Nine has criticised the federal government’s decision to provide key concessions to the tech giants in its news media bargaining code, saying they further entrench the platforms’ monopoly power.
The legislation for the code will use final-offer arbitration as a last resort. However, in a win for Facebook and Google, the code will require the arbiters to consider the value tech giants provide news companies through traffic.
Facebook and Google have previously argued that they provide millions of dollars in indirect value for news publishers in Australia.
Nine says while it’s “grateful” for the code process, the two-way value exchange concession only entrenched their monopoly.
“While we are grateful for the ACCC Code process, the continued concessions to the digital platforms only entrenches both their monopoly power and the significantly unfair imbalance in regulation,” a Nine spokesperson says.
“These companies pay little or no tax, contribute little and often negatively to our culture, and employ no creative teams."
Google Australia reported total income tax expenses of $99.7 million last year and brought in revenue of $1.2 billion, according to financial statements lodged with corporate regulator ASIC.
Meanwhile, Facebook’s income tax expenses was $16,770,378 last year, representing around 2.4% of gross advertising sales. Facebook also recently boosted its Facebook Creative Shop team, headed up by Steve Coll.
“The notion they receive regulatory recognition with the so called two-way value exchange, for something they already have a commercial model to monetise, seriously undermines the fundamental problem the ACCC identified in the beginning of this process - that is an abuse of monopoly power which fundamentally harms the future sustainability of media in Australia."
Another key concession to the tech giants is that the period of notice they are required to give to publishers about algorithm changes has been reduced from 28 to 14 days. It has also been restricted to “conscience” algorithm changes that will have significant impact on ranking, rather than the “machine-learning progress” of algorithm changes that happen continuously.
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