable data pipeline, and it created assets to standardize and automate time-consuming steps, such as data labeling.

Inside the A.I. factory

An A.I. factory is not a physical place, but rather a framework for enabling teams to deliver more A.I. applications in less time and with less effort. It includes, for instance, providing development teams with easy access to the tools and technologies they need via one end-to-end platform that reduces time-consuming and costly integration efforts. Its establishing one A.I. development and deployment playbook and a standard set of protocols, bringing together best practices from across the company so development teams can use them over and over. And its focusing efforts on creating a handful of ready-to-use assetsbe it data products or snippets of codeto give teams a head start on the work.

Consider how simply re-using existing code can help drive several aspects of scale. As recently as a few years ago, most A.I. practitioners had to code every A.I. solution from scratch. Today, organizations can download state-of-the-art, open source, pre-trained A.I. models to use in application development. Weve seen, for example, one global energy company build a model that accurately predicted customer cancellations by applying just a few lines of code.

At the same time, emerging low-code/no-code platforms enable employees without extensive A.I. expertise to develop A.I. applications. Organizations seeing the highest returns from A.I. are 1.6 times more likely to be using these time-saving tools.

For code that still needs to be written in-house, leading organizations actively encourage a focus on reusability. They incentivize and celebrate the efforts of data scientists and engineers to contribute their code to a central codebase that other practitioners can easily tap into for other applications.

With reusable code available and nontechnical workers able to help develop basic A.I. applications, the most skilled A.I. practitioners are then freed to focus their expertise on engineering robust, high-value applications for the business. This increases their productivity, allowing organizations to do more with fewer of the most elite A.I. talent. It also keeps all A.I. practitioners a lot happier because each can focus on the most engaging tasks in their area of expertise, a key to their retention for the organization.

And, importantly, it lets the organization redirect talent dollars and efforts toward other emerging roles required to build out the A.I. factory. One such role is that of the machine learning engineer, who is skilled in turning A.I. models into enterprise-grade production systems that run reliably and automate the machine learning pipeline, from data ingestion to the generation of business insights.

Now is the time to lean into A.I., even during this time of disruption and resiliency. The winning companies are doubling down and proving its possible to do more A.I. in less time and with less effort so they can realize the high returns the technology promises.

Alex Singla is a senior partner and global co-leader of QuantumBlack, A.I. by McKinsey in Chicago. Alex Sukharevsky is a senior partner and global co-leader of QuantumBlack, A.I. by McKinsey in London. McKinsey is a partner of Fortunes Global Forum. 

Our new weekly Impact Report newsletter will examine how ESG news and trends are shaping the roles and responsibilities of todays executivesand how they can best navigate those challenges. Subscribe here.

While its pretty clear that pressurized housing affordability has triggered some deflation in the U.S. housing market, industry insiders remain divided on what the ongoing home price correction will look like in 2023. The reason? Demand and supply are sending mixed signals.

On the housing demand front, things remain slumped with mortgage purchase applications (down 38% year-over-year) currently just below their lowest point during the 2000s housing crash. On one hand, if financial conditions ease and mortgage rates fall in 2023, homebuyer demand would increase. On the other hand, the pandemics housing demand boom couldve had a pull-forward effect that results in a slower than expected post-pandemic housing market.

On the housing supply front, things remain fairly tight nationally. While spiked mortgage rates corresponded with a huge decline in demand, it hasnt caused sellers to rush for the exits. In fact, new listings on Realtor.com are down 17.25% on a year-over-year basis. Many buyers who would normally be looking to move up to a bigger house have postponed the switch because they dont want to give up their fixed 2% or 3% mortgage rates they have for their current house.

