Artificial Intelligence Company’s Economic Indicators

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The cost of production often goes up with a slight rise in inflation index. In other words, capital expenditure is negatively affected when inflation is high. In the case of starting and running an Artificial Intelligence Company, it is crucial to mention that capital expenditure is of great importance (Ogawa and Kosugi 33). A company of this nature definitely requires huge capital investment when acquiring vital goods and services.

A controlled inflation implies that an artificial intelligence company can greatly benefit in an economic environment where inflation is under check. Nevertheless, economic pundits argue that it is a challenging beast for governments to keep inflation in check since myriads of factors (both internal and external) have an upper hand in the rate of inflation index. If inflation rate goes beyond 2 percent, an artificial intelligence business may experience reduction in profit margin (Pfeifer 137).

On the other hand, it is vital to mention that if an artificial intelligence company has come of age and it is generally at the level of a large corporation, it can swiftly maneuver the challenges posed by inflation. The latter can be explained by the fact a well established business organization is at an economically-vantage position to enjoy economies of scale. At the startup stage of a company, economies of scale are less likely to be met.

When the rate of inflation goes overboard, it may lead to export slump as a result of impacts posed by currency exchange rates. Since an artificial intelligence company aims to make a given profit margin at the end of a fiscal year, a slump in export will definitely compel the company to increase its price for products being offered in the market (Ajmera and Clinton 1). If the artificial intelligence company opts to operate globally, its goods and services may be rendered less competitive.

If the input prices fall considerably, the pressure posed on the balance sheet of an artificial intelligence company will also be eased down proportionately. Input prices account for the major cost burden of such a company. For example, research and development alongside innovation and invention require significant capital input. In addition, if interest rates on loans are reduced with the passage of repayment period, there is no doubt that an artificial intelligence company will be in a position to make major advances (Armenter 309).

Nonetheless, the cost base of an artificial intelligence firm may go up due to wage increases. As much as the company can cut costs by adopting the minimum wage guidelines, the need to attract the best skills and professional acumen surpass the ideology of sticking with the minimum wage. Hiring a team of experts to work on the firm will demand excellent pay which definitely pushes up the cost of operation.

Most economies are fast-tracked by small to medium-sized business entities because on average, they employ the highest number of workers. Wages and related benefits usually comprise the largest operation cost in any type of business establishment (Andrieş, Ihnatov, and Tiwari 561). If employees are to be remunerated above the minimum wage guidelines, then the profit margin of an artificial intelligence company is likely to go down. In the event that the overheads accrued as wages are unmanageable, then a company may be compelled to reduce employee hours, initiate hiring freezes or generally downsize the workforce.

However, the management team of an artificial intelligence company should weigh in all the available options before making the final decision. Regardless of the decision made, wages and allowances should be considered before starting an artificial intelligence firm. Expansion and inventory are also jeopardized economically with the rise in minimum wage.

If a client opts to borrow venture capital from a financial institution, there are also pros and cons that accompany such a decision. For instance, most banks rarely offer good deal to both young and established businesses. For instance, if interest rates on credit facilities (loans) are not offered to businesses on reducing balance, the overall cost of repayment may be too high.

The state of labor market is yet another substantial factor that can either impede or enhance the growth of an artificial intelligence firm. In strict economic terms, labor is treated as a commodity. In other words, market forces directly affect the cost of labor at any given time in the operation of a firm. Higher wages are commanded by workers during economic booms. As a result, the overall cost of labor tends to go up (Yunjong 214). On the other hand, the price of labor declines in economic downturns. Hence, hard economic times lower the cost of labor while affecting the profit margin downwards simultaneously.

In recap, it can be concluded that a number of economic indicators affect the establishment of an artificial intelligence company. While some of the effects can be mitigated by the management teams, others such as inflation and exchange rates are beyond local control.

Works Cited

Ajmera, Richa, and Angie Clinton. “Current Employment Statistics Data and Their Contributions as Key Economic Indicators.”Monthly Labor Review (2016): 1. Print.

Andrieş, Alin Marius, Iulian Ihnatov, and Aviral Kumar Tiwari. “Comovement Of Exchange Rates: A Wavelet Analysis.”Emerging Markets Finance & Trade 52.3 (2016): 574-588. Print.

Armenter, Roc. “Sustainable Monetary Policy and Inflation Expectations.” B.E. Journal of Macroeconomics 16.2 (2016): 301-334. Print.

Ogawa, Takehiko1, and Yukio1 Kosugi. “A Neural Network For Identification Of Economic Indicators Using An Answer-In-Weights Scheme.” Systems & Computers in Japan 29.5 (1998): 29-36. Print.

Pfeifer, Christian. “A Note on Dual Internal Labor Markets and Wages of Temporary Workers: Evidence from Linked-Employer-Employee Data.” Journal of Labor Research 35.2 (2014): 133-142. Print.

Yunjong, Eo. “Structural Changes in Inflation Dynamics: Multiple Breaks At Different Dates For Different Parameters.” Studies in Nonlinear Dynamics & Econometrics 20.3 (2016): 211-231. Print.

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