So do buyers (for whom low demand is a potential tailwind) or sellers (for whom tight supply is a potential tailwind) have the upper hand? One of the best indications might be the direction of inventoryand its speed of change. At first glance, it might be easy to assume that inventory (i.e. active listings for sale) is simply a measurement of supply, however, its also a measurement of demand. If homebuyers pull back, and homes sit on the market longer, that can increase inventory levels (currently up 46.8% year-over-year) even if new listings (currently down 17.3% year-over-year) decline.

Lets take a closer look at inventory data in the nations 400 largest markets.

Shortly after mortgage rates spiked this spring, the overheated U.S. housing market cooled. That swift pullback in buyer demand finally gave inventory breathing room to rise.

While national inventory levels on Realtor.com are up 46.8% year-over-year, the picture varies significantly by market. Cities like Austin and Phoenix have seen their respective inventory levels soar 160.7% and 176%. Meanwhile, markets like Chicago and New York City remain essentially unchanged.

When it comes to inventory, the speed of change matters. A sudden inventory spike often marks a housing market that has moved into a full-blown correction. Of course, we now know that's exactly what happened this summer in markets like Austin and Phoenix, where home values are already down 10.4% and 8.1% from their respective 2022 peaks.

Why are inventory levels spiking in some markets and flat in others? Well, for starters, fundamentals.

Every quarter, Moodys Analytics assesses whether local fundamentals, including local income levels, can support local house prices. If a regional housing market is overvalued by more than 25%, Moodys Analytics deems it "significantly overvalued." The Pandemic Housing Boom saw the "significantly overvalued" camp skyrocketed from 3 markets in the second quarter of 2019 to 210 markets by the second quarter of 2022. These frothy markets include places like Boise ("overvalued" by 74%) and Austin ("overvalued" by 61%).

Fast-forward to today, and those "significantly overvalued" markets, on aggregate, are shifting faster. The influx of high-earning remote workers saw home prices in boomtowns, like Boise and Idaho Falls, detach from local incomes. Of course, that becomes a problem when both remote worker migration slows and a 1981-level mortgage rate shock causes many local would-be borrowerswho must meet lenders' strict debt-to-income ratiosto lose their mortgage eligibility. Cue falling home prices.

In total, there were 751,544 active listings on Realtor.com in November 2022. That's up from 511,899 listings in November 2021 and 683,606 active listings in November 2020. However, the number still remains far below the pre-pandemic active listing count of 1.14 million in November 2019.

Some firms, like CoreLogic and Home.LLC, doubt that U.S. home prices will fall in 2023 with inventory being this tight. Researchers at Morgan Stanley say those housing bulls should reconsider their stance.

"The fact that we expect home prices to start falling on an annual basis in March 2023 despite tight inventory reflects how unprecedented this affordability situation is in the U.S. housing market," writes Morgan Stanley researchers who expect U.S. home prices to decline by around 10% from peak-to-trough even though supply remains below 6 months of inventory.

How can home prices fall even if inventory levels remain below pre-pandemic levels?

"When demand abruptly falls off a cliff, the absolute level of supply isn't as relevant. This is where watching the rate of change on both supply and demand separately is critical," Rick Palacios Jr., director of research at John Burns Real Estate Consulting, tells Fortune. "Investors accounted for the highest percentage of buyers ever this [housing] cycle in many markets. The lion's share of those buyers are now on the sidelines, with some needing to sell given overleveraged and really were just taking a flyer on home price appreciation continuing to rip higher. Those days are now over, and these sellers don't exhibit [the] same emotional/behavioral qualities associated with traditional owner-occupiers, which historically keeps home prices somewhat sticky on the downside. Builders also account for roughly twice their historical market share norm when it comes to for sale housing supply in the system (denominator there is resale supply plus new home supply under construction and finished inventory). Builders meet the market on price whereas traditional owners aren't as quick to drop prices."

Heading forward, John Burns Real Estate Consulting expects inventory to rise further next spring. In terms of national home prices, the research firm expects a 20% to 22% peak-to-trough decline if affordability remains hampered by 6% mortgage rates next year.

"It's very likely we see supply rise come spring, which is typical. New home supply in particular should rise, as we know finished homes (completions) are now increasing and builders have a lot more unsold homes still under construction working through the system," Palacios tells Fortune. "This will be the first spring selling season since 2008 where mortgage rates are ~6%, so we're expecting a bumpy ride in general for sellers, especially if the economy is officially in a recession."

Whenever a group like Morgan Stanley or John Burns Real Estate Consulting says U.S. home prices, they're talking about a national aggregate. Whatever comes next will surely vary by market.

Among the country's 400 largest housing markets, 36 markets are back to pre-pandemic housing levels. The searchable chart (in alphabetical order) above displays those 36 markets.

In theory, higher inventory levels could depress home prices in those markets.

Among the country's 400 largest housing markets, 364 markets are back to pre-pandemic inventory levels. The searchable chart (in alphabetical order) above displays those 364 markets.

While Morgan Stanley researchers don't think tight inventory will prevent home prices declines, they do believe tight inventory levels will prevent a 2008-style crash.

"Although supply doesn't keep home price growth floored at zero, we do believe it prevents home price declines from becoming too large" writes Morgan Stanley researchers.

Want to stay updated on the housing downturn? Follow me on Twitter at @NewsLambert.

Our new weekly Impact Report newsletter examines how ESG news and trends are shaping the roles and responsibilities of todays executives. Subscribe here.

Its a bold choice considering inflation reached a four-decade high of 9.1% in June, but most economists now believe consumer price increases have since peaked.

In the months since inflation topped 9%, gas prices have dropped nearly 40%, the housing market has tumbled, and prices for goods overall have begun to cool. 

As a result, year-over-year inflation fell to 7.1% last month, and although thats still well above the Federal Reserves 2% target rate, Jason Draho, head of asset allocation for the Americas at UBS Global Wealth Management, believes that inflation is likely to continue falling rapidly in the first part of 2023.

Disinflation is already happening, he wrote in a Dec. 16 research note. That there will be disinflation is not really in dispute at this point, the debate is over how much disinflation there will actually be.

The major argument in economics circles next year will be whether inflation will fall back to the Fed 2% target without more interest rate hikes than are currently expected, or if inflation will hit a floor at around 4%, Draho said.

But he made it clear that choosing disinflation as his word of the year, or WOTY, for 2023 isnt a forecast.

Predicting that disinflation will be the most dominant market story next year is not a prediction for significant disinflation, with a return to 2% inflation by year-end, he wrote. But just as the Fed tightening financial conditions is what drove performance across financial markets this year, potential disinflation could do the same in 2023. If thats not a recipe for a finance WOTY, then nothing is.

Draho went on to lay out a few key reasons for selecting disinflation as his word of the year. 

First, he said that the amount of disinflation in the U.S. economy will be critical for Federal Reserve policy. The Fed has raised interest rates seven times this year in an attempt to cool the economy and tame inflation. 

The more rampant and structural disinflation is, the sooner the Fed can stop hiking rates and start cutting, Draho wrote.

Second, he argued that the Fed has become the most important variable for investors over the past year. Rising interest rates have made risky investments less attractive, leading to major price declines in once-high flying tech stocks and cryptocurrencies, but that could shift in 2023.

[A] Fed that goes from investor foe to friend is the inflection point to watch next year, he wrote.

Third, Draho described how the amount of disinflation next year will determine whether the U.S. economy will be able to achieve a soft landingwhere inflation is tamed without a recession.

And finally, while the other obvious choice for word of the year in 2023 might be recession-relatedlike soft landing or hard landingDraho said the debate over what a recession might look like is really about semantics.

Both could entail negative earnings growth, he said. Whereas there is a big macro difference between a lot versus a little disinflation.

Our new weekly Impact Report newsletter examines how ESG news and trends are shaping the roles and responsibilities of todays executives. Subscribe here.